Advances in Cancer Research, Volume 108

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Advances in

CANCER RESEARCH

Volume 108

This page intentionally left blank

Advances in

CANCER RESEARCH

Volume 108 Edited by

George F. Vande Woude Van Andel Research Institute Grand Rapids Michigan, USA

George Klein Microbiology and Tumor Biology Center Karolinska Institute Stockholm, Sweden

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London, NW1 7BY, UK Linacre House, Jordan Hill, Oxford OX2 8DP, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands

First edition 2010 Copyright # 2010 Elsevier Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the Publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier website at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material. Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. ISBN: 978-0-12-380888-2 ISSN: 0065-230X

For information on all Academic Press publications visit our website at www.elsevierdirect.com

Printed and bound in USA 10 11 12 10 9 8 7

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Contents

Contributors to Volume 108

vii

Insights into the Evolution of Lymphomas Induced by Epstein–Barr Virus David Vereide and Bill Sugden I. II. III. IV.

EBV and its Extensive Presence in Lymphomas 2 Tumor Cells Differ in Their Dependence on EBV 6 A Model for EBV-Induced Lymphomagenesis 9 Conclusion 14 References 15

Recent Advances in the Research of Hepatitis B Virus-Related Hepatocellular Carcinoma: Epidemiologic and Molecular Biological Aspects Jia-Horng Kao, Pei-Jer Chen, and Ding-Shinn Chen I. II. III. IV. V. VI. VII. VIII.

Introduction 22 Factors Associated with HCC Development in Patients with Chronic HBV Infection 26 Viral Factors in HBV-Related HCC 27 Nonviral Factors in HBV-Related HCC 44 Primary Prevention of HBV-Related HCC 45 Molecular Carcinogenesis of HBV-Related HCC 46 Genetic Variations and HCC: Virus and Host Perspectives Conclusions 60 References 61

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Contents

The ATM–Chk2 and ATR–Chk1 Pathways in DNA Damage Signaling and Cancer Joanne Smith, Lye Mun Tho, Naihan Xu, and David A. Gillespie I. II. III. IV. V. VI. VII. VIII. IX.

Introduction 74 Activation of the ATM–Chk2 and ATR–Chk1 DNA Pathways 75 Checkpoint Functions of the ATM–Chk2 and ATR–Chk1 Pathways 79 The Three Rs of Damage Signaling: Resection, Recombination, and Repair 84 ATM–Chk2 and ATR–Chk1 Pathway Alterations in Cancer 87 Exploiting Homologous Recombinational Repair (HRR) Defects for Cancer Therapy 92 DNA Damage Signaling as a Barrier to Tumorigenesis 95 Checkpoint Suppression as a Therapeutic Principle 97 Future Perspectives 102 References 104

microRNAs in Cancer: From Bench to Bedside Maria Angelica Cortez, Cristina Ivan, Peng Zhou, Xue Wu, Mircea Ivan, and George Adrian Calin I. II. III. IV. V. VI.

Introduction 114 Alterations of miRNA Expression in Cancer 115 Causes of miRNA Expression Variations 117 Pathways Involving miRNA Alterations 126 Clinical Applications 136 Concluding Remarks 143 References 144

Index

159

Contributors

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

George Adrian Calin, Department of Experimental Therapeutics and The RNA Interference and non-codingRNA Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA (113) Ding-Shinn Chen, Department of Internal Medicine, Graduate Institute of Clinical Medicine, Hepatitis Research Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan (21) Pei-Jer Chen, Department of Internal Medicine, Graduate Institute of Clinical Medicine, Hepatitis Research Center and Department of Medical Research, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan (21) Maria Angelica Cortez, Department of Experimental Therapeutics and The RNA Interference and non-codingRNA Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA (113) David A. Gillespie, Beatson Institute for Cancer Research, Garscube Estate and Faculty of Medicine, University of Glasgow, Glasgow, UK (73) Cristina Ivan, Department of Experimental Therapeutics and The RNA Interference and non-codingRNA Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA (113) Mircea Ivan, Department of Medicine, Microbiology and Immunology, Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, Indiana, USA (113) Jia-Horng Kao, Department of Internal Medicine, Graduate Institute of Clinical Medicine, Hepatitis Research Center and Department of Medical Research, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan (21) Joanne Smith, Beatson Institute for Cancer Research, Garscube Estate, Glasgow, UK (73) Bill Sugden, McArdle Laboratory for Cancer Research, University of Wisconsin–Madison, Madison, Wisconsin, USA (1) Lye Mun Tho, Beatson Institute for Cancer Research, Garscube Estate and Faculty of Medicine, University of Glasgow, Glasgow, UK (73) vii

viii

Contributors

David Vereide, McArdle Laboratory for Cancer Research, University of Wisconsin–Madison, Madison, Wisconsin, USA (1) Xue Wu, Department of Medicine, Microbiology and Immunology, Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, Indiana, USA (113) Naihan Xu, Beatson Institute for Cancer Research Garscube Estate, Glasgow, UK (73) Peng Zhou, Department of Biological Science, Purdue University Calumet, Hammond, Indiana, USA (113)

Insights into the Evolution of Lymphomas Induced by Epstein–Barr Virus David Vereide and Bill Sugden McArdle Laboratory for Cancer Research, University of Wisconsin–Madison, Madison, Wisconsin, USA

I. EBV and its Extensive Presence in Lymphomas A. EBV Is Retained in Cells Only if It Provides Them a Selective Advantage B. Specific Examples of EBV-Positive Malignancies II. Tumor Cells Differ in Their Dependence on EBV III. A Model for EBV-Induced Lymphomagenesis A. Implications of the Model B. Predictions from the Model IV. Conclusion References Epstein–Barr virus (EBV) encodes a wealth of oncogenic instructions, including the abilities to drive a resting normal B cell to proliferate and to override apoptotic stimuli. EBV is found in almost all types of lymphomas at varying frequencies. However, the particular viral genes expressed differ considerably among tumors. We have examined the role of EBV in several lymphomas by conditionally evicting the extrachromosomal viral genome from tumor cells in vitro and have found a graded dependence on the virus. Tumor cells that express all the known latent viral genes have been found to depend on the virus to drive proliferation and to block apoptosis at least in part by repressing the proapoptotic protein Bim. Other tumor cells, which express fewer viral genes, also depend on the virus to block apoptosis, but rely on the virus to promote but not to drive proliferation. Lastly, tumor cells with the fewest viral genes expressed have been found to require EBV to prevent the inefficient induction of a Bim-independent apoptosis. We present a model for the evolution of EBV-induced lymphomas in which tumors are initially “addicted” to the virus for almost all oncogenic functions. These tumors are targets for the immune system because they express multiple immunogenic viral proteins. Therefore, EBVinduced tumors are under selective pressure to acquire cellular mutations that can replace viral functions. We posit that the heterogeneity in viral gene expression among different EBV-associated lymphomas reflects a dynamic process by which tumors evolve to be less dependent on the virus. # 2010 Elsevier Inc.

Advances in CANCER RESEARCH Copyright 2010, Elsevier Inc. All rights reserved.

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0065-230X/10 $35.00 DOI: 10.1016/S0065-230X(10)08004-8

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David Vereide and Bill Sugden

I. EBV AND ITS EXTENSIVE PRESENCE IN LYMPHOMAS Lymphoid neoplasms represent a diverse set of tumors that arise from B, T, or natural killer cells. The 2008 WHO classification recognizes at least 60 different types, which are broadly categorized as mature B cell neoplasms, mature T-cell and NK-cell neoplasms, Hodgkin’s lymphomas (HL), or posttransplant lymphoproliferative disorders (PTLDs) (Jaffe et al., 2008). These tumors are identified and classified at presentation by a collection of variables including cell morphology, immunohistochemical markings, genetic markings, and involvement at specific anatomical sites (Jaffe et al., 2008). Remarkably, Epstein–Barr virus (EBV) is found in all four categories of lymphoid neoplasms, ranging in the frequency of its presence from being in virtually all endemic Burkitt’s lymphomas (BLs), to being in approximately 9% of diffuse large B cell lymphomas (Kelly and Rickinson, 2007; Park et al., 2007). EBV is a gammaherpes virus that preferentially infects B lymphocytes. Primary infection of the virus occurs by oral transmission and, while usually asymptomatic, can present as infectious mononucleosis (Henle et al., 1968). When EBV infects a primary resting B cell, it drives the cell out of quiescence. These proliferating cells (known as transformed cells) are also prevented from dying by apoptosis (Altmann and Hammerschmidt, 2005; Altmann et al., 2006). Transformed cells express multiple viral genes from the extrachromosomal viral genome (a DNA plasmid) including the protein BamHI fragment H rightward open reading frame 1 [BHRF1], six nuclear proteins (the Epstein–Barr virus nuclear antigens [EBNAs]), three membrane-associated proteins (the latent membrane proteins [LMPs]), two noncoding, nonpolyadenylated small RNAs (the EBERs), and greater than 25 miRNAs (Fig. 1; Cai et al., 2006; Kelly et al., 2009; Young and Rickinson, 2004; Zhu et al., 2009). These cells are considered latently infected because they do not produce progeny virus. While EBV can readily infect B lymphocytes in vitro from different stages of B cell development, it appears that in vivo the virus resides primarily in memory B cells (Babcock et al., 1998; Hochberg et al., 2004; Siemer et al., 2008). There is little if any viral gene expression in these cells, which are therefore likely quiescent (Babcock et al., 2000; Miyashita et al., 1997; Tierney et al., 1994). The differentiation of an infected memory B cell to a plasma cell upon antigen binding is hypothesized to trigger the lytic infection, during which a great many more viral genes are expressed (EBV encodes over 100 genes) that allow the virus to use the cell to produce progeny virus (Laichalk and Thorley-Lawson, 2005). EBV is a remarkably successful virus: not only does it persist for the lifetime of its host but it also infects greater than 90% of the human population. It is not,

3

The Evolution of EBV-Induced Lymphomas LMP2A

LMP2B

Cp Wp

LMP1 BART Locus (miRNAs) EBERs oriP

EBV 165 kbp

EBNA-LP EBNA2 EBNA1

Qp

BHRF1 protein and miRNAs

EBNA1 EBNA3C EBNA3B

EBNA3A

Fig. 1 A map of the genome of Epstein–Barr virus (EBV); the genome exists as a plasmid of approximately 165 kilobase pairs of double-stranded DNA. Several DNA elements are shown including the latent origin of replication, OriP, and the promoters Cp, Wp, and Qp. Dotted lines represent transcripts. The approximate locations of the coding sequences for EBV’s latent viral genes are depicted.

however, unrestrained. EBV is well controlled by a competent immune system (Hislop et al., 2002). In rare cases, particularly those in which the host’s immune system is compromised clinically (to allow for graft acceptance, for example) or pathogenically (such as by infection with malaria or HIV), the virus is causally associated with the development of tumors. These tumors include not only lymphomas but also both gastric and nasopharyngeal carcinomas (NPCs). Viral gene expression in different tumors is extremely heterogeneous (Table I). Indeed, the particular set of viral genes expressed varies not only between classes of tumors but sometimes among a class, from case to case. Surprisingly, the viral genes shown to be necessary for EBV to transform cells in vitro are often not expressed within the tumor or, as occurs in Wp-restricted BL, can be deleted from the viral genome all together (Kelly et al., 2002; Young and Rickinson, 2004).

Table I Viral Gene Expression Varies Across and Among Classes of EBV-Associated Lymphomas Viral genes

Cell type

Normal, transformedg Burkitt'sh Diffuse large B celli Hodgkin'sj Primary effusionk PTLDl Wp-restricted Burkitt'sm

EBNA1 + + + +/− + + +

BART EBERsa miRNAsb,c + + + + + + + +

BHRF1 miRNAsd +

+ + +/− + + +/−

+/− + + − + +

LMP1 + +/− +/− + +/− +/− −

LMP2A + +/− + +/− + +/− +/−

LMP2B + − − +/− NDf − NDf

EBNA2, 3s, LPe + +/− +/− − − +/− +/−

Poorly or non-immunogenic Immunogenic + always detected, +/− variably detected, − not detected a

often just one of the two co-expressed EBERs was assayed

b

in some cases, the BART transcript instead of the BART miRNAs was assayed

c

often one or several representative miRNAs only were assayed as these genes are co-expressed from a single locus

d

often one representative miRNA only was assayed as these genes are co-expressed from a single locus

e

often one representative EBNA only was assayed as these genes are expressed from a polycystronic message

f

not done

g

Young and Rickinson, 2004; Cai et al., 2006

h

Brooks et al., 1993; Niedobitek et al., 1995; Tao et al.,1998; Pratt et al., 2009

i

Shibata et al., 1993; Kuze et al., 2000; Xia et al., 2008

j

Pallesen et al., 1991; Deacon et al., 1993; Pfeffer et al., 2004

k

Horenstein et al., 1997; Xia et al., 2008

l

Thomas et al., 1990; Cen et al., 1993; Tao et al., 1998; Capello et al., 2003; Timms et al., 2003; Pratt et al., 2009

m

Kelly et al., 2002; Kennedy et al., 2003; Bell et al., 2006; Pratt et al., 2009

The Evolution of EBV-Induced Lymphomas

5

A. EBV Is Retained in Cells Only if It Provides Them a Selective Advantage And yet, regardless of the particular genes that are expressed in a tumor, EBV must provide selective advantage(s) to all the tumors with which it is present in the bulk of the cells. This is known from recent studies of the replication of EBV plasmids (Nanbo et al., 2007). An intrinsic property of the synthesis of the viral plasmid genome is that not all viral DNAs are synthesized each cell cycle. This inefficiency has a consequence for a population of proliferating cells: EBV will be lost at a rate of 8% per cell per generation if it does not provide any selective advantage to its host cell. A population of cells will maintain EBV only if those cells that do retain it thereby possess the ability to outgrow those that lose it. EBV-positive tumors can be shown to retain EBV in the majority of the cells extrachromosomally and thus we can conclude that these tumors make use of the tumor virus to sustain themselves (Brousset et al., 1992; Huang et al., 1974; Kaschka-Dierich et al., 1976; Lindahl et al., 1976; Raab-Traub and Flynn, 1986; Reedman et al., 1974; Roth et al., 1994). However, what those selective advantages are and how they differ among tumors remains largely unknown.

B. Specific Examples of EBV-Positive Malignancies Two types of lymphoma, PTLD and endemic BL, illustrate well the diversity in viral gene expression among the large set of EBV-associated tumors. PTLD is a heterogeneous disease characterized by the outgrowth of lymphoblasts in immunosuppressed patients following organ transplantation. When PTLD arises rapidly (a median of less than 1 year following transplantation), cells responsible for the disease are commonly infected with EBV and are thought to express all the known latent viral genes (Timms et al., 2003), and thus resemble EBV-infected normal cells (transformed cells) in vitro. In contrast, late-onset PTLD (arising after 1 year posttransplantation) can express fewer viral genes (Timms et al., 2003). Endemic BLs, which are almost always EBV-positive also express few viral genes (Tao et al., 1998). These tumors arise in young African children, after repeated bouts of malaria. Malaria serves as an immunosuppressor, allowing the expansion of EBVinfected cells (Lam et al., 1991). The majority of endemic BLs (which will be referred to here as canonical BL) express the viral proteins EBNA1 and sometimes LMP2A and the viral noncoding RNAs only; they do not consistently express all the latent proteins found in early-onset PTLD (Niedobitek et al., 1995; Tao et al., 1998). BLs have an invariant cellular mutation in which the myc locus is translocated to any one of the three immunoglobulin

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David Vereide and Bill Sugden

loci, and are often found with mutations in p53 (Lindstrom and Wiman, 2002). Because these tumor cells express few viral genes and possess consistent cellular mutations, they clearly differ from transformed cells infected with EBV in vitro or early-onset PTLD cells. Wp-BLs (Wp-BL), approximately 15% of endemic BL, activate the viral Wp promoter to express an additional five viral proteins compared to canonical BL (Kelly et al., 2002). These five proteins are also found in early-onset PTLD, making Wp-BL an intermediate (with respect to viral gene expression) between tumors with full viral latent gene expression (early-onset PTLD) and ones with minimal viral gene expression (canonical BL). Thus, BLs and PTLDs together form a set of lymphomas that express overlapping but nonidentical sets of viral genes and arise under different environmental conditions.

II. TUMOR CELLS DIFFER IN THEIR DEPENDENCE ON EBV We have examined the role of EBV in tumor cell lines derived from both BL and PTLD tumors (manuscript in review). Because EBV exists in these cells as an extrachromosomal plasmid, the role the virus plays can be identified by disrupting viral replication and thus forcing the loss of viral plasmids from tumor cells. Two viral DNA elements and one viral protein mediate the replication of EBV; all else is provided by the cell. The origin of plasmid replication, OriP, consists of two clusters of DNA binding sites for the viral protein EBNA1 (Lindner and Sugden, 2007). One cluster of four sites, termed DS, serves as a licensed origin of DNA synthesis (Chaudhuri et al., 2001; Dhar et al., 2001; Schepers et al., 2001). The second cluster of approximately 20 sites, termed FR, mediates the maintenance of plasmids in proliferating cells (Krysan et al., 1989; Middleton and Sugden, 1994; Reisman et al., 1985). EBNA1 on binding DS recruits the cellular origin recognition complex, ORC, required for the initiation of DNA synthesis (Chaudhuri et al., 2001; Schepers et al., 2001). EBNA1 on binding FR likely tethers EBV plasmids to AT-rich sites in human chromosomes via EBNA1’s AT-hook domains (Sears et al., 2004) to allow the newly synthesized plasmids to partition equally to daughter cells 88% of the time during mitosis (Nanbo et al., 2007). Clearly, EBNA1 is critical for the maintenance of EBV in proliferating cells. We exploited this critical dependence of the virus on EBNA1 by devising a system for the conditional expression of a dominant negative derivative of EBNA1 (dnEBNA1) which lacks the ability to maintain EBV (Kirchmaier and Sugden, 1997): the expression of dnEBNA1 evicts the virus from cells. Populations of cells in which the virus is present extrachromosomally (both tumor and normal cells) have a broad range in numbers of viral

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The Evolution of EBV-Induced Lymphomas

plasmids per cell. A consequence of this distribution of genomes is that cells with few copies are the first to lose EBV in the presence of dnEBNA1 while cells with high numbers will be able to undergo multiple divisions before losing the virus. In this way, the loss of the virus from cell populations is inherently asynchronous and any consequences that result from the complete loss of the virus are also asynchronous. EBV was conditionally evicted from the PTLD cell line PTLD1 (which appears to express the full set of viral latent genes), the Wp-BL cell line OkuBL, and two canonical BL cell lines Sav-BL and Dante-BL (Table II; Kelly et al., 2002; Pratt et al., 2009). The loss of EBV was associated with defects in growth of all tumor cell types but the defects differed substantially among them. PTLD1 and Oku-BL cells exhibited a strong inhibition of growth which correlated with the induction of apoptosis. Canonical BL cells (the cell lines Sav-BL and Dante-BL) also exhibited the inhibition of growth upon the forcible loss of EBV, but the inhibition was not as pronounced as for the other tumor cell lines as shown by their accumulation of viable EBV-negative cells. Furthermore, there was significant clonal variation for a given canonical BL, with several clones exhibiting slight or undetectable changes in growth rates. The induction of apoptosis could be observed in some canonical BL cells, but the extent of this death varied from clone to clone, too. Collectively, these observations distinguished PTLD1 and Oku-BL cells from canonical BL cells phenotypically: PTLD and Oku-BL cells rely more on EBV to block apoptosis than do canonical BL cells. This distinction in dependence on EBV between tumor cell lines was reflected in the proapoptotic changes that occurred within the different tumor cells. In both PTLD1 and Oku-BL cells, which have an acute Table II

Tumor cell line

Lymphoma

Plasmid copiesa

Viral proteinsb

Non-coding RNAsb

MicroRNAsb

PTLD1

PTLD

9

10

2

>25

Oku-BL

Wp-BL

13

6

2

>25

Dante-BL

BL

24

1

2

>22

Sav-BL

BL

30

1

2

>22

aaverage b

Tumor Cell Lines in Which EBV Was Evicted with dnEBNA1

number of plasmids per cell expressed in the tumor cell line

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David Vereide and Bill Sugden

apoptotic phenotype, the levels of the proapoptotic protein Bim increased as they lost EBV. In contrast, no consistent change in Bim levels was observed in canonical BL cells as they lost EBV. These observations are consistent with recent work demonstrating that the viral proteins EBNA3A and 3C cooperate to repress Bim’s expression because while EBNA3A and 3C are expressed in PTLD1 and Oku-BL cells, they are not expressed in canonical BL cells (Anderton et al., 2008). However, because some canonical BL cells do undergo apoptosis as they lose EBV, these observations also indicate that EBV blocks at least two apoptotic pathways, one that involves Bim (Bim-dependent) and other that does not (Bim-independent). They furthermore raise the possibility that canonical BL cells rely less on EBV to block apoptosis because they require the virus to block only the Bim-independent pathway while PTLD1 and OkuBL need EBV to block both the Bim-dependent and -independent pathways. PTLD1 and Oku-BL tumor cells shared the same apoptotic phenotype when EBV was evicted, but differed in their dependence on EBV to drive proliferation. We used the overexpression of the anti-apoptotic cellular oncogene Bcl-XL to substitute for EBV’s block to apoptosis; this substitution unmasked a role for EBV in effecting the proliferation of these tumor cells. Oku-BL cells were found to proliferate independently of EBV, albeit more slowly due to a delay in G1/G0. We termed the ability of EBV to render the cell’s exit from G1/G0 efficient as the “promotion” of proliferation. In contrast, PTLD1 cells quiesced in G1/G0 as they lost EBV. Thus, not only can EBV both promote and drive proliferation but these functions distinguish its roles in PTLD1 and Oku-BL cells: PTLD1 cells require EBV to drive proliferation, while Oku-BL cells require EBV to promote it. The identification of multiple functions EBV provides these lymphomas collectively reveals a correlation. PTLD1 tumor cells express the most viral genes among the tumor cells studied and rely most on EBV to block apoptosis and drive proliferation. Oku-BL cells express fewer viral genes than PTLD1 cells, and have a reduced reliance on EBV to block apoptosis and promote but not drive proliferation. Lastly, canonical BL cells express even fewer viral genes than Oku-BL cells and rely least on EBV to prevent the inefficient induction of apoptosis, and this occurs only in some cells. Therefore, the number of viral genes expressed in these lymphoma cell lines correlates with the extent of their dependency on the virus. There is also an inverse correlation between number of viral genes expressed in the different tumor cells and their known cellular mutations. For example, the PTLD line used in this study was confirmed to possess a normal karyotype. In contrast, both the Wp-BL and canonical BL cells contain translocations of the myc locus to an immunoglobulin locus, rendering the myc oncogene constitutively active. Furthermore, Wp-BL cells have a wild-type p53, while canonical BL cells often have mutations in p53 (Anderton et al., 2008). Thus, cells with more expressed viral genes possess fewer known mutations

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The Evolution of EBV-Induced Lymphomas

(of oncogenic potential) in cellular genes, and cells with the fewest expressed viral genes possess a greater collection of mutant cellular genes.

III. A MODEL FOR EBV-INDUCED LYMPHOMAGENESIS The correlation and the inverse correlation noted above together support a model for EBV-induced lymphomagenesis (Fig. 2). Additionally, we have borrowed liberally from some of the observations and interpretations of others to construct this model, in particular from the work of Rickinson, Klein, and their colleagues (Kelly et al., 2002, 2007; Timms et al., 2003). The presence of EBV provides a normal B cell an almost complete set of oncogenic instructions, including the ability to proliferate and the ability to Immune selective pressure

Viral oncogenes Post-transplant lymphoproliferative disorder (early onset) Wp-restricted Burkitt’s lymphoma Transformed cell

Canonical Burkitt’s lymphoma Primary effusion lymphoma Hodgkin’s lymphoma

Activated cellular oncogenes inactivated cellular tumor suppressors

Tumor evolution

Fig. 2 A model for EBV-induced lymphomagenesis. Epstein–Barr virus (EBV) provides a broad set of oncogenic instructions including the ability to proliferate and the ability to ignore apoptotic signals (“transforming” cells). However, EBV-infected cells are targeted by a competent immune system. Thus, EBV induces the formation of lymphomas which, in response to the selective pressure imposed by the immune system, evolve in a way that reduces dependence on the virus by acquiring compensating cellular mutations. Different EBV-positive lymphomas present clinically at different points in this evolutionary progression. Several representative lymphomas are depicted with their most common set of expressed viral genes (Table I). The lengths of the arrows for each tumor correlate with the duration of the tumors’ evolution from primitive (many viral genes expressed) to more complex states (fewer viral genes expressed).

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David Vereide and Bill Sugden

override apoptotic signals. However, these “proto” tumor cells are constantly eliminated by a fully competent immune system because they express foreign antigens. In a normal infection, EBV avoids immune detection by setting up a form of latency in some cells in which few if any viral proteins are expressed (Babcock et al., 2000; Miyashita et al., 1997; Tierney et al., 1994). These cells are quiescent because they do not express viral genes responsible for driving the proliferation of normal cells (LMP1 and EBNA2) (Dirmeier et al., 2005; Zimber-Strobl et al., 1996). Thus, for a “proto” tumor cell to survive and continue to proliferate, it must evolve in a way that reduces its dependence on those viral genes that can be detected by the immune system. It can gain this independence by acquiring mutations that replace the roles of the virus. An early-onset PTLD tumor without cytogenic abnormalities is “primitive” in that it has not acquired any (or few) evolutionary changes that would limit its dependence on the virus. Indeed, it has little selective pressure to do so: the host’s immune system is severely compromised in PTLD. Tumors such as Wp-BL can be viewed as being more complex than early-onset PTLD. These tumors depend on fewer viral genes and must have acquired more compensating cellular mutations. Tumors such as canonical BLs, which express the fewest viral genes, are even further advanced in complexity. Canonical Burkitt’s thus have lost most dependence on viral genes and are only a few steps away from dependence on cellular oncogenes alone. It is therefore not surprising that some canonical BLs spontaneously lose the virus in culture (Shimizu et al., 1994). Thus, EBV induces the formation of lymphomas which, in response to the selective pressure imposed by the immune system, evolve in a way to reduce dependence on the virus by acquiring compensating cellular mutations. The model does not assume that EBV provides a complete set of oncogenic instructions. Indeed, EBV-infected normal cells are not immediately immortalized and still go through crisis upon reaching the Hayflick limit, the point at which, as a result of shortened telomeres, cells become senescent (Shay and Wright, 2000). Even primitive EBV-positive tumors (such as early-onset PTLD) appear immortalized. For example, we have cultured PTLD1 cells in excess of 250 days (at least 175 doublings) without any signs of crisis. Thus, there are likely genetic or epigenetic changes—beyond those selected for by the immune system to replace viral genes—that contribute to tumor formation.

A. Implications of the Model The proposed model of EBV-induced lymphomagenesis explains the bewildering complexity of differential viral gene expression between and among tumors (Table I). The viral genes that are most conserved in their expression across all lymphomas are those that are either nonimmunogenic

The Evolution of EBV-Induced Lymphomas

11

(viral miRNAs or EBERs) or poorly so (EBNA1) (Blake et al., 1997; Levitskaya et al., 1997). The viral genes that are not conserved are the immunogenic proteins which are under selection by the immune system (Khanna et al., 1992; Murray et al., 1992). The case to case variation that occurs for some viral genes is likely to reflect the capacity of a host’s immune system to target and eliminate specific viral epitopes. For example, both diffuse large B cell lymphomas and PTLDs (particularly late-onset PTLDs) vary in their expression of viral proteins: tumors can lack LMP1 expression, EBNA2 expression, or both (Capello et al., 2003; Kuze et al., 2000; Timms et al., 2003). Indeed, the observation that late-onset PTLDs tend to have fewer viral genes expressed than early-onset PTLDs clearly supports the contention that over time selection favors those tumor cells with reduced viral gene expression. Some of the nonimmunogenic viral genes, such as the viral miRNAs, can be found to vary in expression among tumors (Pratt et al., 2009; Xia et al., 2008). This variation can be explained in part by co-selection. For example, the BHRF1 miRNAs, whose expression is not conserved across tumor types, are thought to be expressed from a transcript that also encodes immunogenic viral proteins (the EBNAs) (Cai et al., 2006). Selections against the viral proteins in this case would also select against the BHRF1 miRNAs. Therefore, the variation in viral gene expression in EBV-positive lymphomas can be explained, at least in part, as arising from selection against the immunogenic viral proteins. In the model presented for EBV-associated lymphomagenesis, the simplest evolutionary step would involve two events as a viral oncogenic function is replaced by a cellular one: a cellular oncogene is activated and the corresponding viral oncogene is inactivated. Cellular oncogenic activation occurs, of course, by mutation. The inactivation of viral oncogenes need not occur this way. To be sure, there are examples of viral gene inactivation by mutation (Wp-BLs inactivate EBNA2 by its deletion; Kelly et al., 2002), but what is more commonly observed is simply an alteration in viral promoters such that a different set of viral genes are expressed. For example, in EBVinfected normal B cells, the poorly immunogenic EBNA1 protein is expressed from a polycistronic transcript that encodes other, highly immunogenic EBNA proteins. This transcript originates from the Cp promoter (Fig. 1) (Altmann et al., 2006). However, in canonical BL cells only EBNA1 is expressed because transcription originates from the downstream Qp promoter, thereby preventing the coexpression of the other EBNAs (Tao et al., 1998). Thus, some evolutionary steps during EBV-induced lymphomagenesis are fostered by cis-acting viral regulation that likely normally serves other functions during the viral life cycle. However, an implication from the presented model is that the set of genes expressed in a tumor need not reflect a particular host–virus relationship. Tumors have frequently been found to express either the full battery of latent genes (referred to as type III latency) or two subsets of this set (referred to as

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type II and type I latency). Thus, it has been proposed that each of these profiles of expression reflects a particular program the virus follows in normal cells as part of its lifecycle. While there is in vivo evidence consistent with this notion (Babcock et al., 2000; Chen et al., 1995; Tierney et al., 1994), reports continue to emerge describing tumors that express different subsets of viral genes, blurring lines once thought to be well demarcated (Kelly et al., 2002, 2006) . Indeed, it seems rather that one of many combinations of latent viral genes may be expressed in a given tumor, with certain combinations predominating in certain lymphoma types. The sets of genes found expressed in tumors are likely to result from selection: tumor cells differ in their evolution from their normal, parental, infected cells, and the genes they express need not reflect so-called normal latency programs. An apparent contradiction to the presented model is the observation that some tumors such as NPC and perhaps HL, while overtly EBV-positive in vivo often lose the virus when explanted in culture (Cheung et al., 1999; Kis et al., 2003). Tumors that were once EBV-positive but lose the virus following propagation in culture appear to counter the proposition that EBV provides selective advantage(s) to them in vivo. However, we interpret these observations to indicate that the selective pressure to retain EBV in vivo is absent in cell culture. What constitutes this selective pressure is not clear. At least one of the genes found in HL or NPC tumors, LMP1, is clearly important for driving the proliferation of infected normal B cells (Dirmeier et al., 2003, 2005; Kaye et al., 1993). Why is LMP1 not needed to drive proliferation of these tumors in cell culture? Because the cells can proliferate independently of LMP1 in culture, they likely have the capacity to do so in vivo. Why then do they express LMP1 at all, given that the protein can elicit an immune response (Khanna et al., 1992)? Certainly, viral genes are multifunctional and it is unlikely that all of their functions are yet known. It may well be that viral genes such as LMP1 are expressed in tumors for reasons other than to drive proliferation, for example, to promote survival (Zimber-Strobl et al., 1996). This notion is supported by the observation that LMP1 is found to drive the proliferation of cells only in the presence of functional EBNA2 (Zimber-Strobl et al., 1996). HL or NPCs do not express EBNA2 (Brooks et al., 1992; Deacon et al., 1993; Pallesen et al., 1991), and thus may not need LMP1 for their proliferation. It has also been proposed that tumors such as NPC retain EBV in vivo but lose it in vitro because the virus is employed by the tumor to evade the immune system. Expression profiles of EBV-positive NPC biopsies show a correlation between the presence of the virus and downregulation of MHC class I genes (Sengupta et al., 2006). It is, however, unclear whether this is a direct result of the presence of virus or selection for cells that can harbor the virus and still evade immune detection. Clearly, the immunogenicity of viral proteins conflicts with the idea of viral suppression of immune recognition.

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It perhaps is simpler to hypothesize that tumor cells harboring EBV in vivo but not in vitro do so for reasons other than immune protection such as promoting survival in the tumor microenvironment (Sengupta et al., 2008). One additional implication of the model is particularly provocative. The model suggests that some (perhaps many) lymphomas that present clinically as EBV-negative were once EBV-positive, but evolved to be independent of the virus in vivo. This notion, while difficult to disprove, could be tested if a vaccine against EBV becomes available. Significant drops in the incidence of all lymphoma (both EBV-positive and EBV-negative) in vaccinated populations would be evidence that EBV is a bonafide “hit and run” tumor virus.

B. Predictions from the Model Several testable predictions grow from the proposed model of EBV-induced lymphomagenesis. Firstly, the accumulated cellular mutations in a particular tumor should functionally compensate for those viral genes it no longer expresses. For example, Wp-BLs do not express the proliferative viral genes LMP1 and EBNA2 but do express anti-apoptotic genes such as BHRF1, EBNA3A and EBNA3C. This model predicts that Wp-BLs have accumulated cellular mutations that drive proliferation, but not ones that protect cells from apoptosis (Kelly et al., 2009; Watanabe et al., 2010). In canonical BLs, which do not express LMP1, EBNA2, BHRF1, EBNA3A, or EBNA3C, the model predicts cellular mutations that not only drive proliferation but also block apoptosis. The set of genetic alterations that allow a cell to reduce its dependency on EBV is not likely to be small. For example, BLs perhaps rely on misregulated myc, rather than EBV, to drive proliferation. Work with transgenic mice that mimic the juxtaposition of the IgH E enhancer and the myc locus demonstrates that myc misregulation is oncogenic: these mice succumb to B cell lymphomas at an early age (Adams et al., 1985). We have found, however, that the ability of myc to replace EBV’s ability to drive proliferation is dependent on changes in addition to the activation of myc. Specifically, PTLD1 cells could not proliferate in the absence of EBV upon activation of mycER (a fusion of myc to the tamoxifen-responsive estrogen ligand-binding domain) and this failure was associated with the inability of mycER to regulate all of its target genes. This result with a PTLD cell line is consistent with findings in a normal B cell line dependent conditionally on EBNA2 and its downstream target, LMP1, for proliferation. Some but not all clones of these cells in which myc was abundantly expressed constitutively could eventually proliferate in the absence of functional EBNA2 if propagated long enough. The clonal variation present in these experiments indicates the requirement for additional cellular alterations beyond efficient myc expression to substitute for EBNA2 and LMP1 (Polack et al., 1996). These observations

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together indicate that those cells that do convert from EBV-driven proliferation to myc-driven proliferation likely require not only the activation of myc but also the upregulation or downregulation of cofactors that can affect myc’s global effect on cellular gene expression. Second, the model predicts tumors that arise with immunogenic viral genes only when the immune system is compromised. For example, PTLDs arise expressing many immunogenic viral genes in immunosuppressed hosts. Hodgkin’s patients are not known to be immunocompromised. The model predicts that patients with HL in which the LMPs are expressed but the EBNA2 and 3s are not would either have a CTL response to the EBNAs but not to the LMPs (which generally do not elicit as robust a response as many of the EBNAs; Khanna et al., 1992; Murray et al., 1992), or tolerate certain antigens, for example, by developing a niche in which the tumor is generally protected from CTLs (Di Stasi et al., 2009).

IV. CONCLUSION The presented model for EBV-induced lymphomagenesis, besides forming a framework by which EBV-associated malignancies can be understood, also has implications for understanding the evolution of tumors in general. An EBV-induced tumor is a system initially replete with viral oncogenes upon which the tumor depends. The tumor could be said to be “addicted” to the virus. However, when placed under negative selection for viral oncogenes by the immune system, these virally induced tumors “rehabilitate” by evolving alternative means to survive and grow. Thus, apparent oncogene addiction is misleading, because it can exist only in the absence of selective pressure against that oncogene. That the notion of oncogene addiction is misleading is consistent not only with studies of mouse models in which tumors evolve means of survival when deprived of the oncogene that induced them (Boxer et al., 2004; Ewald et al., 1996) but also with the failure of therapies in which single oncogenes are targeted (Hochhaus et al., 2002; Ring and Dowsett, 2004). Clearly, tumors rely on sets of oncogenic instructions to be maintained. However, the particular set of instructions a given tumor relies upon needs not be fixed. Thus, successful strategies to eliminate tumors will need to identify and target multiple oncogenic pathways operating in the tumor, creating a genetic bottleneck too narrow for the tumor’s evolutionary escape.

ACKNOWLEDGMENTS This work was funded by grants from the National Cancer Institute, National Institutes of Health (Grant P01 CA022443, Grants R01 CA133027 and R01 CA070723). D. V. was supported by a predoctoral fellowship from the National Cancer Center. B. S. is an American Cancer Society Research Professor.

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Recent Advances in the Research of Hepatitis B Virus-Related Hepatocellular Carcinoma: Epidemiologic and Molecular Biological Aspects Jia-Horng Kao,*,{ Pei-Jer Chen,*,{ and Ding-Shinn Chen* *Department of Internal Medicine, Graduate Institute of Clinical Medicine, Hepatitis Research Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan { Department of Medical Research, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan

I. Introduction II. Factors Associated with HCC Development in Patients with Chronic HBV Infection III. Viral Factors in HBV-Related HCC A. Viral Load B. Genotype C. Subgenotype D. Naturally Occurring Mutants E. Precore and CP Mutants F. Pre-S Deletion G. Potential Interactions Between Known HBV Factors H. Nomogram for Predicting HCC Risk I. Role of Occult HBV Infection in Hepatocarcinogenesis IV. Nonviral Factors in HBV-Related HCC V. Primary Prevention of HBV-Related HCC VI. Molecular Carcinogenesis of HBV-Related HCC A. Chronic Inflammation: A Critical Step Toward Hepatocarcinogenesis and the Role of Nuclear Factor-B B. Specific HBV Proteins Associated with Hepatocarcinogenesis VII. Genetic Variations and HCC: Virus and Host Perspectives A. Viral Genetic Variations B. Host Genetic Factors C. Candidate Genes with Somatic Mutations or with Aberrant Expression Patterns D. Genome-Wide Analysis of Genetic Aberrations and Gene Expression Patterns E. Deregulation of Cellular Pathways F. Single Nucleotide Polymorphism (SNP) Analysis G. Gender Disparity in HBV-Related HCC H. Identification of HCC Predisposition Gene(s) in Familial Multiplex HCC

Advances in CANCER RESEARCH Copyright 2010, Elsevier Inc. All rights reserved.

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0065-230X/10 $35.00 DOI: 10.1016/S0065-230X(10)08003-6

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22 VII. Conclusions References

Hepatocellular carcinoma (HCC) is one of the commonest cancers worldwide, and more than half of HCC patients are attributable to persistent hepatitis B virus (HBV) infections. The best and cheapest way to prevent HBV-related HCC is the implementation of universal hepatitis B vaccination program, by which the incidence rates of childhood HCC have been reduced in several countries, including Taiwan. However, there are still hundreds of millions of HBV carriers in the world that remain a global health challenge. In the past decade, several hepatitis B viral factors such as serum HBV DNA level, genotype, and naturally occurring mutants have already been identified to influence liver disease progression and HCC development in HBV carriers. Several easyto-use scoring systems based on clinical and viral characteristics are developed to predict HCC risk in HBV carriers and may facilitate the communication between practicing physicians and patients in clinical practice. In addition, the role of nonviral factors in HBV-related HCC has also been increasingly recognized. On the basis of these emerging data, it is recommended that HBV carriers should be screened and monitored to identify those who have a higher risk of liver disease progression and require antiviral treatments. Regarding the molecular carcinogenesis of HCC development, despite some progress in the research of cell biology of HCC in the past decade, aberrant pathways involved in maintaining HCC phenotypes have not been completely elucidated yet. In the future, through comprehensive and integrated approaches to analyze the genomes of human HCC, novel target genes or pathways critically involved in hepatocarcinogenesis may hopefully be identified. # 2010 Elsevier Inc.

I. INTRODUCTION Although safe and effective vaccines are available for more than two decades, hepatitis B virus (HBV) infection is still an important public health problem and the major cause of chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC) worldwide (Kao and Chen, 2002). The World Health Organization (WHO) has documented HBV to be second only to tobacco as a potent environmental carcinogen (Gomaa et al., 2008). Belonging to the family Hepadnaviridae, HBV is the smallest human DNAvirus with a genome of 3200 base pairs (Ganem and Varmus, 1987). The partially double-stranded circular DNA encodes four overlapping open reading frames, including surface (S), core (C), polymerase (P), and X genes (Hunt et al., 2000; Fig. 1). Due to a high error rate of the viral reverse transcriptase, HBV genome evolves over time and the estimated rate of nucleotide substitution is around 1.4–3.2 10 5/site per year (Okamoto et al., 1987). This unique replication strategy accounts for the majority of point mutations and deletions or insertions observed in the HBV genome. The long-time evolution of HBV therefore leads to the occurrence of various genotypes, subgenotypes, viral mutants, recombinants, and even quasispecies (Lau and Wright, 1993).

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3213 S1 pre 28

15 7

preS2

Polymerase

56

S

834

230

er

2458

DD YM

EcoRI 3221,1

rim

9

(+)

DR1

(−)

DR2

A RN

p

e

or

C

19

03

1622

Prec

ore

76

13

1816

183

7 X

Fig. 1 Hepatitis B virus is a partially double-stranded circular DNA virus, encoding four overlapping open reading frames. S for the surface gene, C for the core gene, P for the polymerase gene, and X for the X gene. Naturally occurring viral mutants including mutations in precore, core promoter and deletion in pre-S genes have been reported to be associated with the development of hepatocellular carcinoma.

The quasispecies nature refers to a mixture of closely related but distinct viral genomes in a given subject. HCC is the major primary cancer of the liver, and HCC has become the fifth most common cancer in men and eighth in women in the world, with an estimated 0.5–1 million new cases per year and 80% of them occurring in developing countries (Kao and Chen, 2005). HCC is also the third leading cause of cancer-related death worldwide, suggesting that current therapy for HCC is far from satisfactory. Remarkable geographic and ethnic variations have been found in the incidence of HCC, from the low rate of 3.8 per 100,000 among white men in the United States to the high rate of 18–35 per 100,000 among Asian men in the Far East and Southeast Asia (Gomaa et al., 2008). Most of the new cases occur in East or Southeast Asia. In contrast,

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the prevalence of HCC in Western countries has traditionally been low; however, increasing incidence has been reported in the United States and in some European countries (Jemal et al., 2009). Therapeutic options are available for HCC (Lin and Kao, 2010), including liver transplantation, surgical resection, and percutaneous ablation therapies, and these can be potentially “curative” if HCC is diagnosed at early stage. Use of these options varies between Asian and Western countries, except for liver transplantation, they are generally restricted to patients with early-stage HCC without advanced cirrhosis. Most HCC patients present with advanced disease and are ineligible for “curative” interventions. For patients who have unresectable and intermediate-stage HCC, transarterial chemoembolization (TACE) is the most commonly adopted therapy; however, careful patient selection is important and survival benefits are generally limited to those with well-preserved liver function and no vascular invasion or extrahepatic spread. Clinical trials have demonstrated a survival advantage of only approximately 2–3 months with sorafenib among patients with advanced-stage HCC (Cheng et al., 2009; Llovet et al., 2008). The outcomes of these trials in Asian and Western countries, however, were noticeably different in terms of overall survival, time to progression, and adverse events. Reasons for these differences are not fully understood, but may include differences in the severity of underlying disease, differing etiologic factors, and/or cultural differences in relation to the reporting of adverse events. The risk factors associated with the development of HCC include chronic infection with either HBV or hepatitis C virus (HCV) (Kao and Chen, 2005), the presence of cirrhosis, carcinogen exposure especially aflatoxin B1 (AFB1), alcohol abuse, genetic factors, male gender, cigarette smoking, and advanced age (Lok, 2004). Overall, at least 75–80% of HCC are attributable to persistent viral infections with either HBV (50–55%) or HCV (25–30%). Strong geographic correlations have been found between the incidence of HCC and the prevalence of hepatitis B surface antigen (HBsAg) or antibody to HCV (anti-HCV) (Gomaa et al., 2008; Kao and Chen, 2005; Lok, 2004). In HBV endemic countries, chronic HBV infection has the strongest association with the development of HCC (Kao and Chen, 2002; Lok, 2004). Several lines of evidence have strongly indicated an etiologic association between persistent HBV infection and HCC, including the geographical correlation between prevalence of chronic HBV infection and incidence of HCC (Fig. 2), high prevalence of HBsAg in HCC patients, increased relative risk of HCC in HBsAg carriers, presence of integrated HBV DNA in HCC, reduced incidence of childhood HCC after HBV vaccination, and association of chronic hepadnavirus infection with HCC in animal models (Lok, 2004). In this chapter, recent advances in the research of HBV-related HCC, including clinical and molecular aspects, are reviewed and discussed.

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<3.3

<5.6

<9.0

<15.0

<98.9

Age-adjusted incidence rates of liver cancer per 100,000 (men)

HBsAg prevalence 8%–High 2–7%–Intermediate <2%–Low Prevalence of global HBV infection

Fig. 2 Geographical correlation between age-adjusted incidence rate of hepatocellular carcinoma and global prevalence of chronic hepatitis B virus infection.

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II. FACTORS ASSOCIATED WITH HCC DEVELOPMENT IN PATIENTS WITH CHRONIC HBV INFECTION The identification of risk factors associated with end-stage liver disease, including cirrhosis and HCC, in HBV carriers is important for the development of preventive and treatment strategies (Wands, 2004). Previous studies have shown that risk factors associated with the development of HCC in HBV carriers include (1) virus-related factors such as persistently high levels of HBV replication, genotype C or D infection, and mutations in the HBV genome (core promoter [CP] and pre-S); (2) host-related factors such as old age (> 40 years), male gender, presence of cirrhosis, family history of HCC, Asian or African race, genetic diversity, and comorbidity of diabetes, obesity or nonalcoholic fatty liver disease; (3) external factors such as cigarette smoking , alcohol abuse, AFB1 exposure, and concurrent infection with HCV, hepatitis D virus (HDV), or human immunodeficiency virus (HIV) (Fattovich et al., 2008). These factors are found to act synergistically to induce disease progression and increased risk of cirrhosis and HCC (Table I).

Table I

Factors Associated with the Development of Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Virus Infection Virus Persistently high HBV replication Genotype (C > B, D > A) Specific HBV mutants (core promoter mutant, pre-S deletion)

Host Male gender Advanced age or longer duration of infection Presence of cirrhosis Family history of HCC Ethnicity (Asian, African > Caucasian) Genetic alteration Diabetes mellitus Obesity Hepatic steatosis Repeated hepatitis flare

Environmental Concurrent HCV, HDV, or HIV infection Alcohol drinking Cigarette smoking Aflatoxin exposure Betel nut chewing

HCC, hepatocellular carcinoma; HBV, hepatitis B virus; HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus.

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III. VIRAL FACTORS IN HBV-RELATED HCC A. Viral Load The impact of baseline viral load at enrollment on the risk of cirrhosis and HCC development over time has been increasingly recognized in HBV carriers aged between 30 and 65 years. In a population-based prospective cohort including seven townships in Taiwan, 3582 untreated HBV carriers were enrolled (REVEAL-HBV study; Fig. 3). Of them, 85% were HBeAgnegative and were followed for a mean duration of 11 years (Chen et al., 2006a; Iloeje et al., 2006). According to this landmark study, the higher the serum HBV DNA level at early 1990s, the higher the risk of cirrhosis and HCC development at early 2000s (Fig. 4). The cumulative incidence of cirrhosis increased with serum HBV DNA level and ranged from 4.5% to 36.2% for patients with a hepatitis B viral load of less than 60 IU/mL and 2 105 IU/mL or more, respectively (P < 0.001). After adjusting for HBeAg status and serum alanine transaminase (ALT) level among other

Seven townships in Taiwan; individuals with ages 30–65 years eligible N = 89,293

HCC free individuals enrolled N = 23,820 HBsAg negative or insufficient serum sample for tests removed HBsAg positive with adequate baseline HBV DNA sample N = 3851 HCV seropositive or diagnosed with cirrhosis or died within 6 months of entry removed HCC analysis, N = 3653 Follow-up 41,779 person-years

Cirrhosis analysis, N = 3582 Follow-up 40,038 person-years

Fig. 3 The flow of patients in a population-based prospective cohort study (REVEAL-HBV) to assess the impact of HBV DNA level at entry on the risk of cirrhosis and hepatocellular carcinoma over time.

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Fig. 4 Adjusted relative risks for (A) cirrhosis and (B) hepatocellular carcinoma by serum HBV DNA level at study entry for HBV carriers in a population-based prospective cohort study.

variables, hepatitis B viral load was the strongest predictor of progression to cirrhosis. The relative risk started to increase at an entry HBV DNA level of 2 103 IU/mL (relative risk 2.5; 95% CI, 1.6–3.8). Those with HBV DNA

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levels of 2 105 IU/mL or more had the greatest risk (relative risk 6.5; 95% CI 4.1–10.2; P < 0.001). Even in HBeAg-negative patients with normal serum ALT levels at entry, the risk of cirrhosis was also increased significantly as serum HBV DNA level increased (Iloeje et al., 2006). The relationship between serum HBV DNA level and risk of HCC was also evaluated. In the same prospective cohort, the risk factors associated with the development of HCC were assessed in 3653 HBV carriers age between 30 and 65 years (Chen et al., 2006a). A significant biological gradient of HCC risk by serum HBV DNA level from 60 IU/mL (undetectable) to 2 105 IU/mL or greater was observed. The relationship was most prominent for participants who were seronegative for HBeAg with normal serum ALT levels and no cirrhosis at study entry. Participants with persistent elevation of serum HBV DNA level during follow-up had the highest HCC risk. These results were confirmed by another prospective cohort study in 2763 adult HBV carriers with 11 years of follow-up from Haimen city in China, assessing the relationship between HBV viral load at entry and mortality (Chen et al., 2006b). In comparison with HBV carriers without detectable viremia, the relative risk for HCC mortality in those with low viral load (< 2 104 IU/mL) was 1.7 (95% CI, 0.5–5.7) and 11.2 (95% CI, 3.6–35.0) in those with high viral load ( 2 104 IU/mL). Taken together, the ample evidence from these different prospective cohort studies strongly documents that the best predictor of adverse outcomes (cirrhosis, HCC and liver-related mortality) in Asian adult HBV carriers is the serum HBV DNA level at enrollment, independent of HBeAg status, baseline serum ALT level, and other risk factors. However, whether there exists a “safe” serum HBV DNA level for nonprogressive liver disease in the real world and whether this phenomenon holds true for Western HBV carriers who acquire HBV infection later in life remain to be established. In a recent report, the serum samples of the subjects from the REVEAL-HBV study were reanalyzed by using a more sensitive molecular method with the detection limit of 20 IU/mL. The results showed that the hazard ratio of HCC development significantly increased with elevation of serum HBV DNA level after adjustment for other factors (Chen et al., 2007a). Another age, sex, and HBeAg status-matched case–control study of 92 HBV-related HCC patients and 184 CHB patients without HCC was conducted in Hong Kong (Fung et al., 2007), aiming to investigate the threshold HBV DNA level below which HCC is unlikely to occur. Serum HBV DNA levels were high in both HCC and control HBeAg-positive patients, while HCC was more likely to develop in HBeAg-negative patients with HBV DNA level > 2000 IU/mL. Of particular note is that 15% of HCC patients had HBV DNA levels < 200 IU/mL at the time of diagnosis. A recent study in Taiwan further indicated that even inactive HBV carriers have a substantial risk of HCC and liver-related death compared with individuals not infected with HBV (Chen et al., 2010). All these facts suggest that even a

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relatively low serum HBV DNA level (< 200 IU/mL) is associated with HCC development in a certain proportion of CHB patients. Although high serum HBV DNA level at study entry has been suggested to increase the risk of HCC in HBV carriers over time, little is known about the longitudinal course of viral load and its relationship with HCC development. A case–cohort study nested within a cohort of 2874 HBsAg-positive male Taiwanese government employees aged 30 years or older was thus conducted to address this issue (Wu et al., 2008a). HBV genotype and viral load were tested from 112 cases and 1031 noncases. Prediagnostic HBV DNA levels were measured in multiple samples collected from each man (total 7706 samples), taken over periods of up to 16 years before the diagnosis of HCC. The results showed that baseline viral load influenced HBV genotype-specific HCC risks and persistence of high viral load ( 4.39 log10 copies/mL) could predict HCC development. HBeAg (P < 0.0001), genotype C infection (P ¼ 0.0369) and longitudinal ALTelevation (defined as ALTabnormality in 50% of the visits) (P ¼ 0.0005) were positively correlated with longer duration of persistence for high viral load. After multivariate adjustment, genotype C (odds ratio [OR], 5.97; 95% CI, 3.44–10.34), high viral load detected at 50% of the visits (compared with sustained low viral load: OR, 5.04; 95% CI, 2.31–11.00) and longitudinal ALTelevation (compared with sustained normal ALT levels: OR, 2.84; 95% CI, 1.46–5.51) accounted for 43.5%, 57.2%, and 24.9% of HCC patients, respectively. These data suggested that sustained lower level of HBV DNA < 4.39 log10 copies/mL may be associated with normalization of ALT levels and decreased risk of HCC development. Because of the heterogeneity of HCC populations, risk factors may differ between old HCC patients and young HCC patients. Our previous case– control study showed that young ( 40 years old) HCC patients had lower viral load than old HCC patients (log10HBV DNA: 4.2 vs. 4.8, P ¼ 0.056). In addition, high viral load was associated with the development of HCC in old patients (OR, 1.584; 95% CI, 1.075–2.333; P ¼ 0.02) rather than in young patients (OR, 0.848; 95% CI, 0.645–1.116; P ¼ 0.239; Tsai et al., 2007). Thus, viral factors in association with the development of HCC may be different between young and old HBV-related HCC patients and the direct oncogenic pathway may play a more important role in the hepatocarcinogenesis of young patients. Even in patients who already have HCC, serum HBV DNA level appears to impact their prognosis. In Hong Kong, high viral load in unresectable HCC patients prior to systemic chemotherapy was found to be associated with a high incidence of hepatitis flare due to HBV reactivation and thus had an adverse effect on survival (Yeo et al., 2007). In addition, another study (Hung et al., 2008) aimed to identify the risk factors for HCC recurrence in 72 patients who underwent liver resection for HBV-related HCC. By using multivariate analysis, serum HBV DNA level > 2000 IU/mL (OR, 22.3,

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P ¼ 0.001), AFP > 1000 ng/mL (OR, 7.4, P ¼ 0.02), tumor size > 5 cm (OR, 5.1, P ¼ 0.02), and age > 60 years at the time of HCC resection (OR, 4, P ¼ 0.01) were found to be independently associated with HCC recurrence. Among them, viral load of > 2000 IU/mL was the most important correctable risk factor for HCC recurrence. A recent study from Taiwan also indicated that tumor factors are associated with early HCC recurrence within 2 years of surgery, whereas high viral load and hepatic inflammatory activity are associated with late recurrence (Wu et al., 2009). A retrospective study on 125 HBV-related HCC patients with a median follow-up of 104 weeks from China also indicated a positive relationship between baseline HBV DNA level, and the incidence of HCC recurrence or metastasis, ranging from 22% for HBV DNA level of less than 200 IU/mL to 80% for HBV DNA level of 20,000 IU/mL or greater (Huang et al., 2008). Whether profound suppression of HBV DNA level by current antiviral agents could reduce the incidence of hepatitis flare, HCC recurrence, or metastasis and improve survival in these HBV-related HCC patients awaits further investigations.

B. Genotype According to the heterogeneity of the virus sequence, at least eight HBV genotypes (A–H) are defined by divergence in the entire HBV genomic sequence > 8% (Schaefer, 2007). Epidemiologic studies have shown that each genotype has its distinct geographic and ethnic distribution (Kao, 2002; Kao and Chen, 2006). Genotypes A and D occur frequently in Africa, Europe, and India, while genotypes B and C are prevalent in Asia. Genotype E is restricted to West Africa, and F is found in Central and South America. Genotype G has been reported in France, Germany, and the United States. Lastly, the eighth genotype H was described in Central America (Fig. 5). Interestingly, it is noted that genotypes B and C are prevalent in highly endemic areas, such as Asian countries, where perinatal or vertical transmission plays an important role in spreading the virus and the same genotype may be conserved in the same population, whereas genotypes A, D, E, F, and G are frequently found in areas where horizontal transmission is the main mode of transmission. Extensive phylogenetic analyses have shown that HBV genotypes can be further subdivided into subgenotypes by at least 4% difference in entire genome sequence. Except for genotypes E and G, all genotypes have subgenotypes (Schaefer, 2007). Epidemiologic data show that in genotypes A, B, and C, respective subgenotype A1 (Aa)/A2 (Ae), B1 (Bj)/B2 (Ba), and C1 (Cs)/C2 (Ce) differ widely in many virologic aspects. These subgenotypes also display distinct geographic distribution as do genotypes (Liu and Kao, 2006; Schaefer, 2007). For example, in Asian countries, subgenotype B1 dominates in Japan and B2 in China and Vietnam. Subgenotype C1 is

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F G

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D

D

F,H

B,C

D

A,D

B,C

E

A,D

B,C

F F

D

A,D

A A,B,C,D

Fig. 5 Worldwide distribution of HBV genotypes. The size of capitals indicates the relative prevalence of each genotype in a given area.

common in southern China and Southeast Asia and C2 in Taiwan, Japan, Korea, and northern China (Table II). In recent years, ample evidence has recognized that HBV genotypes influence the natural course of liver disease in HBV carriers, especially in Asian countries where genotypes B and C prevail. We have summarized the current knowledge on this issue before (Kao, 2002; Kao and Chen, 2006; Liu and Kao, 2006; Liu et al., 2005). In addition, due to the unique distribution of HBV genotypes in Asian and Western countries, the clinical significance and virologic characteristics of HBV genotype could only be reliably compared between genotypes B and C or genotypes A and D. In brief, genotype B patients have an early and frequent HBeAg seroconversion (shorter immune clearance phase) than genotype C patients, and are thus associated with less progressive liver disease (Chan et al., 2004; Chu and Liaw, 2007; Chu et al., 2002; Kao et al., 2000a, 2004; Yuen et al., 2004, 2007). Similarly, genotype D has been shown to have a less favorable prognosis than genotype A (Liu and Kao, 2006). The clinical significance of genotypes E–H remains to be examined. Nevertheless, it should be emphasized that all HBV genotypes can lead to end-stage liver disease including cirrhosis and HCC. Most previous retrospective or case–control studies indicated that genotype C patients had more severe liver disease including cirrhosis and HCC than genotype B patients. Our recent 14-year prospective study on 4841 Taiwanese men who were HBV carriers also demonstrated that genotype C infection was associated with an increased risk of HCC compared with other HBV genotype infections (adjusted OR, 5.11; 95% CI, 3.20–8.18; Yu et al., 2005). A community-based prospective cohort study further showed that genotype C infection was associated with the risk of HCC development

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Table II

Worldwide Distribution of HBV Subgenotypes

HBV subgenotype A1 (Aa) A2 (Ae) B1 (Bj) B2 (Ba) B3 B4 B5 C1 (Cs) C2 (Ce) C3 C4

Distribution Asia and Africa: India, Japan, Nepal, Philippine, South Africa Europe and North America: France, Germany, Poland, United States, United Kingdom Japan China, Taiwan, Vietnam Indonesia Vietnam Philippines Southeast Asia: Bangladesh. China, Hong Kong, Malaysia, Thailand, Vietnam East Asia: China, Japan, Korea, Taiwan Polynesia Northeast Australia

(Yang et al., 2008). These findings indicated that genotype C infection is correlated with a higher risk of developing HCC in HBV carriers. Of interest, we also found genotype B was significantly more common in patients with HCC aged less than 50 years compared with age-matched asymptomatic carriers in Taiwan (80% vs. 52%; P ¼ 0.03). This predominance was more remarkable in younger patients with HCC, being 90% in those aged less than 35 years, and most were non-cirrhotic (Kao et al., 2000a). Similar findings were observed in a recent report from China, in which all HCC patients younger than 35 years were infected with genotype B (Yuan et al., 2007). These data thus suggested that certain genotype B strains may be associated with the development of HCC in young HBV carriers. However, the pathogenesis remains to be investigated. Several studies from Japan, Hong Kong, and China already confirmed that genotype C infection has a higher risk of HCC development then genotype B infection (Chan et al., 2004; Ding et al., 2001; Orito et al., 2001; Yuen et al., 2004). Recent analysis from the REVEAL-HBV study showed that the hazard ratio of cirrhosis after adjustment for age, gender, smoking, alcohol use, and HBV DNA level was 1.9 for genotype C compared to genotype B, indeed suggesting genotype C was an independent risk factor for cirrhosis development in HBV carriers (Chen et al., 2007b). HBV genotypes also influence the clinicopathologic features of patients with resectable HCC. Among 193 patients with resectable HBV-related HCC in Taiwan, genotype B patients were less associated with cirrhosis compared with genotype C patients (33% vs. 51%, P ¼ 0.01). Pathologically, genotype B patients had a higher rate of solitary tumor (94% vs. 86%, P ¼ 0.048) and more satellite nodules (22% vs. 12%, P ¼ 0.05) than

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genotype C patients (Lin et al., 2007). These characteristics may contribute to the HCC recurrence patterns and prognosis of HBV-related HCC patients with genotype B or C infection (Chen et al., 2004). Although the influence of HBV genotypes on disease progression and clinical outcome has been increasingly recognized, the virologic and molecular mechanisms involved remain largely unknown. The influence of HBV genotypes on the intra- and extracellular expression of HBV DNA and antigen has recently been reported (Sugiyama et al., 2006). The intracellular expression of HBV DNA and HBcAg was higher for genotypes B and C than genotypes A and D. So did the extracellular expression of HBV DNA and HBeAg. The intracellular accumulation of HBV DNA and antigens may play a role in inducing liver cell damage. In addition, the highest replication capacity of genotype C may explain why genotype C is associated with more severe histologic liver damage than other genotypes. On the other hand, a strong extracellular virion secretion may endow a high infectious capacity to blood from individuals infected with this genotype. In line with in vitro experiments, our recent study on 70 HBeAg-positive CHB patients also showed that the expression of intrahepatic HBcAg levels was comparable between HBV genotypes B and C infection (Liu et al., 2009a). These data suggest that virologic differences may exist among HBV genotypes; however, whether immunopathogenesis differs between various HBV genotypes need further studies. A timely relevant study showed that the frequency and interferon- (IFN-)-producing capacity of peripheral blood plasmacytoid dendritic cells (pDCs) were dramatically reduced in chronic hepatitis B patients at the immunoactive phase, and genotype C patients harbored an even lower reduction in IFN- production than genotype B patients (Wang et al., 2007a). This observation may correlate with different outcomes of immunomodulatory treatment and the progression of liver disease in HBV carriers infected with different genotypes. In summary, virologic differences and subsequent interactions with host immune responses may influence clinical outcomes and epidemiologic characteristics of patients with different HBV genotype infections.

C. Subgenotype Although the clinical significance of HBV genotype has become recognized, limited studies have examined the clinical relevance of HBV subgenotypes. HBV subgenotype A1 appears to be associated with low serum HBV DNA levels as well as a low prevalence of serum HBeAg and is implicated in the high incidence of HBV-related HCC in Africa (Kramvis et al., 1998); whereas subgenotype A2 has a higher rate of sustained remission

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after HBeAg seroconversion and a lower rate of liver-related death than other genotypes during long-term follow-up (Sugauchi et al., 2004; Tanaka et al., 2004). The positivity of HBeAg has been found to be more frequent in carriers of subgenotype B1 than B2 (Sugauchi et al., 2002). Another study analyzed the distribution of HBV subgenotypes in 296 HBV-related HCC patients collected from all over Japan (Orito et al., 2005). They found HBV subgenotype B2 in 4.4%, B1 in 7.4%, and genotype C in 86.5%. Interestingly, in the Tohoku district and Okinawa, subgenotype B2, B1 and genotype C were found in 6.7%, 40.0%, and 48.9%, respectively, compared to 4.0%, 1.6%, and 93.2% in the other districts in Japan. In addition, subgenotype B1 was more frequently found in the group older than 65 years while subgenotype B2 was found in all age groups. These data suggest that HBV subgenotype B1 may run a more indolent course than subgenotype B2. HBV genotype C is divided into subgenotypes C1–C4. In Hong Kong, 80% of HBV genotype C patients belonged to subgenotype C1, and the remaining 20% belonged to subgenotype C2 (Chan et al., 2005). When subgenotype C1 and C2 were compared, subgenotype C1 was associated with a higher tendency to develop basal CP (BCP) mutations (80% vs. 50%; P ¼ 0.14), a higher prevalence of C at nucleotide 1858 (C-1858) (95% vs. 0%; P < 0.001), and a lower prevalence of precore stop codon mutations (5% vs. 50%; P ¼ 0.002). It is hence proposed that subgenotypes C1 and C2 have different epidemiologic distributions and virologic characteristics. To test this speculation, we studied the distribution of HBV subgenotypes in 242 Taiwanese HBsAg carriers with various stages of liver disease, and found that HBV subgenotype C2 was the predominant subgenotype in Taiwan. In addition, there was no significant difference in the distribution of the HBV genotype C subgenotypes among patients with different stages of liver disease, suggesting subgenotypes of genotype C may have minimal impact on liver disease progression of chronic hepatitis B in Taiwan (Tseng et al., 2007). Similarly, a cross-sectional study of 211 patients with various stages of liver disease in China showed that the proportion of HBV genotype C was greater among cirrhosis and HCC patients, while genotype B was common in chronic hepatitis patients. In addition, no significant differences in clinical features were found between patients with HBV subgenotypes B2, C1, and C2 (Wang et al., 2007b). A recent prospective study on 1006 CHB patients with a median follow-up of 7.7 years from Hong Kong showed that subgenotype C2 has the highest risk of HCC (hazard ratio, 2.75; 95% CI, 1.66–4.56; P < 0.0001) and subgenotype C1 has intermediate risk (hazard ratio ¼ 1.70; 95% CI, 1.09–2.64; P ¼ 0.020) compared to genotype B (Chan et al., 2008). Nevertheless, further studies from different parts of world are needed to confirm the clinical impact of each HBV subgenotype on the pathogenesis and progression of liver diseases.

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D. Naturally Occurring Mutants Due to the spontaneous error rate of viral reverse transcription, naturally occurring HBV mutants emerge under the pressure of host immunity or specific therapy during the course of chronic HBV infection (Gunther et al., 1999). These HBV mutants could display alteration of epitopes important in the host immune recognition, enhanced virulence with increased levels of HBV replication, resistance to antiviral therapies, or facilitated cell attachment/penetration and thus have implications at both the clinical and epidemiologic levels. Several HBV mutant strains including mutations in precore, CP, and deletion mutation in pre-S/S genes have been reported to be associated with the pathogenesis of fulminant hepatitis or progressive liver disease, including cirrhosis and HCC (Hunt et al., 2000; Fig. 1).

E. Precore and CP Mutants The production of HBeAg is regulated by precore and CP genes of HBV, precore nucleotide 1896 mutation from guanine (G) to adenine (A) as well as changes of two nucleotides, an adenine (A) to thymine (T) transversion at nucleotide 1762 together with a guanine (G) to adenine (A) transition at nucleotide 1764 within the BCP lead to a proportion of HBeAg-negative patients continue to have moderate levels of HBV replication and active liver disease (Carman et al., 1989; Okamoto et al., 1990, 1994). Of HBV viral proteins, X protein is a multifunctional regulator that modulates host transcription, cell responses to genotoxic stress, protein degradation, and signal transduction pathways (Murakami, 2001). This property makes the X gene a candidate for a role in the development of HCC in patients with chronic HBV infection. Several mutations in the X gene of the HBV genome are frequently found in patients with advanced liver disease, suggesting that these mutants may play a certain role in the pathogenesis of HBV infection. Among these mutations, double nucleotide mutations (A1762T/G1764A) in BCP also affect codons 130 and 131 of the X protein (K130M and V131I) (Fig. 6). Taken together, a dual change of A1762T/ G1764A in BCP will not only diminish the production of HBeAg, enhance viral replication and lead to increased host immune response but also induce an amino acid change in the X protein to promote hepatocarcinogenesis. Clinically, the role of precore G1896A stop codon mutation in disease progression remains debatable. Recent case–control and longitudinal studies (Liu et al., 2006a; Yang et al., 2008) showed precore G1896A stop codon mutation may reduce the risk of HCC in HBV carriers (Table III).

37

Recent Advances in the Research of Hepatitis B 1613

1849

Core promoter

Upstream regulatory region

Basal core promoter 1742

Enhancer II 1627

1774

Box a 1644

Box b 1666

1701

1713

1653 GTCTTACATAAGACGACTCT

1752/1753 1762/1764/1766 GATTAGGTTAAAGGTCTTT

C V I

T HB-X protein

L H K H94Y

R

T L K

A G V

I127N/S/T K130M V131I

Fig. 6 Commonly encountered mutations in core promoter region of hepatitis B virus genome.

Table III

Combined Risk of Hepatocellular Carcinoma Associated with Serum HBV DNA Level and Basal Core Promoter A1762T/G1764A Mutation

Age (per year) Gender (male) Genotype (C) 5 Viral load 10 copies/mL PC/BCP W/W M/W W/M M/M

OR

95% CI

1.15 3.15 1.00 – – 1.43 8.44 39.48 30.41

1.11–1.20 1.26–7.87 0.40–2.53 – – 0.27–7.47 1.79–39.85 5.06–308.10 6.18–149.57

OR, odds ratio; 95% CI, 95% confidence interval; PC, precore 1896; BCP, basal core promoter 1762/1764; W, wild strain; M, mutant strain. Multivariate analysis, adjusted for age, gender, and viral genotype. Precore G1896A stop codon mutation may reduce the risk of HCC in HBV carriers.

In contrast, although BCP A1762T/G1764A mutation can also be found in asymptomatic hepatitis B carriers, many cross-sectional and case–control studies have consistently demonstrated that this mutation is correlated with progressive liver disease including HCC (Chen et al., 2006c; Kao et al., 2003; Kuang et al., 2004; Lin et al., 2004, 2005, 2006a,b; Tong et al., 2006). For example, in a cohort study of 250 genotype B- or C-infected

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HBV carriers with different stages of liver disease, we found that genotype C patients had a higher prevalence of BCP A1762T/G1764A mutation than genotype B (OR, 5.18; 95% CI, 2.59–10.37; P < 0.001). The likelihood of A1762T/G1764A mutation paralleled the progression of liver disease, from 3% in inactive carriers to 64% in HCC patients (OR, 20.04; 95% CI, 7.25– 55.41; P < 0.001). Patients with BCP A1762T/G1764A mutation were significantly associated with the development of HCC than those without (OR, 10.60; 95% CI, 4.92–22.86; P < 0.001), and the risk was observed for both genotypes B and C (Kao et al., 2003). Consistently, it has been shown that BCP A1762T/G1764A mutation (OR, 1.92; 95% CI, 1.14–3.25) was statistically significantly associated with HCC risk even after adjusting for ALT levels, anti-HBe, HBV genotype, viral load, and other sequence variants (Chou et al., 2008). Further analysis indicated that the increased HCC risks for at-risk sequence variants were attributable to the persistence of these variants. These findings are confirmed by a long-term follow-up study involving 400 HBV carriers in the United States (Tong et al., 2006) and a case–control study from Hong Kong (Yuen et al., 2004), in which the presence of BCP A1762T/G1764A mutation was independent predictors for the risk of HCC development. In the Philippines, a cross-sectional study of 100 HBV carriers with various stages of liver disease revealed that 51 HBV genotype A1, 22 genotype B and 27 genotype C strains, and genotypes B and C were more prevalent than genotype A in cirrhosis and HCC patients (P < 0.02). In addition, the prevalence of BCP A1762T/ G1764A mutant was higher in HCC patients with genotypes B and C. Multivariate analysis indicated that age and CP mutation were risk factors for HCC development (Sakamoto et al., 2006). Taking these lines of evidence together, BCP A1762T/G1764A mutation seems to play an important role in the pathogenesis of liver disease progression and serves as the strongest viral factor associated with HCC risk in HBV carriers. The biological mechanisms involved in BCP A1762T/G1764A mutationrelated hepatocarcinogenesis remain to be established. A recent in vitro study showed that BCP activity of HBV strains isolated from asymptomatic carriers was decreased when 1762A is mutated to 1762T or 1764G is mutated to 1764A by using site-directed mutagenesis (Dong et al., 2008). In contrast, the promoter activity of HBV strains isolated from HCC patients was increased when 1762T and 1764A are reversely mutated into 1762A and 1764G, respectively. In addition, 1764G seems to contribute more promoter activity than 1762T. Further experiment indicated that T1762A and G1764A double mutations could synergize the reduction of promoter activity. Our recent study on 70 patients with HBeAg-positive CHB also indicated that the expression level of HBcAg correlated with high serum viral load (P ¼ 0.015) and BCP wild-type sequence (P ¼ 0.037). In addition, in vitro assays supported that BCP A1762T/

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G1764A mutant had lower subcellular expression of HBcAg compared with BCP wild-type strain (Liu et al., 2009a). Apart from BCP A1762T/G1764A mutation, mutations in other parts of the CP region have become increasingly recognized to be associated with HCC development in patients with chronic HBV infection (Ito et al., 2006; Liu and Kao, 2008; Muroyama et al., 2006; Shinkai et al., 2007; Tanaka et al., 2006; Yeh et al., 2000) (Table IV).

F. Pre-S Deletion Deletions in the pre-S gene of HBV genome are frequently in chronic HBV infection (Fernholz et al., 1991; Gerken et al., 1991). The deletion over pre-S gene may affect the expression of middle and small surface proteins, resulting in intracellular accumulation of large surface protein (Xu and Yen, 1996), and contribute to more progressive liver cell damage and finally hepatocarcinogenesis (Fan et al., 2001; Sugauchi et al., 2003). In our recent case–control study, pre-S deletion mutant of HBV was determined in 202 asymptomatic carriers and 64 HCC patients with chronic HBV genotype B or C infection. The presence of pre-S deletion mutant was independently associated with the development of HCC (OR, 3.72; 95% CI, 1.44–9.65, P ¼ 0.007) (Chen et al., 2006c). Our further mapping study of pre-S region revealed all the deletion regions encompassed T- and B-cell epitopes, and most of them lost one or more functional sites, including polymerized human serum albumin-binding site and nucleocapsid-binding site. These findings lend support to the biological significance of emerging HBV pre-S deletion mutants, which may contribute to more progressive liver cell damage and finally hepatocarcinogenesis. A longitudinal study on 141 HBeAgnegative patients without liver cirrhosis or HCC at study entry from Taiwan also confirmed that pre-S deletion was a significant risk factor for HCC development in them (Chen et al., 2007c).

G. Potential Interactions Between Known HBV Factors In light of these emerging data, HBV DNA level, HBV genotypes, and mutant stains are closely associated with the development of HBV-related HCC. In our earlier study, we already found that genotype C has a higher frequency of BCP A1762T/G1764A mutation than genotype B that is 50% versus 6% (Kao et al., 2000b). It was recently proposed genomic algorithms associated with HCC on the basis of genotype/subgenotype-specific mutations after comparing the complete genomic sequences of HBVs among 100 HCC patients and 100 age-matched CHB patients without HCC (Sung et al.,

Table IV Reported Associations Between Core Promoter Mutations and Development of HBV-Related Hepatocellular Carcinoma Author (year)

Study design

Study population

Yeh et al. (2000)

Cross-sectional study

67 HCC; 100 chronic hepatitis B

Kao et al. (2003)

Cross-sectional study

Liu et al. (2006a)

Cross-sectional study

60 inactive carriers; 190 patients with different stages of liver disease 160 carriers; 200 HCC

Tanaka et al. (2006)

Matched cross-sectional control study

118 carriers with HBV/C1 (44% HCC) and 210 HBV/C2 (46% HCC)

Muroyama et al. (2006)

39 HCC; 36 non-HCC

Chou et al. (2008)

Matched cross-sectional control study Matched cross-sectional control study Matched cross-sectional control study Longitudinal, hospital-based cohort

Yang et al. (2008)

Longitudinal, community cohort

Ito et al. (2006) Shinkai et al. (2007)

40 inactive carriers; 40 chronic hepatitis B; 40 HCC; all HBeAg-negative 80 HCC; 80 non-HCC; all infected with HBV/C2 4841 male carriers 2762 HBsAg-positive adult population; the majority HBeAg-negative

Remarks HB-X protein Codon-31 mutation associated with HCC A1762T/G1764A dinucleotide mutation associated with HCC High viral load and basal core promoter A1762T/G1764A mutation associated with HCC C1653T and/or T1753V mutations associated with HCC; HBV/C subgenotypes have differential mutation patterns Codon-38 change associated with HCC C1653T in the box associated with HCC C1653T and T1753V mutation associated with HCC Temporal relationship between basal core promoter mutations and risk of HCC HCC associated with genotype C and core promoter A1762T/G1764A dinucleotide mutation

HCC, hepatocellular carcinoma; HB-X protein, hepatitis B X protein; HBV, hepatitis B virus; HBsAg, hepatitis B surface antigen.

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2008). For example, mutations C1165T, A1762Tand G1764A, T2712C/A/G, and A/T2525C were associated with genotype B-related HCC, mutations T31C, T53C, and A1499G were associated with subgenotype C2-related HCC, and mutations G1613A, G1899A, T2170C/G, and T2441C were associated with subgenotype C1-related HCC. Amino acid changes caused by these mutations were found throughout the X, envelope, and precore/core regions in association with HBV genotype B, C2, and C1, respectively. However, it is still unclear whether a specific combination of these factors is associated with the risk of the development of HCC. In one of our prospective studies, the risk of HCC was shown to increase approximately fivefold among men infected with HBV genotype C compared with genotype B. HBV viral load was higher with HBV genotype C than with HBV genotype B, and men who had both HBV genotype C and a very high hepatitis B viral load had a 26-fold higher risk of HCC than those with other genotypes and low or undetectable viral loads (Yu et al., 2005). These observations suggest additive risks of viral load and HBV genotype C in the development of HCC. Similarly, a study from Hong Kong reported that serum HBV DNA, HBV genotypes, liver cirrhosis, male sex, older age, and lower serum albumin levels were independent risk factors of HCC, and high HBV DNA levels (log HBV DNA > 6.5 copies/mL) as well as HBV genotype C, particularly subgenotype C2, may increase the risk of HCC in CHB patients (Chan et al., 2008). We recently investigated the independent and interactive effects of each known viral factor on the development of HCC. Compared with patients with low HBV load and the BCP A1762/ G1764 wild-type strain, the adjusted OR of developing HCC was more than 30-fold in patients with an HBV load 20,000 IU/mL and the BCP A1762T/ G1764A mutant, irrespective of HBV genotype (Liu et al., 2006a) (Table III). In addition to serum viral load at study entry, The REVEAL-HBV Study Group also investigated the association of HBV genetic characteristics, including HBV genotype and specific genetic mutations, with the risk of HCC development (Yang et al., 2008). They found that the multivariable-adjusted hazard ratio of developing HCC was 1.76 (95% CI, 1.19–2.61) for genotype C versus genotype B, 0.34 (95% CI, 0.21–0.57) for precore G1896A versus wild type, and 1.73 (95% CI, 1.13–2.67) for BCP A1762T/G1764A versus wild type. HCC risk was highest among participants infected with genotype C HBV and wild type for the precore 1896 variant and mutant for the BCP 1762/1764 variant (adjusted hazard ratio, 2.99, 95% CI, 1.57–5.70; P < 0.001), independent of serum HBV DNA level. Studies of HBV-related HCC in patients without cirrhosis may help to explain the direct effect of viral factors in HCC development. We have examined the role of BCP A1762T/G1764A mutation, precore G1896A mutation and serum viral load in liver cancer, presenting in the absence of cirrhosis, by comparing 44 patients without cirrhosis, but with HBV-related

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HCC, to 42 individuals with cirrhosis and HBV-related HCC (Liu et al., 2006b). Our data showed that male gender, BCP A1762T/G1764A mutation, and viral load greater than 20,000 IU/mL were independently associated with the risk of HCC development in non-cirrhotic CHB patients. These observations suggest that viral factors predisposing to HCC development may be similar between cirrhotic and non-cirrhotic patients. We also addressed the interactions among pre-S deletion, precore mutation, and BCP A1762T/G1764A mutation in various stages of chronic HBV infection (Chen et al., 2006c). The results revealed that the presence of pre-S deletion and BCP A1762T/G1764A mutation were significantly associated with the development of progressive liver diseases. In addition, combination of mutations rather than single mutation was associated with the development of progressive liver diseases, especially in combination with pre-S deletion. Similarly, a case–control study from Hong Kong on the risks for HCC with respect to HBV genotypes, specific viral mutants, serum HBV DNA levels, and cirrhosis showed that CP mutant, T1653, HBV DNA 2000 IU/mL, and cirrhosis were independent factors for HCC. In addition, the risks remarkably increased in HBV carriers with these factors in combination (Yuen et al., 2008). In Japan, an age, sex, and HBeAg status-matched cross-sectional control study was conducted to determine HCC-associated mutations of the HBV genome in the entire X, CP, and precore/core regions between 80 patients infected with HBV subgenotype C2 with HCC and 80 without HCC. They found the prevalence of the T1653 mutation in the box region, and V1753 and A1762T/G1764A mutations in the BCP region were significantly higher in the HCC group than in the non-HCC group. Further multivariate analysis showed that the presence of T1653, V1753, and low platelet count was independent predictive factors for HCC in patients with HBV subgenotype C2 (Shinkai et al., 2007). In China, a cross-sectional study indicated that V1753 and BCP A1762T/G1764A mutation seem to be associated with HCC development, especially in patients with HBV subgenotype C1 (Yuan et al., 2007). A recent study from Taiwan reported that HBV with a complex mutation pattern (pre-S deletion, A1762T/G1764A, and T1766 and/or A1768 mutants) rather than a single mutation was associated with the development of liver cirrhosis, and the patterns of mutation combinations differed between HBV genotypes B and C (Chen et al., 2007c). These findings from Asian countries altogether suggest that in addition to HBV DNA level, accumulation of complex viral mutants with precore mutation, BCP A1762T/G1764A mutation, T1653, and pre-S deletion mutation may affect the long-term outcomes of CHB patients with genotype B or C infection. Further studies are required to see whether the same story holds true for patients with genotype A or D infection in other parts of the world.

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H. Nomogram for Predicting HCC Risk In clinical practice, counseling CHB patients on their individual risk of disease progression is necessary, and thus several predictive scores or nomograms for the prediction of HCC risk in CHB patients have been developed (Wong et al., 2010; Yang et al., 2010; Yuen et al., 2009). The clinical scoring systems or nomograms usually consist of previously confirmed independent risk predictors such as sex, age, family history of HCC, alcohol consumption, serum ALT level, HBeAg status, serum HBV DNA level, and/or HBV genotype. These easy-to-use nomograms based on noninvasive clinical characteristics are found to accurately predict HCC risk in either community- or hospital-based HBV carriers and may facilitate the communication between practicing physicians and patients in daily practice. However, these predictive scoring systems need further validation in different populations of the world.

I. Role of Occult HBV Infection in Hepatocarcinogenesis The clinical significance of detectable HBV DNA in blood or liver tissues but with undetectable HBsAg or so-called “occult HBV infection” has been a matter of debate for many years (Marrero and Lok, 2004). Although controversies exist, many studies indicated that occult HBV infection does occur and the prevalence rates vary widely in different geographic regions and clinical settings. A higher rate of occult HBV infection has been reported in Western chronic hepatitis C patients with HCC (Cacciola et al., 1999); suggesting concomitant HBV and HCV infection could increase the risk of HCC development. However, our data did not support this conclusion (Kao et al., 2002) and this discrepancy may be reasoned by the different rates of HBV endemicity in different geographic areas. Several mechanisms are raised to account for the absence of detectable HBsAg in these patients, including progressive decline in HBV replication, genetic mutations in the HBsAg gene, and environmental factors (Brechot, 2004). In patients with HCC and occult HBV infection, HBx protein expression and cellular gene cis-activation can be identified in HCC cells. Therefore, the mechanisms speculated for HBsAg-positive HCC may also work for HBsAg-negative HCC (Pollicino et al., 2004). Occult HBV infection may become an emerging issue for epidemiologic studies of HCC, especially in HBV endemic areas.

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IV. NONVIRAL FACTORS IN HBV-RELATED HCC Several large population studies have indicated that obesity and type 2 diabetes are associated with an increase in HCC incidence and mortality; however, the possible joint influence of obesity/diabetes and HBV/HCV infections on the risk of HCC remains to be better understood. And thus, we conducted a long-term follow-up study that included 23,820 residents with a followed-up for 14 years to explore the relationship between obesity/ diabetes as well as other metabolic factors and risk of HCC stratified by HBV and HCV infection (Chen et al., 2008). The results showed that extreme obesity (body mass index 30 kg/m2) was independently associated with a fourfold risk of HCC among HCV patients and a twofold risk in subjects negative for both HBV and HCV infections, but not in HBV patients. In addition, diabetes was associated with HCC in all three groups, with the highest risk in HCV patients (RR, 3.52; 95% CI, 1.29–9.24) and lowest in HBV carriers (Relative Risk (RR), 2.27; 95% CI, 1.10–4.66). They found more than 100-fold increased risk of HCC in HBV or HCV carriers with both obesity and diabetes, indicating synergistic effects of metabolic factors and hepatitis. These findings suggest that both obesity and diabetes are factors predictive of HCC risk, but with some difference in HBV and HCV patients. The mechanisms related to these differences remain to be investigated. AFB1 has long been known to be a hepatocarcinogen. A recent Taiwanese study evaluated the role of oxidative stress and aflatoxin exposure on risk of HCC by using a case–control study nested within a large community-based cohort (Wu et al., 2008b). Urinary AFB1 metabolites, 8-oxo-7,8-dihydro-20 deoxyguanosine (8-oxodG), and the level of urinary 15-F(2t)-isoprostane (15-F(2t)-IsoP), a biomarker of lipid peroxidation, were determined. Urinary AFB1 metabolites and 8-oxodG were found to be associated with the level of urinary 15-F(2t)-IsoP, and urinary 15-F(2t)-IsoP was significantly associated with HCC risk (OR, 2.53; 95% CI, 1.30–4.93). In addition, the combination of urinary 15-F(2t)-IsoP above the mean and HBV infection resulted in an OR of 19.01 (95% CI, 6.67–54.17) compared with those with low urinary 15-F(2t)-IsoP and without HBV infection. These data suggest that elevated urinary 15-F(2t)-IsoP levels may correlate with increasing level of AFB1 exposure and are associated with an increased HCC risk, especially in HBV patients. A similar study also showed that polycyclic aromatic hydrocarbon (PAH)-albumin adducts were associated with an increased HCC risk, especially among those with high AFB1 exposure and chronic HBV infection, implying environmental PAH exposure may enhance the hepatocarcinogenicity of chronic HBV infection (Wu et al., 2007). Familial predisposition as a risk factor for HCC in HBV carriers has been suggested but not thoroughly explored before. In Taiwan, a case–control

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family study was conducted on data from first-degree relatives of 553 HBV carriers who had newly diagnosed HCC (case subjects) and 4684 HBV carriers without HCC (control subjects) (Yu et al., 2000a). The results showed that HBV carriers with a family history of HCC had a multivariate-adjusted rate ratio for HCC of 2.41 (95% CI, 1.47–3.95) compared with HBV carriers without a family history of HCC. For carriers with two or more affected relatives, the ratio increased to 5.55 (95% CI, 2.02–15.26). The excess risk of HCC among relatives was particularly evident in siblings and parents. Among relatives of case subjects, the cumulative risk of HCC was greater if the case subjects were diagnosed before 50 years. Therefore, first-degree relatives of patients with HBV-related HCC appear to be at increased risk of HCC development. Regarding the role of genetic alterations in inflammatory hepatocarcinogenesis of HBV infection, genetic variations in cytokines, antioxidant enzymes, and DNA repair genes and risk of HBV-related HCC has been studied (Chen et al., 2005). Ten polymorphisms in the genes for interleukin1beta (IL-1), interleukin-1-receptor antagonist (IL-1RN), tumor necrosis factor-alfa (TNF-), glutathione S-transferase, XRCC1, hMLH1, and XPD in 577 HBV carriers with HCC and 389 HBV carrier controls were analyzed. The data indicated that only the hMLH1-93*A allele significantly increased HCC risk. However, there was a dose-dependent association between the number of putative high-risk genotypes in the IL-1, TNF-, hMLH1, and XRCC1 genes and HCC. The adjusted OR for HBV carriers with 3 putative high-risk genotypes was 9.29 (95% CI, 2.90–29.75) compared with those with none or only one of the high-risk genotypes. In addition, smoking modified the combined effect of multiple loci in the IL-1RN, IL-1, TNF-, hMLH1, and XRCC1 genes, and a high-risk multilocus genotype only significantly increased the risk in smokers (adjusted OR, 4.84; 95% CI, 1.69–13.92). In summary, genetic variations in cytokine and DNA repair genes contribute to the susceptibility to HBV-related HCC, and smoking seems to increase such genetic susceptibility.

V. PRIMARY PREVENTION OF HBV-RELATED HCC As chronic HBV infections is a common cause of HCC, the best and costeffective strategy to prevent HBV infection is to implement universal hepatitis B vaccination (Asia-Pacific Working Party on Prevention of Hepatocellular Carcinoma, 2010). Ample evidence documents that vaccination of newborns against HBV infection in Taiwan has effectively reduced persistent HBV infections from 15% in the pre-vaccination era to < 1% in the post-vaccination era (Kao and Chen, 2008). In addition, our recent population-based study in

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Taiwan documented that the prevention of HCC by HBV vaccine has extended from childhood to early adulthood (Chang et al., 2009). The study included 1958 individuals aged 6–29 years diagnosed with HCC between July 1, 1983 and June 30, 2004. Of these, 508 were aged 6–19 years at diagnosis (444 unvaccinated, 64 vaccinated) and 1450 were aged 20–29 years at diagnosis (all unvaccinated). In the unvaccinated cohorts, there was a trend for higher incidence of HCC with increasing age; this was significant for those aged 20 years or older (P < 0.001) but not for those below age 20 years. For children aged 6–19 years, vaccination was associated with a significant reduction in the age-specific incidence of HCC (P < 0.001 for each age group [6–9, 10–14, and 15–19 years old at diagnosis]). In each age group and in both vaccinated and unvaccinated cohorts, HCC was more common in boys than in girls. Multivariate analysis adjusted for birth cohort, age, and sex showed an adjusted relative risk of HCC of 0.31 for vaccinated birth cohorts compared with unvaccinated birth cohorts (P < 0.001), and an adjusted relative risk of 2.50 for boys compared with girls (P < 0.001). We continue to follow up the results to demonstrate the anticipated efficacy of childhood HBV vaccination in preventing HCC in adulthood in the near future.

VI. MOLECULAR CARCINOGENESIS OF HBV-RELATED HCC The paradigm of human cancer as a consequence of aberrations of the genome of somatic cells has been firmly established (Chen and Chen, 1999). As the genome largely dictates the biological characteristics of normal cells, the mutated genome of the cancer will also definitely influence cancer behavior; that is, the mutated genes and the subsequently altered gene expression in the cancer cells may account for the cancer’s nature and even the clinical outcome. Following this trend, genetic or genomic studies of HCC have substantially increased and improved our understanding about critical genes and chromosomal changes in the hepatocarcinogenic process. As the development of HBV-related HCC is an interplay between HBV and host hepatocytes, the process of chronic inflammation in the liver matters. In addition, both viral and host genomes contribute to the final pathogenic outcome, either individually or synergistically. The study of chronic inflammation and the genetic factors predisposing to hepatocarcinogenesis will help disclose the critical carcinogenic mechanisms. Finally, an understanding of the unique aberrant biology in cancer cells may help the design of effective therapeutic strategies in the future.

Recent Advances in the Research of Hepatitis B

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A. Chronic Inflammation: A Critical Step Toward Hepatocarcinogenesis and the Role of Nuclear Factor-kB Ample evidence from epidemiologic and clinical studies first linked chronic inflammation to an increased risk of various tumors, including liver, gastric, colon, ovarian, breast, prostate, and other cancers (Karin et al., 2006; Mantovani et al., 2008). In the case of HCC, in addition to major risk factor of chronic HBV and HCV infections, alcohol abuse, hereditary iron overload, and obesity-related nonalcoholic fatty liver disease can all induce persistent inflammation of the liver and subsequent malignant transformation of hepatocytes (Gao et al., 2008; Maher et al., 2008; Seitz and Stickel, 2007; Shoelson et al., 2007). Therefore, irrespective of the etiology, chronic inflammation of the liver is recognized as the critical predisposing factor in most HCCs. The mediators of liver inflammation are mainly immune-related cells and the inflammatory factors they produce, which are abnormally enriched in the local inflammatory microenvironment. Viral infection can recruit macrophages (Kupffer cells), T cells, and other immune cells to the microenvironment, which have been reported to orchestrate the microenvironment for tumor initiation or progression. These immune cells can release proinflammatory factors, including cytokines (such as TNF-, IL-1, IL-6, etc.) and chemokines (such as CXCL8, CXCR4, etc.) (Gao et al., 2008; Karin, 2006; Karin et al., 2006; Lin and Karin, 2007; Mantovani et al., 2008), which can stimulate the transformation of hepatocytes to acquiring tumor-cell features such as self-sufficiency in growth, insensitivity to growth-inhibitory effects, evasion of programmed cell death, limitless replicative potential, sustained angiogenesis, and tissue invasion as well as metastasis (Hanahan and Weinberg, 2000). Notably, there exists an intriguing interplay between target hepatocytes and inflammatory cells within the tumor-inducing microenvironment (Mantovani et al., 2008). The protumorigenic factors released by the immune cells can activate several transcriptional activators within the hepatocytes, including nuclear factor-B (NF-B), signal transducer and activator of transcription 3 (STAT3), and hypoxia-inducible factor 1, among others. Activation of these transcription factors not only can stimulate the tumorigenic activity of hepatocytes but also can lead to the production of more inflammatory mediators, which further recruit and activate immune cells in the liver tissues (Gao et al., 2008; Karin, 2006; Karin et al., 2006; Lin and Karin, 2007; Mantovani et al., 2008). Such an amplification loop establishes a cancer-prone inflammatory microenvironment in the liver. Previous studies on genetic manipulation of mice confirmed the central role of NF-B in tipping the interplay between target liver cells and immune

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cells (Karin et al., 2006; Sun and Karin, 2008). In the mdr2-deficient mouse model (a protein that functions in transporting phospholipids from hepatocytes into bile), spontaneous hepatic inflammation develops, followed by the occurrence of HCC. The activation of NF-B by TNF- in hepatocytes seems to be essential in the late stage of tumor promotion (Pikarsky et al., 2004). In another mouse HCC model induced by the administration of the hepatotoxic mutagen diethyl nitrosamine (DEN), inactivation of the NF-B pathway by knockout of I-appa-B kinase-beta (IKK) in hepatocytes and myeloid cells significantly reduced the liver injury, inflammation, and subsequent HCC development (Maeda et al., 2005). In this model, perturbation of IL-6 released from Kupffer cells and regulated by MyD88 (TLRadaptor)-mediated activation of NF-B is demonstrated to be the mechanism responsible for the diminution of hepatocarcinogenesis (Naugler et al., 2007). IL-1 released from necrotic hepatocytes was further identified as another key factor in initiating the induction and release of IL-6 from Kupffer cells (Sakurai et al., 2008). Therefore, the current lines of evidence from animal models indicate that activation of NF-B in both inflammatory cells and hepatocytes may contribute to inflammation-related hepatocarcinogenesis. It is known that NF-B activation in inflammatory cells can stimulate the release of cytokines such as IL-6 or TNF- and activate both growth-stimulating and apoptosis-suppressing pathways of hepatocytes (Fig. 7). Notably, in addition to activating NF-B, the intriguing link between TNF--induced IKK kinase activity and activation of the mammalian target of rapamycin (mTOR) pathway has been increasingly recognized. IKK kinase can phosphorylate the TSC1 protein, resulting in the suppression of TSC1–TSC2 complex activity and activation of mTOR (Lee et al., 2007) (Fig. 7). Such activation has been found to enhance angiogenesis, which is another important pathway involving inflammation-mediated carcinogenesis.

B. Specific HBV Proteins Associated with Hepatocarcinogenesis In addition to persistent chronic inflammation with subsequent regeneration and accumulation of carcinogenic events in hepatocytes, HBV itself also encodes possible oncogenic viral proteins that may directly contribute to hepatocarcinogenesis. Among hepatitis B viral proteins, HBx protein functions as a multifunctional regulator modulating gene transcription, cell responses to genotoxic stress, protein degradation, apoptosis, and several signaling pathways (Bouchard and Schneider, 2004; Tan et al., 2008). The role of HBx protein

49

Recent Advances in the Research of Hepatitis B Inflammatory response

HBV/HCV infection (hepatocyte damage)

TLR IL-1 IL-1R MyD88

Kupffer cell p65

NF-kB

Cytokines and chemokines

p50

NF-kB binding site IL-6

TNF-a

(and others)

TNF-a

TNF-a IL-6 IL-1 IL-6Ra IL-1R

TNFR

gp130 IKKb

STAT

mTOR

NF-k B

Malignant transformation

Proliferation (expansion of mutations)

p65 p50

NF-k B binding site

Hepatocyte

Fig. 7 Chronic inflammation of the liver predisposing to hepatocarcinogenesis. The immune cells are recruited to liver tissues due to the virus infection and/or other liver damages, including macrophages, T cells, and others. Among them, the macrophages (Kupffer cells) activated through TLR or IL-1R activation release the cytokines and chemokines, which then stimulate the proliferation and transformation of hepatocytes. The activation of NF-B, both in the immune cells and in the hepatocytes, is critical for the interplay between hepatocytes and inflammatory cells within the tumor-inducing microenvironment. The details of this model are discussed in the text.

in malignant transformation of liver cells has been demonstrated in transgenic mouse models, especially in those with high-level HBx protein expression (Kim et al., 1991). Several possible mechanisms have been proposed. One major mechanism comes from its effects on antagonizing p53-dependent antitumor functions, including transcriptional activation and apoptosis (Arbuthnot et al., 2000; Elmore et al., 1997; Ogden et al., 2000). Concerning the effect of HBx protein on multiple cellular signaling pathways, HBx protein is shown to stimulate Ras/MEK/MAPK, JNK/JAK/STAT, and PI3K/Akt pathways to promote cell proliferation and antiapoptosis activity (Arbuthnot et al., 2000; Bouchard and Schneider, 2004). This activity does not require direct interaction between HBx protein and these protein kinases, and c-Src kinase, an upstream activator of these cytoplasmic signaling pathways, may be the key switch turning on these kinase signaling

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cascades (Arbuthnot et al., 2000; Bouchard and Schneider, 2004). HBx protein can activate c-Src indirectly by triggering the release of Ca2þ ions from the endoplasmic reticulum (ER) and mitochondria, which in turn activates the Ca2þ responsive Pyk2 kinase and leads to c-Src activation (Bouchard et al., 2001). In addition, HBx protein also affects a variety of cellular transcriptional factors and interacts with components of basal transcription machinery (ribosome-binding protein 5 and TATA-binding protein), transcriptional activator CREB/ATF, NF-B (Arbuthnot et al., 2000; Bouchard and Schneider, 2004) as well as androgen receptor (AR) (Chiu et al., 2007). Recently, an increasing list of HBx-responsive transcription factors and HBx-responsive transcription elements has been identified, including NF-B, NF-AT, AP-1, and the elements of HIV long terminal repeat and cyclic AMP response elements (Bouchard and Schneider, 2004). Some of these HBx-modulated cellular events not only directly contribute to hepatocarcinogenesis but also stimulate viral transcription and replication, which in turn leads to the increased risk of HBV-related HCC (Bouchard and Schneider, 2004; Keasler et al., 2007; McClain et al., 2007). Apart from HBx protein, frequent deletions at the pre-S region of HBV genome have been identified to be associated with progressive liver diseases (Chen et al., 2006c). A recent report showed that mice carrying transgenes of HBV with a replication competent pre-S deletion mutant develop liver cancer (> 90%), supporting the carcinogenic potential of this pre-S mutant (personal communication with the late Professor Benedict Yen at UCSF). Although the resulting truncated HBsAg has been proposed to accumulate in the ER and induce ER stress (Hsieh et al., 2004), its contribution to hepatocarcinogenesis awaits further clarification.

VII. GENETIC VARIATIONS AND HCC: VIRUS AND HOST PERSPECTIVES A. Viral Genetic Variations As discussed earlier, several HBV genotypes and common mutants are shown to have an increased risk of HCC development. Presumably, the specific HBV genetic variants associated with HCC risk are those selected out during the chronic inflammation process. Although the molecular mechanisms for hepatocarcinogenesis in these HBV genetic variations are not yet identified, these viral variants might have differential contributions to the inflammation and tumorigenesis in hepatocytes, such as enhancing cell proliferation activity or suppressing apoptosis activity (Fig. 8).

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Viral HBV/HCV/HDV

Non viral Alcohol/NASH/ enviromental factors Acute hepatitis

Chronic inflammation

Chronic hepatitis Cycles of virus selection: New viral variants

Cycles of excessive hepatocyte deaths and regenerations

Cirrhosis/nodules

Wild type or variant viral proteins

Selecting cells (clones) with growth/survival advantages

HCC

Selecting clones with invasiveness/angiogenesis

Fig. 8 A proposed model for the selection of viral mutants and hepatocyte genetic changes associated with hepatocarcinogenesis. Either viral or nonviral induced chronic inflammation will cause the excessive death and regeneration of hepatocytes. In the process, the virus with variations contributing to the growth advantage of hepatocytes will be selected. Meanwhile, the hepatocytes with genetic aberrations acquiring tumor-cell features such as self-sufficiency in growth, insensitivity to growth-inhibitory effects, evasion of programmed cell death, limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis will be selected and result in HCC. The details of this proposed model are discussed in the text.

B. Host Genetic Factors Not only viral variants associated with HCC could be selected out during the chronic inflammation process, hepatocytes with genetic aberrations conferring growth or survival advantages will also be selected out for clonal expansion at the stage of liver cirrhosis, forming cirrhotic nodules with monoclonal characteristics (Yeh et al., 2001). Further genetic aberrations in these cells within such precancerous lesions might confer the capabilities of tumor cells, such as invasiveness, limitless replication, or angiogenesis, and so on, allowing them to become malignant with invasive and metastatic characteristics (Fig. 8). Due to multistep characteristic of cancer development, it is generally believed that on average six to seven successive mutations are required to

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convert a normal hepatocyte into an invasive HCC cell (Beckman and Loeb, 2006; Hanahan and Weinberg, 2000). As every mutation contributes to the formation of an expanded clone and thus presents a larger target population of cells for the next mutation, the cells surrounding the first identified HCC actually retain great potential for becoming the next HCC. In particular, many tumor-prone mutations, namely the mutator mutations, affect the stability of the entire genome (Beckman and Loeb, 2006; Loeb et al., 2008), greatly increasing the overall mutation rate and thus accelerating the accumulation of random mutations, including those in oncogenes and tumor suppressor genes. The precancerous cells around the initial HCC are thus prone to become malignant soon after the occurrence of the first tumor. Indeed, the results from a recent microarray analysis indicated that the recurrence, metastases, or survival of HCC patients after surgery are mainly dependent on the gene expression patterns at the nontumorous liver tissues surrounding HCC rather than HCC itself (Budhu et al., 2006; Hoshida et al., 2008). These data support that nontumorous liver surrounding HCC is a fertile ground for HCC occurrence and thus is the critical determinant to predict recurrent HCC (Fig. 8).

C. Candidate Genes with Somatic Mutations or with Aberrant Expression Patterns The most direct genetic evidence supporting the involvement of specific genes in carcinogenesis comes from the observation of frequent somatic changes that presumably are selected during tumorigenesis. In HCC, frequent somatic mutations have only been identified in several genes including p53 ( 20%), -catenin ( 30%), Axin-1 ( 10%), phosphatase and tensin homolog (PTEN) ( 10%), and Smad 2/4 ( 10%) (Aravalli et al., 2008; Chen and Chen, 1999; Tan et al., 2008; Villanueva et al., 2007). The occurrence of p53 and -catenin mutations shows intriguing viral etiology-associated mutation patterns. Most p53 mutations belong to the missense type of somatic mutations. A hot spot p53 mutation at codon 249, with a selective arginine-to-serine substitution (caused by G–T transversion), was identified to be strongly associated with exposure to AFB1 in combination with chronic HBV infection (Bressac et al., 1991). In addition to AFB1, p53 mutations are identified to be independently associated with another etiologic factor, the viral etiology, showing a tendency to occur more frequently in HBV-related HCC than HCV-related HCC, and are also associated with the genomic instability type of HCC (Laurent-Puig et al., 2001). Thus, p53 might play an important role as the checkpoint for the HBVspecific carcinogenic mechanisms. The cocarcinogenic effects of p53 R249S

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and AFB1 in HBV-related HCC have been well documented in a transgenic mouse model (Ghebranious and Sell, 1998). Concerning -catenin mutations, either missense mutations or interstitial deletions, occur predominantly at the N-terminus (at exon 3) and belong to the dominant Wnt-signaling activation mutants (Giles et al., 2003). These mutations cause the loss of phosphorylation consensus sites critical for negative regulation by the GSK3/APC/axin regulatory complex and the resulting effect is the nuclear accumulation of aberrant -catenin proteins, which subsequently complex with T-cell factor/lymphoid enhancing factor transcription factors to activate the transcription of specific genes (Giles et al., 2003). Some of these genes have been proven to be critical to hepatocarcinogenesis, such as cyclin D1, c-myc, and E-cadherin. In contrast to p53, oncogenic -catenin mutations were scarcely identified in HBV-related HCC (Laurent-Puig et al., 2001) and were identified mainly in HCV-related HCC (Huang et al., 1999). A plausible explanation is that either Wnt-signaling activity may specifically participate in HCV-related carcinogenic processes or HBV infection might alternatively confer a mechanism to activate -catenin signaling and bypass the need for the activating mutations. Recent data favored the latter hypothesis, showing that HBx protein can activate the -catenin signaling in HCC cells through inhibiting the c-Src/GSK3-dependent signaling pathway (Cha et al., 2004). An even more detailed molecular mechanism was further demonstrated, showing that HBx protein can activate -catenin through activating Erk kinase, which primes GSK-3 for its subsequent phosphorylation at Ser9 by p90RSK and results in the inactivation of GSK-3 and upregulation of -catenin (Ding et al., 2005). The occasional mutation of Axin-1 (in 10% of HCC), an important regulator of -catenin, further strengthens the importance of the -catenin signaling pathway in hepatocarcinogenesis. In the animal model, although the activating mutant of -catenin is not sufficient to induce HCC, it could accelerate tumor formation in cooperation with other oncogenic factors such as activated Ha-Ras (Harada et al., 2004). In addition to these somatic mutations, several genes show aberrant expression patterns without somatic mutation during hepatocarcinogenesis. For example, several proto-oncogenes are found to be overexpressed or activated in human HCCs, including c-myc, cyclin D1, and the Ras family of genes (Aravalli et al., 2008; Chen and Chen, 1999; Tan et al., 2008; Villanueva et al., 2007). The increased HCC risk attributed to overexpression (or overactivation) of these genes has been demonstrated in transgenic mice models (Deane et al., 2001; Pascale et al., 1993; Sargent et al., 1996). In contrast to the activation of oncogenes, genes significantly downregulated in HCC are much more common. The major ones include P16 (INK4A), p14 (ARF), E-cadherin, SOCS-1, DLC-1, APC, RASSF1A, GSTP1, FAP-1, and IGFBP, among others (Aravalli et al., 2008; Chen and Chen, 1999; Tan et al.,

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2008; Villanueva et al., 2007). Some of the genes significantly downregulated in HCC locate at chromosome regions with frequent allelic loss, such as APC on 5q, p16 on 9p, Rb on 13q, E-cadherin on 16q, PTEN on 10q, and FAP-1 on 4q. Therefore, allelic loss is recognized as one possible mechanism for the downregulated expression of these genes. Of note, epigenetic change, the hypermethylation of the DNA promoter regions, might also contribute to the downregulation of gene expression, which may occur in the early precancerous stage of liver cirrhosis (Calvisi et al., 2007; Tischoff and Tannapfe, 2008). This phenomenon has been shown in a variety of genes, including P16 (INK4A), p14 (ARF), E-cadherin, CASP7, TMS1, TIMP3, hMLH1, hMSH2, hMSH3, FAP-1, SOCS-1, SOC3, RASSF1A, GSTP1, SEMA3B, and others (Calvisi et al., 2007; Tischoff and Tannapfe, 2008). Many of these genes function as putative tumor suppressor genes, fitting with the concept of two-hit hypothesis for the knockdown of putative functions (Matsumura et al., 2001; Yeh et al., 2006). Recently, the gene involved in DNA methylation, the glycine N-methyltransferase (GNMT) gene, was reported to be downregulated in HCC. The evidence from knockout mice suggests that GNMT deficiency might contribute to glycogen storage disease in the liver, which might predispose to HCC (Liu et al., 2007). Whether DNA methylation of tumor suppressor genes contributes to HBV-related HCC remains largely unknown and deserves further studies.

D. Genome-Wide Analysis of Genetic Aberrations and Gene Expression Patterns The association of p53 and -catenin mutations with distinct types of HCC elicited by different hepatitis viruses suggests that different carcinogenic pathways might cause distinct group of HCC. Presumably, the patterns of genetic aberrations may help molecular classification of HCC into distinct subgroups. This hypothesis has been supported by the results from comprehensive analysis of genetic aberrations in human HCC and in HCC derived from transgenic mouse models. By genome-wide comparative genomic hybridization and allelotyping analyses, in accordance with the mutation detection of p53, Axin-1, and -catenin genes, human HCC can be clearly divided into two groups according to the patterns of genetic alterations (or chromosome stability status) (Jou et al., 2004; Laurent-Puig et al., 2001). One group is the chromosome stable type, with few genetic aberrations, and the other the chromosome unstable type, with frequent genomic aberrations. -catenin mutations occurred predominantly in the first group of HCC and p53 mutations frequently occurred in the second group of

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HCC, which are usually HBV-related HCC and frequently had concomitant allelic losses on chromosomes 1p, 4q, 6q, 9p, 13q, 16p, 16q, and 17p (Jou et al., 2004; Laurent-Puig et al., 2001). Analysis of genetic aberrations in the transgenic models of HCC also categorized liver tumors into two distinct groups, genome stable versus the genome unstable types. Tumors derived from the c-myc/transforming growth factor- transgenic mouse displayed extensive genomic instability with recurrent loss of heterozygosity at many chromosomes and a low rate of -catenin activation. In contrast, the c-myc/E2F-1 tumors showed a high frequency of -catenin activation in a relatively stable genome (Calvisi et al., 2004). Theoretically, these mouse experimental models could help elucidate the molecular basis of human HCC. Other than genome-wide analysis for genetic aberrations, overall gene expression of HCC could be characterized by using genome-wide transcriptomic microarray analysis. Recent studies identified the up- and downregulated expression of hundreds of genes in human HCC (Midorikawa et al., 2007; Minagawa et al., 2008; Thorgeirsson et al., 2006). The complete list of genes with altered expression could help us to better understand the biological behavior of individual HCC. In brief summary, HCC tissues reveal the increased expression of cell proliferation and mitosis-associated genes and decreased expression of liver-specific genes, suggesting that accelerated cell proliferation and dedifferentiation of cancer cells indeed occur in the carcinogenic process. Moreover, the comparisons of expression profiles between HCC tissues with different etiologic and clinicopathologic features further delineate specific genes involved in different carcinogenic processes, despite discrepant results exist among different studies. The newly developed proteomics technology may resolve this discrepancy through looking at protein profiles of HCC (Teramoto et al., 2008; Zinkin et al., 2008). Recently, microRNAs (miRNAs) have been recognized as host genetic factors regulating the carcinogenic processes of HCC (Ladeiro et al., 2008). In analyzing miRNA expression profiles of paired HCC and adjacent nontumorous tissues, numerous miRNAs were found to have abnormal expression patterns (Aravalli et al., 2008; Jiang et al., 2008; Ladeiro et al., 2008). Some miRNAs even showed differential expression patterns stratified by viral etiologic factors, gender factors, metastasis status, and specific genetic aberrations (Budhu et al., 2008; Jiang et al., 2008; Ladeiro et al., 2008; Liu et al., 2009b), suggesting their various roles in different hepatocarcinogenic processes. The functional roles of some miRNAs in targeting specific oncogenes or tumor suppressor genes are increasingly identified. For example, the let-7 family miRNAs are targeting at the Ras oncogene, miR21 at the PTEN tumor suppressor gene, miR-18a at the estrogen receptor alfa (ER), miR-223 at the Stathmin1 gene, and miR-122 at the cyclin G1 cell-cycle regulator (Aravalli et al., 2008).

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E. Deregulation of Cellular Pathways By using the genome-wide analysis of HCC, the profiles of genetic changes, protein coding genes, miRNA expression patterns, and even the proteomic data are publicly accessible and starting to be integrated (Su et al., 2007). The integration of data can help identify novel critical oncogenes as well as tumor suppressor genes at the specific genomic regions with recurrent genomic amplification or allelic loss in HCC. Further classification of HCC into distinct subgroups not only can help predict the prognosis of each subgroup of patients but also have potential implications to design corresponding molecular-targeted agents. This possibility has been preliminarily proven in a recent study (Boyault et al., 2007). HCCs were classified into six subgroups based on genetic and transcriptomic alterations, and two groups had specific alterations associated with AKT oncogenic pathway, predicting the potential efficacy of targeted therapy with AKT kinase inhibitors. Currently, the integration of genome-wide studies has delineated several signaling pathways that might be critical in hepatocarcinogenesis (Aravalli et al., 2008; Llovet and Bruix, 2008; Villanueva et al., 2007). The first is the activation of the Wnt-signaling pathway. The frequent mutations of -catenin and Axin-1 and the downregulation of two negative regulatory proteins, adenomatous polyposis coli and E-cadherin, pointed out the activated Wnt-signaling pathway as important in HCC. In support of this, two transcriptional targets of this pathway, c-myc and cyclin D1, were reported to be overexpressed in a significant proportion of HCC (Chen and Chen, 1999; Tan et al., 2008). Up to 60% of HCCs were shown to display such dysregulation of the Wnt-signaling pathway (Aravalli et al., 2008; Villanueva et al., 2007). The second pathway is the dysregulation of the G1/S cell-cycle transition. Frequent aberrations of the genes involved in the regulation of G1/S transition of the cell cycle, including p53, INK4a (p16), ARF (p14), Rb, and cyclin D1, have been identified (Aravalli et al., 2008; Villanueva et al., 2007). The signals that drive cells into the S phase converge at the regulation of CDK2 activity (Malumbres and Barbacid, 2001). Except for cyclin D1, the overexpression of which might be contributed to by Wnt pathway activation, the other genes all play negative regulatory roles in CDK2 activation and all have shown loss-offunction aberrations in HCC. Overexpression of cyclin D1, together with CDK4/6, will increase phosphorylation of the Rb gene, resulting in the release of E2F, which also leads to the activation of CDK2 (Malumbres and Barbacid, 2001). Therefore, all these aberrations frequently detected in HCC contribute to acceleration of the G1/S transition in more than 80% of HCC (Malumbres and Barbacid, 2001; Tannapfel et al., 2001). The third pathway involved in carcinogenesis is the insulin-like growth factor (IGF) signaling pathway, with supportive evidence from animal studies (Schirmacher et al., 1992). The genes of the IGF axis with frequent aberrations

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in HCC include the overexpression of IGF-I (–II) and the corresponding receptor, the reduction of IGFBP expression and increased proteolytic cleavage of IGFBPs, which leads to an excess of bioactive IGFs, and defective function of the IGF-II/mannose 6-phosphate receptor involved in degradation of IGF-II, which may further potentiate the mitogenic effects of IGFs in the development of HCC (Aravalli et al., 2008; Villanueva et al., 2007). Moreover, PTEN was demonstrated to significantly lower IGF and IGF-IR expression and thus its deletion might contribute to tumorigenesis through affecting this carcinogenic axis (Aravalli et al., 2008; Villanueva et al., 2007). The fourth pathway involves the Ras/MAPK signaling pathway. Although mutations of the Ras family of genes in HCC are rare, overexpression of Ras proteins has been identified in a significant proportion of HCCs and cirrhotic livers (Aravalli et al., 2008; Villanueva et al., 2007). Moreover, downregulation of inhibitors of the Ras pathways has also been frequently detected in HCC, including the Raf-1 kinase inhibitory protein and Spred-1 (Aravalli et al., 2008; Villanueva et al., 2007). Activation of Ras can facilitate the binding of RAF and then transmit the signals to the downstream MEK/ERK pathways. Mediated through different downstream effectors, activation of Ras can regulate both cell proliferation and apoptosis. The proapoptotic effect of Ras is dependent on its downstream RAS-association domain family (RASSF1) effector. When RASSF1 interacts with activated Ras, it can activate the mammalian sterile 20-like kinase-1 and induce apoptosis (Guo et al., 2007). The frequent epigenetic silencing of RASSF1A identified in HCCs suggests that this proapoptotic activity is turned off in HCCs, thus promoting the tumorigenic effects of Ras pathway activation (Calvisi et al., 2007; Tischoff and Tannapfe, 2008). Several other pathways have also been demonstrated to be involved in hepatocarcinogenesis, including the mTOR pathway (Villanueva et al., 2008), the c-met pathway (Bressac et al., 1991), the JAK/STAT pathway (Calvisi et al., 2006), the Hedgehog pathway (Osipo and Miele, 2006), and telomerase reverse transcriptase activation, among others (Llovet and Bruix, 2008). These pathways allow the identification of potential targets for the molecular-targeted therapies in HCC. Accordingly, sorafenib, a multikinase inhibitor, has been approved for the treatment of advanced HCC in many countries (Llovet and Bruix, 2008). To overcome the complexity of the possible cross talk between different signaling pathways, combination therapies with different molecular target agents should be the next clinical strategy.

F. Single Nucleotide Polymorphism (SNP) Analysis With the aid of single nucleotide polymorphism (SNP) database, the association of specific host genes with the risk of HCC has been evaluated. The results indicated three types of genes may have a significant association

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with HCC risk. The first group is related to host immune responses, including IL-10 (Shin et al., 2003; Tseng et al., 2006), TGF-1 (Migita et al., 2005), and IL-1 (Wang et al., 2003). The second group consists of enzymes involved in the metabolic activation/detoxification of carcinogens of the liver, including UGT1A7 (Vogel et al., 2001), N-acetyltransferase (Yu et al., 2000b), and P450 2E1 (CYP2E1) (Yu et al., 1995). The third group is related to hormone-related factors, such as cytochrome P450c17 (CYP17), steroid 5 -reductase type II (SRD5A2), and the AR (Yu et al., 2001). Finally, other genes are also found to affect the carcinogenic process, including XRCC1 (Yu et al., 2003a), cyclin D1 (Zhang et al., 2002), FAP-1 (Yeh et al., 2006), and GNMT (Tseng et al., 2003). However, further studies are required to clarify how these specific genetic variations influence gene function and increase the susceptibility for the development of HCC.

G. Gender Disparity in HBV-Related HCC One intriguing feature of HBV-related HCC is the male dominance. The male to female ratio is 3–7:1 for HBV-related HCC and 1.5–3:1 for HCV-related HCC (Lee et al., 1999; Shiratori et al., 1995). On the basis of epidemiologic studies, the male predominance is proposed to be related to testosterone and AR activities. Human studies showed that elevated testosterone levels and the presence of genetic polymorphisms linked to increased androgen activity are significantly associated with an increased risk of HCC in male HBsAg carriers (Yu et al., 2000c; Yu et al., 2001). In rodent HCC models, castration or treatment with antiandrogen agents can protect male rodents from tumor development (Toh, 1981). Therefore, upregulation of the androgen pathway in male patients is considered to accelerate liver carcinogenesis. Another line of evidence from the epidemiologic study of the gender disparity of HCC is the increased activity of estrogens in female patients, which might protect them from hepatocarcinogenesis. The risk of HCC in females is shown to be inversely related to the age at menopause and to the number of full-term pregnancies (Yu et al., 2003b). In addition, early oophorectomy (at age 50) was identified as a risk factor for HCC in females, whereas postmenopausal hormone replacement therapy was shown to be a protective factor (Yu et al., 2003b). This is consistent with animal studies, in which ovariectomy increases the susceptibility to HCC in female mice (Nakatani et al., 2001; Vesselinovitch et al., 1980). In addition, our recent animal study further delineated two novel molecular mechanisms that may explain the higher androgen activity identified in male HCC patients and the lower estrogen activity identified in female HCC patients (Chiu et al., 2007). First, the higher activity of androgen in male HCC cases might come from an intriguing interaction between the HBx protein and the androgen-signaling pathway, in which HBx can stimulate

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the transcriptional activity of AR in a ligand-dependent manner, mainly through its activation of the c-Src kinase pathway (Yang et al., 2009). The identification of androgen response elements in the enhancer I of HBV further provided another mechanism of male predominance in chronic hepatitis B and the increased risk of HCC (Wang et al., 2009). Second, we recently found that the lower activity of estrogens in female HCC cases might be attributed to a novel miRNA-mediated regulatory mechanism (Liu et al., 2009b). miR-18a, which is preferentially elevated in female HCC patients, was identified to target the ESR1 gene, which encodes the ER. Moreover, overexpression of miR-18a was found to decrease ER but stimulate the proliferation of HCC cells. Therefore, suppression of the estrogen pathway by overexpression of miR-18a was shown to be a novel mechanism underlying the protective role of estrogens in hepatocarcinogenesis in female subjects (Liu et al., 2009b). In addition to the protective role of estrogen signaling mediated through ER in the hepatocytes of female HCC cases, the protective role of estrogens against HCC in females can also be mediated through regulating cytokine release from Kupffer cells in the liver. It has been demonstrated that estrogens could protect hepatocytes from malignant transformation through downregulation of the secretion of IL-6 from Kupffer cells, a critical process in the DEN-induced HCC mouse model (Naugler et al., 2007). Therefore, the gender disparity in HCC can be attributed to differential androgen and estrogen sex hormone factors in each gender. The higher activity of androgen pathway functions as a tumor-promoting factor in male hepatocarcinogenesis, whereas the higher activity of estrogen pathway functions as a tumor-suppressing factor in female hepatocarcinogenesis. As both mechanisms work in a ligand-dependent manner, both ligand and receptor of these sex hormones need to be included for the assessment of relative HCC risk in each gender.

H. Identification of HCC Predisposition Gene(s) in Familial Multiplex HCC The paradigm of liver cancer as a consequence of aberrations of the genome of somatic cells has been firmly established. However, there remains a problem in identifying the disease-causing gene(s) instead of the peripheral gene aberrations secondary to the initiative events from a great number of aberrations. One solution for this issue is to search for the gene(s) predisposing familial HCC. Genome-wide association study (GWAS) is widely used for a comprehensive analysis of the association between host genetic characteristics and disease phenotypes. The applications of GWAS to map disease-association SNPs or genes have already gleaned many positive

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results, covering eye diseases, cardiovascular and metabolic diseases, and cancers (McCarthy et al., 2008). It does not only identify the disease-associated genes but also points out new, previously unknown, mechanisms, such as the roles of IL23 and Th17 in inflammatory bowel diseases (Cho, 2008; Duerr et al., 2006). Therefore, it is logical to apply GWAS to HCC research to discover new genes or new mechanisms. However, due to the inherent heterogeneous nature of HCC, the GWAS analysis needs to be conducted in a more homogenous and well-defined group of HCC patients with a hereditary trait. Fortunately, a familial predisposition has been known as a risk factor for HCC in HBV carriers. HBV carriers with a family history of HCC have a multivariate-adjusted risk ratio for HCC of 2.41 compared with HBV carriers without a family history of HCC. Strikingly, for HBsAg carriers with two or more affected relatives, the ratio increased to 5.55 (95% CI 2.02–15.26) (Yu et al., 2000a). Preliminary segregation analysis consistently suggested that HBV-related HCC from the multiplex family may contain a major susceptibility gene to HCC (Cai et al., 2003). The relatively homogenous nature of these HCC patients is thus appropriate for GWAS analysis to identify novel genes implicated in the development of familial HCC.

VII. CONCLUSIONS HCC is one of the most common cancers worldwide, and more than half of the patients are attributable to persistent HBV infection. The best and cheapest way to prevent HBV-related HCC is the implementation of universal hepatitis B vaccination, by which the incidence rates of childhood HCC have been reduced in several countries including Taiwan. However, worldwide there are still hundreds of millions of HBV carriers that remain a global health challenge. In the past decade, several hepatitis B viral factors such as serum HBV DNA level, genotype, and naturally occurring mutants that influence liver disease progression and HCC development in HBV carriers have been identified. In addition, the role of nonviral factors in HBV-related HCC has also been increasingly recognized. On the basis of these emerging data, it is recommended that HBV carriers should be screened and monitored with relevant virologic assays to identify those who have a higher risk of liver disease progression, and who are candidates for antiviral treatments. On the other hand, despite decades of effort in elucidating the genetic and cellular changes in HCC, the results are still far from satisfactory. Genetic and genomic classification of HCC, in comparison with colorectal and lung cancer, has not reached significant clinical usefulness yet. Research on the

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cell biology of HCC has made some progress, but has not yet discovered the aberrant pathways involved in maintaining HCC phenotypes. In the future, the new platforms of high-density oligonucleotide arrays, gene expression analysis with next-generation sequencing technologies and nonbiased protein coding gene sequencing should be used to analyze the genomes of human HCC and further identify novel target genes or pathways critically involved in hepatocarcinogenesis.

ACKNOWLEDGMENTS This work was supported by grants from the Department of Health and the National Science Council, Executive Yuan, Taiwan.

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The ATM–Chk2 and ATR–Chk1 Pathways in DNA Damage Signaling and Cancer Joanne Smith,* Lye Mun Tho,*,{ Naihan Xu,* and David A. Gillespie*,{ *Beatson Institute for Cancer Research, Garscube Estate, Glasgow, UK Faculty of Medicine, University of Glasgow, Glasgow, UK

{

I. II. III. IV. V. VI. VII. VIII. IX.

Introduction Activation of the ATM–Chk2 and ATR–Chk1 DNA Pathways Checkpoint Functions of the ATM–Chk2 and ATR–Chk1 Pathways The Three Rs of Damage Signaling: Resection, Recombination, and Repair ATM–Chk2 and ATR–Chk1 Pathway Alterations in Cancer Exploiting Homologous Recombinational Repair (HRR) Defects for Cancer Therapy DNA Damage Signaling as a Barrier to Tumorigenesis Checkpoint Suppression as a Therapeutic Principle Future Perspectives References

DNA damage is a key factor both in the evolution and treatment of cancer. Genomic instability is a common feature of cancer cells, fuelling accumulation of oncogenic mutations, while radiation and diverse genotoxic agents remain important, if imperfect, therapeutic modalities. Cellular responses to DNA damage are coordinated primarily by two distinct kinase signaling cascades, the ATM–Chk2 and ATR–Chk1 pathways, which are activated by DNA double-strand breaks (DSBs) and single-stranded DNA respectively. Historically, these pathways were thought to act in parallel with overlapping functions; however, more recently it has become apparent that their relationship is more complex. In response to DSBs, ATM is required both for ATR–Chk1 activation and to initiate DNA repair via homologous recombination (HRR) by promoting formation of single-stranded DNA at sites of damage through nucleolytic resection. Interestingly, cells and organisms survive with mutations in ATM or other components required for HRR, such as BRCA1 and BRCA2, but at the cost of genomic instability and cancer predisposition. By contrast, the ATR–Chk1 pathway is the principal direct effector of the DNA damage and replication checkpoints and, as such, is essential for the survival of many, although not all, cell types. Remarkably, deficiency for HRR in BRCA1- and BRCA2-deficient tumors confers sensitivity to cisplatin and inhibitors of poly(ADP-ribose) polymerase (PARP), an enzyme required for repair of endogenous DNA damage. In addition, suppressing DNA damage and replication checkpoint responses by inhibiting Chk1 can enhance tumor cell killing by diverse genotoxic agents. Here, we review current understanding of the organization and functions of the ATM–Chk2 and ATR–Chk1 pathways and the prospects for targeting DNA damage signaling processes for therapeutic purposes. # 2010 Elsevier Inc.

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0065-230X/10 $35.00 DOI: 10.1016/S0065-230X(10)08002-4

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I. INTRODUCTION Cells in multicellular organisms are continuously exposed to DNA damage arising from a variety of endogenous and exogenous sources. These include reactive oxygen species, ultraviolet light, background radiation, and environmental mutagens. To protect their genomes from this assault, cells have evolved complex mechanisms, collectively referred to as DNA damage responses, that act to rectify damage and minimize the probability of lethal or permanent genetic damage. The cellular response to DNA damage encompasses multiple repair mechanisms and checkpoint responses that can delay cell cycle progression or modulate DNA replication. Collectively, these processes are essential to maintain genome stability. DNA damage responses are orchestrated by multiple signal transduction processes, key among which are the ATM–Chk2 and ATR–Chk1 pathways. Activation of these pathways is crucial for the proper coordination of checkpoint and DNA repair processes; however, they can also modulate other biological outcomes such as apoptosis or cell senescence. In recent years, it has become evident that DNA damage responses are central both for the evolution and therapy of cancer. Inherited defects in DNA damage responses predispose to cancer by enhancing the accumulation of oncogenic mutations, while genome instability is also common in sporadic cancers. More recently, it has become apparent that oncogenic mutations elicit spontaneous DNA damage that can suppress the evolution of incipient cancer cells. Escape from this tumor suppressive barrier may be a major factor in selecting for additional genetic changes during tumor progression such as mutation of the p53 tumor suppressor, the most frequent alteration in human cancer. Conversely, radiation and genotoxic chemotherapies remain a mainstay of conventional cancer treatment and are likely to remain so for the foreseeable future. Such therapies are, however, imperfect and can incur severe side effects. As a result, much current interest is focused on understanding how normal and tumor cells respond to DNA damage and determining whether DNA damage responses could be exploited or manipulated for therapeutic purposes. Two concepts in particular have attracted attention in recent years. First, inherent defects in genome stability mechanisms, such as homologous recombination, can confer tumor sensitivity to specific genotoxic agents or inhibition of complementary repair pathways. Second, evidence suggests that pharmacological suppression of DNA damage or checkpoint responses can enhance the efficacy of conventional genotoxic agents. Although promising, a full understanding of the biology and functions of the DNA damage signaling pathways will be crucial for the future success of such approaches.

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II. ACTIVATION OF THE ATM–CHK2 AND ATR–CHK1 DNA PATHWAYS DNA damage responses are controlled by biochemical pathways whose principal components and general organization have been conserved from yeasts to humans (Rhind and Russell, 2000). In vertebrates, the two main signaling pathways activated by DNA damage consist of the ATM–Chk2 and ATR–Chk1 protein kinases (Sancar et al., 2004). ATM and ATR are large kinases with sequence similarity to lipid kinases of the phosphatidylinositol-3-kinase (PI3K) family, but which phosphorylate only protein substrates (Abraham, 2001). Key among these substrates are the serine– threonine checkpoint effector kinases, Chk1 and Chk2, which are selectively phosphorylated and activated by ATR and ATM respectively to trigger a wide range of distinct downstream responses (Bartek and Lukas, 2003). The ATM–Chk2 and ATR–Chk1 pathways respond to different aberrant DNA structures (Fig. 1); ATM is recruited to and activated primarily at DNA double-strand breaks (DSBs) in conjunction with the MRE11:RAD50: NBS1 (MRN) sensor complex (Lee and Paull, 2005; Suzuki et al., 1999), whereas ATR is activated via recruitment to tracts of single-stranded DNA (ssDNA) in association with its partner protein, ATRIP (Dart et al., 2004; Lupardus et al., 2002; Zou and Elledge, 2003). The basic mechanisms involved in ATM–Chk2 and ATR–Chk1 pathway activation have been elucidated in considerable detail. ATM and Chk2 are activated potently by radiation and genotoxins that induce DSBs, but only weakly, if at all, by agents that block DNA replication without inducing damage (Matsuoka et al., 2000). In undamaged cells, ATM is thought to exist as inactive homodimers. In response to DSBs, inactive ATM homodimers are rapidly induced to autophosphorylate in trans, resulting in dissociation to form partially active monomers (Bakkenist and Kastan, 2003). The exact nature of the primary activating signal that triggers ATM autophosphorylation remains unknown; however, it does not appear to be limited to the immediate vicinity of the damage and may be linked to long-range alterations in chromatin structure (Bakkenist and Kastan, 2003). Serine (S) S1981 was the first autophosphorylation site to be identified; however, this residue is not essential for ATM function, at least in mice (Pellegrini et al., 2006), although its modification is tightly linked to ATM activation under most circumstances (Bakkenist and Kastan, 2003). Subsequent studies have documented additional ATM autophosphorylation sites at S367 and S1893 that may contribute to the activation process, perhaps explaining why S1981 is individually nonessential, while acetylation mediated by the TIP60 acetyl-transferase may also play a role (Lavin and Kozlov, 2007).

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Fig. 1 Activation of the ATM–Chk2 and ATR–Chk1 pathways The ATM–Chk2 and ATR– Chk1 pathways are activated selectively by DSBs and tracts of ssDNA complexed with RPA respectively. Phosphorylation events are indicated by (P) in red, acetylation by (Ac) in yellow. DSBs in chromatin stimulate ATM autophosphorylation and acetylation but full activation also requires recruitment to sites of damage in conjunction with the MRN complex where ATM modifies multiple substrates including the downstream effector kinase, Chk2, leading to Chk2 activation and downstream signal transduction. ATR is recruited to tracts of ssDNA-RPA through its interacting partner, ATRIP, where it phosphorylates and activates Chk1 in conjunction with the TopBP1 and Claspin mediator proteins. Two additional mediator proteins, Timeless and Tipin, also contribute to ATR–Chk1 activation although their functions are less well-understood and omitted here for clarity. The ultimate result of ATM–Chk2 and ATR–Chk1 signaling is the activation of multiple DNA damage and replication checkpoint responses that are summarized in Fig. 2. Please refer to the text for further details and explanation.

ATM monomers are then recruited to DSBs via interactions with the MRN sensor complex (Lee and Paull, 2007), stimulating full activation and providing a platform that enables ATM to act locally on multiple substrates at the site of damage. Local substrates include the variant histone, H2AX, forming the DNA damage-associated -H2AX histone mark (FernandezCapetillo et al., 2004), the MRN complex itself (discussed in more detail below), the cohesin SMC1 (Kitagawa et al., 2004), and the downstream effector kinase Chk2 (Lukas et al., 2003). ATM phosphorylates Chk2 on a

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specific threonine (T) residue, T68, located within an N-terminal serine/threonine-glutamine (SQ/TQ)-rich motif (Ahn et al., 2000). Once phosphorylated, the SQ/TQ motif of one Chk2 molecule is recognized by the phosphopeptidebinding Fork-head associated (FHA) domain of another, leading to transient homodimerization, intermolecular activation loop autophosphorylation, and full activation (Ahn et al., 2002; Cai et al., 2009; Oliver et al., 2006). Once activated, Chk2 is thought to dissociate from sites of damage and disperse as a monomer throughout the nucleus to act on multiple substrates involved in cell cycle progression, apoptosis, and gene transcription (Lukas et al., 2003). Known substrates of Chk2 include the p53 tumor suppressor protein (Chehab et al., 2000; Shieh et al., 2000) and its regulator MDMX (Chen et al., 2005), Cdc25 family phosphatases (Blasina et al., 1999; Chaturvedi et al., 1999; Matsuoka et al., 1998), the BRCA1 tumor suppressor (Lee et al., 2000), and transcription factors such as FOXM1 (Tan et al., 2007) and E2F1 (Stevens et al., 2003). It is important to note that ATM also targets other substrates at sites of damage in addition to those mentioned above, including NBS1, BRCA1, MDC1, and p53BP1 among others (Lavin, 2008). In addition, ATM acts on other substrates which do not necessarily concentrate at sites of damage. For example, ATM plays an important role in activating the p53 response to DNA damage both by phosphorylating p53 itself and its stability regulators, MDM2 and MDMX (Chen et al., 2005; Lavin and Kozlov, 2007); however, this is considered to take place in the nucleoplasm rather than specifically at DSBs (Lavin, 2008). In addition, there is increasing evidence that ATM may also have substrates and functions in the cytoplasm (Lavin, 2008). By contrast, ATR–Chk1 signaling is activated most strongly when DNA replication is impeded, for example as a result of nucleotide depletion or replication-blocking DNA damage lesions such as those inflicted by ultraviolet (UV) light (Abraham, 2001). When replication is blocked, DNA polymerases become uncoupled from the replicative helicase (Byun et al., 2005), generating tracts of ssDNA that rapidly become coated with the trimeric ssDNA-binding protein complex, Replication Protein A (RPA). ATR is recruited to and activated at such tracts in association with its partner protein, ATRIP, which interacts directly with ssDNA complexed with RPA via the 70kD RPA1 subunit (Zou and Elledge, 2003). Replication fork stalling generates ssDNA directly; however, this structure can also arise through the action of nucleotide excision repair (NER) or at dysfunctional telomeres. In addition, it is important to note that the ATR– Chk1 pathway is also activated in response to DSBs when ssDNA is generated as a result of nucleolytic strand resection (discussed in more detail below). Conversely, replication of damaged DNA can result in DSBs when leading-strand DNA polymerases encounter single-strand nicks or abasic sites. As a result, the ATM–Chk2 and ATR–Chk1 pathways are frequently

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activated simultaneously in cells exposed to diverse genotoxic stresses, including ionizing radiation and most or all cytotoxic chemotherapy agents. Unlike ATM, there is currently no evidence that ATR activation involves autophosphorylation or indeed any other posttranslational modification (Abraham, 2001). Instead, efficient ATR activation and downstream phosphorylation of Chk1 depends on the actions of two mediator proteins, TopBP1 and Claspin. TopBP1, which is recruited to ssDNA-RPA via the PCNA-like RAD9: RAD1: HUS1 checkpoint clamp (Delacroix et al., 2007), contains a domain that stimulates ATR activity, although exactly how this occurs is unclear (Kumagai et al., 2006; Mordes et al., 2008). A second mediator, Claspin, which likely associates with active replication forks during normal replication (Lee et al., 2003), is then subject to ATR-dependent phosphorylation within a short, repeated motif which, once modified, binds Chk1 and serves as a platform for ATR-mediated phosphorylation and activation (Guo et al., 2000). Phosphorylation within the Claspin Chk1-binding motifs depends on ATR kinase activity (Kumagai and Dunphy, 2003); however, the modified residues do not occur within consensus SQ/TQ) ATR target sites. So far the kinase directly responsible for this final crucial step in Chk1 activation has not been unambiguously identified; proposed candidates include ATR, Chk1 itself, and Cdc7 (Bennett et al., 2008; Chini and Chen, 2006; Kim et al., 2008). Interestingly, recent studies have also revealed a requirement for two additional mediators, Timeless and Tipin (Timeless-interacting protein), both for normal replication and for ATR–Chk1 activation in response to replication stress (Kondratov and Antoch, 2007). Timeless binds to both ATR and Chk1 whereas Tipin can interact with Claspin (Kemp et al., 2010). Recent data indicate that like ATRIP, Tipin binds to a specific subunit of the RPA complex (although RPA2 rather than RPA1) and is required for stable association of both Timeless and Claspin with tracts of ssDNA-RPA (Kemp et al., 2010). In addition to checkpoint activation, Timeless and Tipin also seem to be required for replication fork stabilization and restart (Errico et al., 2007). Interestingly, the Drosophila homologue of Timeless is a circadian rhythm regulator, although whether this function is also conserved in mammals is less clear (Kondratov and Antoch, 2007). Phosphorylated Claspin then recruits Chk1 (Jeong et al., 2003) to ssDNARPA complexes, bringing it into close proximity with active ATR (Kumagai and Dunphy, 2003) and enabling ATR to phosphorylate Chk1 directly at multiple S/T-Q sites within the C-terminal regulatory domain, most notably at serines (S) S317 and S345, which are widely monitored as surrogate markers of activation. Phosphorylation of these sites, and in particular serine (S) S345, is essential for Chk1 biological activity, although exactly how these modifications regulate Chk1 catalytic remains poorly understood (Niida et al., 2007; Walker et al., 2009). ATR-mediated phosphorylation is reported to stimulate Chk1 kinase activity by relieving inhibition by the

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C-terminal regulatory domain (Oe et al., 2001; Walker et al., 2009); however, it may also promote release of Chk1 from chromatin (Smits et al., 2006). Chk1 also undergoes autophosphorylation during activation (Kumagai et al., 2004); however, this does not occur within the activation loop (Chen et al., 2000), and the exact target sites and functional consequences of this modification have not yet been clearly established. Once activated, Chk1 is thought to dissociate from Claspin to act on both nuclear and cytoplasmic substrates (Lukas et al., 2003). Important Chk1 substrates involved in cell cycle control include positive and negative regulators of Cdk inhibitory phosphorylation, such as Cdc25A (Falck et al., 2002), Cdc25C (Blasina et al., 1999), and Wee1 (Lee et al., 2001). Chk1-mediated phosphorylation inhibits the activity of both Cdc25A and Cdc25C under conditions of genotoxic stress, although by different mechanisms; phosphorylation of Cdc25A targets the protein for degradation (Falck et al., 2002), while phosphorylated Cdc25C is sequestered in an inactive form through association with 14-3-3 proteins (Peng et al., 1997). Wee1 kinase activity, by contrast, is stimulated by Chk1-mediated phosphorylation (Lee et al., 2001). Chk1 is also thought to modulate recombination by phosphorylating Rad51 (Sorensen et al., 2005) and BRCA2 (Bahassi et al., 2008), and to mediate DNA damage-induced repression of gene transcription through phosphorylation of histone H3 (Shimada et al., 2008). Although predominantly nuclear, a proportion of active Chk1 also localizes at the centrosome, where it is thought to control the timing of activation of the mitotic Cdk1/cyclin B complex, and thus the onset of mitosis, both after damage and during unperturbed cell cycles (Kramer et al., 2004). As with ATM, ATR is also thought to act on many other substrates in addition to Chk1, including BRCA1, mini-chromosome maintenance (MCM) proteins, and components of the RPA complex (Cimprich and Cortez, 2008). In addition, global proteomic analyses suggest that ATM and ATR probably phosphorylate many other substrates; however, in most cases, the functional significance of these modifications has not yet been established (Matsuoka et al., 2007). In contrast to ATM and Chk2, however, ATR and Chk1 are thought to be active at low levels even during unperturbed cell cycles, particularly during S-phase (Syljuasen et al., 2005), potentially explaining why they are essential in many cell types.

III. CHECKPOINT FUNCTIONS OF THE ATM–CHK2 AND ATR–CHK1 PATHWAYS DNA damage or DNA synthesis inhibition in vertebrate cells evokes the activation of multiple, mechanistically distinct checkpoint responses that facilitate repair and promote cell survival (Kastan and Bartek, 2004).

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Fig. 2 Multiple DNA damage and replication checkpoints in vertebrate cells DNA damage and DNA synthesis inhibition evoke multiple, mechanistically distinct checkpoint responses in vertebrate cells that are controlled by the ATM–Chk2 and ATR–Chk1 pathways. In response to DNA damage these can delay entry to S-phase (G1 checkpoint), slow the replication of damaged DNA (intra-S checkpoint), or prevent entry to mitosis while damage persists (G2 checkpoint). When DNA synthesis is inhibited disinct checkpoint responses are triggered that serve to stabilize stalled replication forks (fork stabilization), suppress the firing of latent replication (origin suppression), and delay the onset of mitosis until DNA replication is complete (S-M checkpoint). Please refer to the text for further details and explanation.

As shown in Fig. 2, DNA damage induces cell cycle delays at the G1/S and G2/M transitions (the G1 and G2 checkpoints), and a transient decrease in the rate of DNA synthesis (the intra-S checkpoint). Of these, the G1 checkpoint is unique in depending primarily on the function of the p53 tumor suppressor protein and its downstream target, the cyclin-dependent kinase inhibitor p21CIP1 (Kastan and Bartek, 2004). G2 arrest, by contrast, is imposed by blocking activation of the mitotic Cdk1-cyclinB complex by preventing removal of the inhibitory threonine 14/tyrosine 15 (T14/ Y15) phosphorylation of Cdk1 (O’Connell et al., 2000). This is achieved, at least in large part, via inhibition of Cdc25 family phosphatases which play an important role in reversing this inhibitory phosphorylation to rapidly activate the Cdk1-cyclin B complex and trigger the onset of mitosis (Boutros et al., 2007). The molecular mechanism of the intra-S checkpoint is less well defined; however, it can involve both active replication fork slowing and suppression of replication origin firing (Grallert and Boye, 2008; Seiler et al., 2007). When DNA synthesis is blocked, additional replication checkpoint responses are required to stabilize stalled replication forks and prevent the formation of new forks by suppressing late replication origin firing (Branzei

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and Foiani, 2009). Because these functions are essential if cells are to resume replication and complete S-phase when circumstances permit, they are sometimes collectively termed the “replication recovery checkpoint.” In addition, it is essential that cells arrested in S-phase do not attempt mitosis until replication is complete. The mitotic delay triggered by DNA synthesis inhibition, which is also likely mediated through maintenance of inhibitory phosphorylation of Cdk1 (O’Connell et al., 2000), is generally termed the S-M checkpoint to distinguish it from the G2 arrest induced by DNA damage. Checkpoint mechanisms were first dissected in detail in budding and fission yeast. Each possesses an ortholog of Chk2; Rad53 in budding yeast and Cds1 in fission yeast, while both also express Chk1 homologs (O’Connell et al., 2000). The checkpoint functions of the yeast effector kinases are, however, remarkably variable; in budding yeast Rad53 is the dominant effector of both DNA damage and replication checkpoints, whereas in fission yeast DNA damage responses are assigned to Chk1 while Cds1 regulates replication checkpoint functions (O’Connell et al., 2000). When Chk1 and Chk2 were identified in vertebrates, the obvious question was “what would be the ‘division of labor’ compared to these model organisms?” Initially it was widely considered that the ATM–Chk2 and ATR–Chk1 pathways acted in parallel, with Chk1 and Chk2 playing overlapping or partially redundant roles in downstream checkpoint responses (as depicted in Fig. 1). Although some early studies suggested that Chk1 and Chk2 shared certain common substrates involved in cell cycle arrest, such as Cdc25 family phosphatases (Chaturvedi et al., 1999; Matsuoka et al., 1998), subsequent genetic and biochemical data have increasingly emphasized ATR–Chk1 as the principal, direct effector of the DNA damage and replication checkpoints, with ATM–Chk2 playing an auxiliary role specifically in the response to DSBs (Bartek and Lukas, 2003; Kastan and Bartek, 2004). Fundamental differences in the normal physiological functions of the ATM–Chk2 and ATR–Chk1 pathways were initially evident from the phenotypes of mice and cells deficient for each pathway. Thus, germ-line inactivation of ATR or Chk1 results in early embryonic lethality, whereas ATM- and Chk2-knockout mice are viable, and at least in the case of Chk2, remarkably normal (Brown and Baltimore, 2000; Liu et al., 2000; Takai et al., 2000). Similarly, ATM- and Chk2-deficient somatic cells can proliferate successfully in culture (Jallepalli et al., 2003; Lavin and Shiloh, 1997; Xu and Baltimore, 1996), whereas acute genetic inactivation of ATR or Chk1 leads to rapid cell death (Brown and Baltimore, 2003; Liu et al., 2000; Niida et al., 2007). Interestingly, DT40 lymphoma cells represent an exception to this general rule, since they survive genetic inactivation of Chk1 function, albeit with impaired cell growth and survival (Zachos et al., 2003).

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ATM-deficient human and mouse cells classically exhibit impaired G1, intra-S, and G2 checkpoint proficiency after DNA damage (Lavin, 2008). As mentioned previously, ATM is an important determinant of p53 stabilization and activation, and evidence suggests that the weakened G1 arrest in ATM mutant cells is attributable to inefficient p53 activation and downstream p21CIP1 induction (Kastan et al., 1992). The mechanism of the intra-S checkpoint is more complex. ATM is thought to suppress DNA replication via two distinct mechanisms; firstly, via direct phosphorylation of NBS1, and secondly through Chk2-mediated degradation of Cdc25A leading to inhibition of Cdk2, which in association with cyclins E or A is required for DNA replication origin firing (Falck et al., 2002). Interestingly, G2 checkpoint impairment in ATM mutant cells after exposure to ionizing radiation is markedly cell cycle phase-dependent. Thus, ATM-deficient cells in G2 phase at the time of damage are unable to arrest efficiently, whereas cells damaged in G1 and S-phase experience instead a prolonged arrest on reaching G2 compared to genetically normal counterparts (Xu et al., 2002). This has been explained by the existence of two molecularly distinct G2/M checkpoint arrests; “immediate G2 arrest,” which is triggered rapidly in G2 cells, and “G2 accumulation,” which affects cells that reach G2 after traversing S-phase and develops over many hours (Xu et al., 2002). Immediate G2 arrest is ATM-dependent and of relatively short duration, whereas G2 accumulation is ATM-independent (but ATR– Chk1-dependent) and much longer-lasting (Xu et al., 2002). Why this should be is not fully understood; it may reflect a more stringent requirement for ATM-dependent DSB processing for rapid and efficient checkpoint activation in G2 than in S-phase (discussed in more detail below), whereas the prolonged G2 accumulation experienced by ATM mutant cells could reflect a defect in DNA repair (Beucher et al., 2009). As for mechanism, Chk2 was initially invoked as a downstream effector of ATM-dependent G2 arrest via phosphorylation and inhibition of the Cdk1-activating Cdc25C phosphatase (Chehab et al., 2000; Matsuoka et al., 1998); however, as discussed below, Chk2-deficient cells do not exhibit consistent defects in G2 checkpoint proficiency. As with ATM, Chk2 has been implicated in p53 activation (Chehab et al., 2000), and consistent with this, Chk2-deficient mice show impaired G1 checkpoint arrest and defects in p53-dependent gene transcription after damage (Hirao et al., 2002; Takai et al., 2002). They also show a marked reduction in p53-dependent apoptotic responses in adult and developing tissues and increased organismal survival after whole body irradiation (Hirao et al., 2002; Takai et al., 2002). Perplexingly, however, p53 regulation is not altered in Chk2-deleted HCT116 cells, a human cancer cell line that retains wild-type p53 (Jallepalli et al., 2003). Also puzzling is the fact that G2 checkpoint impairment as a result of Chk2 inactivation is observed

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in some, but not all, experimental systems. Thus, MEFs and HCT116 cells deleted for Chk2 retained normal G2 checkpoint proficiency (Jallepalli et al., 2003; Takai et al., 2002), whereas Chk2 knockout DT40 cells exhibited a weakened and delayed G2 arrest at early times after irradiation (Rainey et al., 2008). Interestingly, this defect was most severe in G2 cells, much as described for ATM (Xu et al., 2002), whereas the slower G2 accumulation response was relatively unaffected (Rainey et al., 2008). Variations in the severity of G2 checkpoint deficiency in cells genetically deficient for Chk2 have often been interpreted in terms of compensation by Chk1; however, it is equally possible that the role of Chk2 in this checkpoint simply varies between cell types. Phenotypic analysis of checkpoint proficiency in ATR- or Chk1-deficient mouse embryos and cells is complicated by loss of viability; however, inactivation of both genes results in profound G2 and S-M checkpoint defects after DNA damage or replication arrest (Brown and Baltimore, 2000; Brown and Baltimore, 2003; Liu et al., 2000; Takai et al., 2000). Consistent with this, Chk1-deficient DT40 cells (which lack functional p53) show complete loss of proficiency for all DNA damage and replication checkpoint responses (Zachos et al., 2003, 2005). Strikingly, these cells show no measurable G2 arrest or intra-S checkpoint activation after irradiation at any dose tested (Zachos et al., 2003), in marked contrast to the partial loss of G2 checkpoint proficiency that results from deletion of Chk2 (Rainey et al., 2008). Loss of G2 checkpoint proficiency was furthermore associated with failure to maintain inhibitory T14/Y15 Cdk1 phosphorylation (Zachos et al., 2003), indicating that Chk1 is essential to restrain the mitosis-promoting activity of Cdc25 family phosphatases after DNA damage. In addition, when DNA polymerase is inhibited Chk1-deficient cells suffer a combination of progressive replication fork collapse and futile origin firing that leads ultimately to S-M checkpoint failure and premature entry to mitosis with unreplicated DNA (Zachos et al., 2005). Interestingly, Chk1-deficient DT40 cells also exhibit high levels of spontaneous replication fork collapse during unperturbed cell cycles which is compensated by increased replication origin firing (Maya-Mendoza et al., 2007; Petermann et al., 2006). Although this compensation mechanism evidently allows these cells to replicate successfully and to maintain an S-phase of approximately normal length, replication fork collapse as a result of Chk1 inhibition is a potential cause of cell death in other cell types (Syljuasen et al., 2005). The effects of inhibiting ATR or Chk1 on DNA damage and replication checkpoint proficiency have also been widely explored in cells in culture using various dominant-negative, siRNA depletion, or chemical inhibition approaches. The general consensus that has emerged from such studies [reviewed in (Bartek and Lukas, 2003; Kastan and Bartek, 2004;

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Stracker et al., 2009)] is broadly consistent with the genetic analysis in mice and DT40 cells summarized above; namely that ATR and Chk1 are crucial both for the G2 and intra-S checkpoint responses induced by DNA damage and for the S-M and fork stabilization/origin suppression checkpoints triggered by replication arrest. Whether the ATR–Chk1 pathway also contributes to p53-dependent G1 arrest under any circumstances is less clear. Athough some early biochemical data implicated ATR and Chk1 as potential regulators of p53 (Shieh et al., 2000; Tibbetts et al., 1999), more recent evidence that ATR–Chk1 activation by DNA damage is largely restricted to the S and G2 phases of the cell cycle makes it seem unlikely to be a major physiological determinant of G1 arrest (Jazayeri et al., 2006; Walker et al., 2009).

IV. THE THREE RS OF DAMAGE SIGNALING: RESECTION, RECOMBINATION, AND REPAIR In eukaryotes DNA DSBs are repaired via two main mechanisms; nonhomologous end-joining (NHEJ) and homologous recombination repair (HRR). NHEJ occurs throughout the cell cycle; however, because HRR requires a sister chromatid to serve as a template, this mechanism is restricted to the S and G2 phases. Unlike NHEJ, HRR requires extensive DNA damage processing to generate tracts of ssDNA that, once coated with Rad51 recombinase, invade the homologous DNA duplex to initiate repair. Such single-stranded tracts are generated by resection of DSBs in a 30 –50 direction, a reaction initiated by the endonuclease activity of the MRN complex (Mimitou and Symington, 2009). Because of its central role in initiating HRR, DNA strand resection at DSBs is regulated during the cell cycle and this regulation is thought to play a major role in restricting HRR to S and G2. In yeasts, Cdk activity controls DNA strand resection via phosphorylation of Sae2 (Wohlbold and Fisher, 2009), a putative nuclease that promotes resection in collaboration with the yeast counterpart of MRN (Huertas et al., 2008). Vertebrate cells express an ortholog of Sae2 in the form of CtIP, which is also required for DSB resection (Sartori et al., 2007). Resection is also cell cycle-regulated in vertebrate cells, and evidence suggests that Cdks regulate this process at least in part via direct phosphorylation of CtIP in a manner analogous to yeast (Huertas and Jackson, 2009; Yun and Hiom, 2009). Thus, because Cdk-mediated phosphorylation of CtIP is required for strand resection, increased Cdk activity in S and G2 ensures maximum resection activity in these phases of the cell cycle.

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Two other important components required for HRR are the BRCA1 and BRCA2 tumor suppressors. BRCA1 is a multifunctional protein that plays multiple roles in checkpoint activation, DNA repair, and gene transcription in response to DNA damage (Huen et al., 2010). BRCA1 is recruited to sites of ionizing radiation-induced DNA damage and is required for efficient generation of ssDNA at early times after irradiation (Schlegel et al., 2006). Although the mechanism is not fully understood, BRCA1 interacts with MRN and CtIP, suggesting that it too may play a role in strand resection (Chen et al., 2008). BRCA2 by contrast is required for homologous recombination downstream of strand resection through its well-established role in loading the Rad51 recombinase protein (Thorslund and West, 2007). As shown in Fig. 3, the recent discovery that ATM is required for strand resection and downstream activation of ATR–Chk1 in response to DSBs provides a new framework for understanding the organization and integration of checkpoint and repair pathways (Adams et al., 2006; Cuadrado et al., 2006; Jazayeri et al., 2006; Myers and Cortez, 2006). Although not classically considered a core homologous recombination (HR) factor, ATM is required for efficient HRR of a subset of DSBs specifically in G2 phase (Beucher et al., 2009). Molecular details are still emerging; however, current thinking is that initial recognition of DSBs by the MRN complex leads to recruitment and full activation of ATM. Active ATM then promotes the recruitment of CtIP to sites of damage where it interacts with and stimulates the nuclease activity of MRE11 to initiate strand resection and generate short tracts of ssDNA (You et al., 2009). These may be extended through the actions of other nucleases and helicases, such as Exo1 and BLM, to generate more extensive regions of ssDNA that recruit RPA and form both the initiating substrate for HRR and activating platform for ATR–Chk1 activation (Mimitou and Symington, 2009). Exactly how ATM stimulates resection is not yet known; however, NBS1 and CtIP are both subject to ATM-dependent phosphorylation after damage, and at least in the case of CTIP, this modification appears to be required both for recruitment to DSBs and resection (You et al., 2009). The ramifications of this model are extensive. Firstly, it explains why ATM is required for rapid activation of ATR–Chk1 in response to DSBs but not DNA polymerase inhibition, since the latter generates extensive tracts of ssDNA directly without the need for DNA damage processing (Byun et al., 2005; Myers and Cortez, 2006). Secondly, it accounts for why ATR–Chk1 activation in response to irradiation-induced DSBs is largely confined to S and G2 phase (Jazayeri et al., 2006; Walker et al., 2009), since this is when levels of Cdk activity become permissive for efficient strand resection (Cerqueira et al., 2009). Thirdly, it explains why other proteins required for ssDNA generation, such as MRE11, NBS1, and BRCA1, are also required both for rapid ATR–Chk1 activation and G2 checkpoint proficiency in response to DSBs (Myers and Cortez, 2006; Yarden et al., 2002).

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Fig. 3 ATM is required for DNA strand resection and ATR–Chk1 activation in response to DSBs In response to DSBs ATM, in conjunction with the MRN complex, CtIP and BRCA1, is required for nucleolytic strand resection to generate tracts of ssDNA. Evidence suggests that ATM stimulates the nuclease activity of MRE11 to initiate resection but that this process may then be extended through the actions of other nucleases and helicases such as Exo1 and BLM. Once complexed with RPA, such tracts form the platform both for ATR-ATRIP recruitment leading to Chk1 activation and also the initiating structure for HRR. BRCA2 promotes loading of Rad51 which displaces RPA leading to strand invasion and subsequent recombination. In contrast, inhibition of DNA synthesis generates tracts of ssDNA-RPA directly without the need for stand resection by stalling replication forks and uncoupling the replicative polymerase and helicase. The various posttranslational modifications involved in ATM–Chk2 and ATR–Chk1 activation shown in Fig. 1 are omitted here for clarity. In contrast to the more conventional scheme depicted in Fig. 1, in this model ATR and Chk1 are the direct effectors of multiple DNA damage and replication checkpoints with ATM acting upstream specifically in the context of DSBs. ATM and Chk2, however, continue to signal DNA damage independently to p53 in a parallel pathway. Please refer to the text for further details and explanation.

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Placing ATM upstream of ATR–Chk1 activation in response to DSBs suggests a new interpretation of their overlapping, yet distinct, functions in checkpoint signaling (as depicted in Fig. 3). In this model the ATR–Chk1 pathway is the principal direct effector of the damage and replication checkpoints (apart from p53-dependent G1 arrest), with ATM modulating the response specifically to DSBs indirectly via its role in strand resection. Thus, in ATM mutant cells, rapid activation of ATR and Chk1 in response to DSBs will be impaired as a consequence of inefficient strand resection. This defect is likely to be particularly significant in G2 phase, when resection is the principal mechanism of ssDNA generation. This model predicts therefore that the impact of ATM deficiency on ATR–Chk1 activation and thus G2 checkpoint proficiency in response to DSBs will be greatest in G2 phase, consistent with the immediate G2 arrest defect described in ATM mutant cells (Xu et al., 2002). This model also explains why Chk1-deficient DT40 cells lack any detectable G2 checkpoint after irradiation, despite the continued presence of functional ATM and Chk2 (Zachos et al., 2003), since in this scheme Chk1 is the sole direct downstream effector of G2 arrest. Conversely, because DNA polymerase inhibition generates ssDNA directly without the need for resection, ATR and Chk1 are essential for replication checkpoint responses whereas ATM and Chk2 are not. Where to place Chk2 in this scheme becomes an interesting question. ATM and Chk2 are activated in response to DSBs at all stages of the cell cycle, including G1, consistent with their established roles in activating p53 and triggering G1 arrest (Lavin and Kozlov, 2007; Takai et al., 2002). Chk2 is, however, variably required for G2 checkpoint proficiency in different cell types, and whether it is a direct effector of this checkpoint has been questioned (Antoni et al., 2007). One intriguing possibility, suggested by the apparent epistatic relationship between Chk1 and Chk2 in DT40 cells (i.e., where G2 checkpoint proficiency is completely abolished in the absence of Chk1 but only impaired in the absence of Chk2), is that Chk2 might also participate in the DNA strand resection process and thus in regulating the efficiency and cell cycle phase-specificity of ATR–Chk1 activation indirectly. Although there is currently no direct evidence for such a role, further investigation seems warranted.

V. ATM–CHK2 AND ATR–CHK1 PATHWAY ALTERATIONS IN CANCER The importance of genome stability for preventing carcinogenesis is evident both from human cancer predisposition syndromes that result from inherited loss-of-function mutations in DNA damage response genes and

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from the occurrence of sporadic mutations affecting such genes in cancers in otherwise genetically normal individuals (Kastan and Bartek, 2004). Examples of both have been found to affect ATM and Chk2 in human cancer, whereas ATR and Chk1 appear to be mutated only rarely. Important insights into the roles of these pathways in tumor formation have also been obtained from experiments using genetically modified mice. In discussing these issues, we also refer in passing to other gene functions, such as the MRN complex and BRCA1/BRCA2, where these are closely linked either to the regulation or downstream functions of the ATM–Chk2 and ATR–Chk1 pathways. Homozygous germ-line loss-of-function mutations affecting ATM cause the pleiotropic human disease syndrome Ataxia telangiectasia (AT), characterized by immunodeficiency, neurodegeneration, radiation hypersensitivity, and spontaneous predisposition to lymphoma (Shiloh and Kastan, 2001). Similarly, hypomorphic mutations affecting the genes encoding the functionally related MRE11 and NBS1 proteins give rise to the human conditions Ataxia-like disorder (ATLD), and Nijmegen breakage syndrome (NBS), each of which shares some clinical similarities with AT, although only NBS is clearly associated with cancer predisposition (Stewart et al., 1999; Varon et al., 1998). As with AT humans, ATM knockout mice are predisposed to lymphoma and radiosensitive (Xu et al., 1996), while mice with engineered hypomorphic mutations of NBS1 or RAD50 are also cancer prone (Bender et al., 2002; Kang et al., 2002; Williams et al., 2002). The precise cause of cancer predisposition in humans and mice with inherited defects in ATM or MRN components is not yet known; however, genomic instability and an increased mutation rate resulting from repair and checkpoint defects is presumably an important factor. Interestingly, although AT is considered to be an autosomal recessive genetic disorder, individuals heterozygous for ATM mutations show an increased incidence of cancer possibly related to medical or occupational radiation exposure (Briani et al., 2006; Swift et al., 1991). In addition, cells from heterozygote individuals show sensitivity to radiation in vitro that is intermediate between those from AT patients and normal individuals (Swift et al., 1991). Taken together, these findings indicate that ATM is a partially penetrant cancer susceptibility gene that might interact with certain environmental predisposing factors. Somatic mutations affecting ATM have also been documented in sporadic lymphoid malignancies and lung adenocarcinomas, although at relatively low incidence (Ding et al., 2008; Gumy-Pause et al., 2004). In contrast to AT, ATLD, and NBS, human individuals heterozygous for loss-of-function alleles of the BRCA1 and BRCA2 tumor suppressor genes are developmentally normal but suffer from a greatly increased incidence of breast and ovarian cancer (O’Donovan and Livingston, 2010). As mentioned previously, BRCA1 is required for ATR–Chk1 activation, G2 arrest,

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and other complex responses to DNA damage (Huen et al., 2010). In general, however, the tumor suppressive functions of BRCA1 and BRCA2 are attributed to their distinct but essential roles in HR-mediated DNA repair (O’Donovan and Livingston, 2010). Because tumorigenesis involves functional inactivation of the remaining functional BRCA1 or BRCA2 allele through loss of heterozygosity or other means (Collins et al., 1995; Neuhausen and Marshall, 1994), the tumors that arise in susceptible individuals consist of cells that are deficient for HRR whereas those in normal tissues remain proficient (Turner et al., 2005). Genomic instability as a result of HRR deficiency is thought to play a key role in the development of such tumors, presumably by accelerating the accumulation of oncogenic mutations (Tutt et al., 2002); however, as discussed below, the presence or absence of DNA repair proficiency in normal and tumor tissue respectively in such individuals also provides an exploitable therapeutic index. Li Fraumeni syndrome is a multiorgan cancer predisposition condition that is generally, but not exclusively, due to inherited mutations in p53 (Birch, 1994). Heterozygous germ-line mutations in Chk2 were initially reported in a subset of Li-Fraumeni kindreds lacking p53 mutations, consistent with a functional link between Chk2 and p53 (Bell et al., 1999). However, because these mutant alleles were subsequently also found in normal individuals in the general population they are now considered unlikely to be the genetic cause of Li Fraumeni syndrome (Antoni et al., 2007). Nevertheless, studies have established that individuals bearing certain mutant Chk2 alleles do suffer from a statistically significant increase in the incidence of breast, prostate, and other cancers, suggesting that Chk2 is indeed a moderate or low penetrance cancer susceptibility gene in humans (Antoni et al., 2007). Despite this, the tumors that arise in individuals heterozygous for such mutations do not consistently lose the remaining normal Chk2 allele, indicating that Chk2 does not conform to the conventional definition of a tumor suppressor (Antoni et al., 2007). One possibility is that mutant Chk2 proteins exert a dominant-negative effect by inhibiting the endogenous, normal Chk2, to phenocopy loss of function in the heterozygous state. This would be consistent with the occurrence of occasional sporadic Chk2 mutations and rare instances of reduced or absent expression in a variety of different tumor types (Antoni et al., 2007). Alternatively, it is conceivable that Chk2 haploinsufficiency per se synergises with other, as yet unknown, oncogenic events or environmental factors to promote malignant progression. The impact of Chk2 deficiency on tumorigenesis in mice has also been examined. Although Chk2 knockout mice are developmentally normal and not spontaneously cancer prone (Takai et al., 2002), they are more sensitive to chemical skin carcinogenesis, showing an increase both in overall tumor burden and in the rate at which benign tumors formed after exposure to

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chemical carcinogens (Hirao et al., 2002). Whether this increase in tumor sensitivity is attributable to increased genomic instability or perhaps to the relative resistance to stress-induced apoptosis that has been observed in Chk2 knockout mice however remains unclear. In humans, homozygous hypomorphic mutations affecting ATR give rise to Seckel syndrome. Seckel syndrome is associated with a wide range of deleterious symptoms, including growth retardation and microcephaly; however, such individuals do not suffer from an increased incidence of cancer (Kerzendorfer and O’Driscoll, 2009). Seckel syndrome has also been modeled in mice (Murga et al., 2009), and the consequences of acute conditional genetic inactivation of ATR postdevelopment in adult mice have been investigated (Ruzankina et al., 2007). Degenerative and premature aging-like phenotypes were observed in each case, underscoring the crucial importance of ATR for normal development, stem cell survival, and tissue homeostasis; however, neither showed evidence of cancer predisposition (Murga et al., 2009; Ruzankina et al., 2007). In general therefore it appears that partial or complete inactivation of ATR function, although clearly deleterious in at least some cell types in vivo, does not perturb genome stability in such a way as to promote carcinogenesis. Consistent with this, somatic mutations affecting ATR have not been widely found in cancers (Heikkinen et al., 2005), with the exception of rare sporadic stomach and endometrial tumors with microsatellite instability (MSI) (Menoyo et al., 2001; Vassileva et al., 2002; Zighelboim et al., 2009). Germ-line mutations in Chk1 have thus far not been implicated in any human disease and, as with ATR, somatic mutations affecting Chk1 appear to be rare in human cancers, although some exceptions have been reported in tumors with MSI (Bertoni et al., 1999; Menoyo et al., 2001). Embryonic lethality in knockout mice precludes direct assessment of the effect of constitutive Chk1 inactivation on spontaneous or induced carcinogenesis; however, some evidence that partial loss of function can promote tumor formation has been reported. Thus, mammary tumors induced by an oncogenic WNT transgene developed more rapidly in Chk1 hemizygous mice than wild-type (Liu et al., 2000). Importantly, however, loss of the remaining functional Chk1 allele was not observed, suggesting that Chk1 continued to be important for the proliferation or survival of the tumor cells (Liu et al., 2000). More recently, we have examined the effect of experimentally induced hemi- or homozygous conditional deletion of Chk1 in mouse skin on the formation of tumors induced by the chemical carcinogens, DMBA and TPA, using a conditional allele of Chk1 combined with a Keratin14-CreER recombinase transgene (Indra et al., 2000; Lam et al., 2004). This combination allows efficient Chk1 deletion throughout the epidermis in response to systemic treatment with the synthetic estrogen, tamoxifen (LM Tho and DA

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Gillespie, unpublished results). We find that homozygous deletion of Chk1 throughout the epidermis is tolerated without acute pathology, presumably because Chk1 is not essential in the postmitotic, terminally differentiated cells that comprise the bulk of the tissue. However, when recombination is induced immediately prior to carcinogen exposure both the number and size of benign papillomas obtained is strongly suppressed (Table 1). Furthermore, the small lesions that form in ablated skin always derive from cells that escape recombination (LM Tho and DA Gillespie, unpublished results). Although the basis of this tumor suppressive effect is not yet fully understood, we hypothesize that Chk1 is probably essential for the proliferation or survival of the epidermal stem cells that are thought to give rise to chemical carcinogen-induced tumors in the skin (Morris, 2004). Consistent with this, developmental or conditional deletion of Chk1 has been shown to lead to rapid cell death in several other tissues including mammary gland, intestine, and lymphocytes (Greenow et al., 2009; Lam et al., 2004; Zaugg et al., 2007). In marked contrast, Chk1 hemizygous skin supports normal papilloma formation but such hemizygous lesions show an increased probability of progressing to malignant carcinoma (Table 1). We deduce from these experiments that whereas complete loss of Chk1 function is incompatible with skin tumor formation, partial loss of function fosters benign-malignant tumor progression. This conclusion is consistent with the previously described acceleration of WNT-induced tumorigenesis and also evidence that Chk1

Table 1 Conditional Deletion of Chk1 in Mouse Epidermis Suppresses Chemical Carcinogen-Induced Skin Tumorigenesis

Cohort 1 2 3 4

Genotype

Treatment

Mean no. papillomas

% Conversion to carcinoma

Chk1þ/þ K14-CreER Chk1Fl/Fl K14-CreER Chk1Fl/Fl K14-CreER Chk1Fl/þ K14-CreER

Tamoxifen Vehicle Tamoxifen Tamoxifen

17.8 (N ¼ 20) 15.4 (N ¼ 19) 5.5 (N ¼ 18) 14.6 (N ¼ 19)

5.9 5.1 2 9.7

Cohorts of FVB strain mice were bred to express an epithelial-specific K14-CreER transgene (Indra et al., 2000) in combination with either wild-type Chk1 (Chk1þ) or a conditional, lox P-modified allele of Chk1 (Chk1Fl) (Lam et al., 2004). At 6 weeks of age mice were treated systemically with Tamoxifen, resulting in efficient Chk1 deletion throughout the epidermis (L.M. Tho and D.A. Gillespie, unpublished results), or vehicle control. Mice were then immediately treated with a single dose of the carcinogen, DMBA, followed by twice weekly applications of the tumor promoter TPA for up to 30 weeks (Abel et al., 2009). Total plateau papilloma burden was quantified at that time, or at time of sacrifice if earlier, together with the proportion of papillomas that converted to carcinoma. The reduction in papilloma yield in cohort 3 was highly statistically significant compared to controls (cohorts 1 and 2; Mann Whitney test p < 0.001). Similarly, the rate of conversion to carcinoma was significantly elevated in cohort 4 compared to controls (cohorts 1 and 2; chi-squared test p < 0.025). A more detailed description of these data will be presented elsewhere.

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haploinsufficiency can lead to aberrant cell cycle regulation and genomic instability in vivo (Lam et al., 2004; Liu et al., 2000). Conversely, it has been reported that Chk1 hemizygous mice suffer from an increased incidence of anemia associated with increased levels of DNA damage in erythroid progenitors, suggesting that Chk1 haploinsufficiency can also result in degenerative effects in certain cell lineages, perhaps also owing to stem cell death (Boles et al., 2010). The ability therefore of cells and organisms to tolerate partial or complete loss of function within the ATM–Chk2 and ATR–Chk1 pathways is very different. Impairment or even complete loss of ATM–Chk2 signaling is compatible with cell and organism survival, although frequently at the cost of cancer predisposition, presumably at least in part as a result of genomic instability and more rapid accumulation of oncogenic mutations. In contrast, ATR and Chk1 seem to be essential for the proliferation and survival of many, although not all, cell types, both in vitro and in the developing embryo and adult organism, presumably because they control aspects of DNA metabolism that when dysfunctional lead to cell death rather than survival with mutation. In this scheme Chk1 (and arguably ATR) becomes a logical target for therapeutic strategies based on pharmacological checkpoint suppression (discussed below), although evidence from mouse models that partial loss of Chk1 function (i.e., haploinsufficiency) may promote tumorigenesis, or other undesirable pathologies (Boles et al., 2010), clearly merits careful consideration.

VI. EXPLOITING HOMOLOGOUS RECOMBINATIONAL REPAIR (HRR) DEFECTS FOR CANCER THERAPY In recent years it has emerged that in addition to predisposing to cancer as a result of increased genomic instability, defective HRR may also render tumor cells inherently vulnerable to specific conventional anticancer agents and also to new strategies based on inhibition of complementary repair pathways. Thus, BRCA1- and BRCA2-deficient tumor cells have been found to be hypersensitive to cross-linking agents such as cisplatin in vitro (Bhattacharyya et al., 2000; Yuan et al., 1999), most probably because HR is the principal mechanism through which replication forks stalled or collapsed by such lesions are repaired or restarted. Importantly, evidence suggests this is also true in vivo and that ovarian cancers arising in BRCA1 and BRCA2 mutation carriers may respond better to platinum-based therapy than similar sporadic tumors (Foulkes, 2006). Remarkably, secondary mutations that restore BRCA1 or BRCA2 function can be a cause both for drug resistance in ovarian cancer cells in culture and treatment failure in

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patients, emphasizing the importance of HRR deficiency in determining sensitivity to platinum compounds (Sakai et al., 2008,2009; Swisher et al., 2008). Poly (ADP-ribose) polymerase 1 (PARP1) is a nuclear enzyme that is activated by DNA single-strand breaks (SSBs) and plays a crucial role in multiple aspects of the DNA damage response (Rouleau et al., 2010). In response to DNA damage active PARP1 binds to SSBs in DNA and catalyzes the synthesis of branched, protein-conjugated poly (ADP-ribose) chains. Many of these chains become linked to PARP1 itself as a result of automodification, although histones, topoisomerases, and many other proteins involved in diverse aspects of DNA metabolism are also substrates for PARP1 (D’Amours et al., 1999). Once formed at sites of damage, such poly (ADP-ribose) chains recruit multiple proteins involved in DNA repair and modulating chromatin structure (Rouleau et al., 2010). In particular, PARP1 both modifies and recruits XRCC1, a key scaffolding factor required for base excision repair (BER). BER excises damaged or mismatched bases from DNA and also repairs single-strand gaps, nicks, and abasic sites, lesions which arise both spontaneously and as a result of oxidative stress or exposure to alkylating agents. Based on its established role in DNA repair, selective small molecule inhibitors of PARP1 were initially developed with a view to enhancing the potency of DNA damaging chemotherapies (Zaremba and Curtin, 2007). Although PARP1 inhibitors are relatively nontoxic to most cancer cells alone, it was subsequently discovered that tumor cells deficient for BRCA1 or BRCA2 were inherently and exquisitely sensitive to such agents even in the absence of exogenous genotoxic stress (Bryant et al., 2005; Farmer et al., 2005). The basis of this selective sensitization has been explained in terms of synthetic lethality, a term applied to genetic functions or pathways whose individual loss is tolerated but when combined result in lethality (Kaelin, 2005). In this context of course the synthetic lethal interaction occurs between an inherent genetic deficiency for HRR resulting from BRCA1/ BRCA2 mutation and a phenocopy of functional BER deficiency that is imposed through pharmacological inhibition of PARP1. Thus, inhibition of PARP1 leads to accumulation of SSBs and other endogenous DNA damage lesions that would normally be corrected by BER (Jeggo, 1998). Such lesions are particularly problematical in S-phase, since when encountered by replicative polymerases they lead to replication fork collapse and formation of DSBs. HRR is thought to be the main mechanism through which such DSBs can be repaired and replication restarted, enabling HRR proficient cells to tolerate PARP inhibition (Li and Heyer, 2008). However, because BRCA1- and BRCA2-deficient tumor cells are impaired both for HRR and BER under conditions of PARP inhibition, replication-associated DSBs cannot be repaired and thus escalate to lethal proportions (Bryant et al.,

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2005; Farmer et al., 2005). As with resistance to platinum compounds, acquired resistance to PARP inhibition can arise through reversion mutations that restore BRCA2 function, at least in cell lines (Edwards et al., 2008). Importantly, this synthetic lethal principle can be extended to therapy; several recent trials have shown that Olaparib, a potent oral PARP inhibitor, has significant antitumor activity as monotherapy specifically in ovarian cancers arising in BRCA1 and BRCA2 mutation carriers, and remarkably, this activity is achieved without the toxicity associated with conventional genotoxic chemotherapy (Fong et al., 2009, 2010). In addition, much preclinical evidence suggests that PARP inhibition may also synergize with conventional genotoxic chemotherapies, including alkylating agents, platinum compounds, topoisomerase inhibitors, and radiation, both in BRCA1 and BRCA2 mutant and sporadic tumors (Ratnam and Low, 2007). As a result, Olaparib and many other PARP inhibitors are currently undergoing trials both as monotherapies and in combination with conventional agents (Rouleau et al., 2010). The evident promise of PARP inhibition as a selective therapy against tumors with defects in HRR raises the obvious question of how widespread this phenotype is in sporadic cancers arising in genetically normal individuals. Cell culture studies show that experimental inhibition of many diverse gene functions involved either directly in HRR or in DNA damage signaling more generally (including Rad51, RPA1, NBS1, ATM, ATR, Chk1, Chk2, and certain Fanconi anemia gene products) all result in sensitization to PARP inhibition (McCabe et al., 2006). In addition, human and mouse cells genetically deficient for ATM are also sensitized to PARP inhibition (Loser et al., 2010; Williamson et al., 2010). As already mentioned, inherited mutations in several of these genes result in cancer predisposition; however, there is little evidence that such genes are frequently mutated in sporadic cancers. The PTEN tumor suppressor, by contrast, is one of the most frequently mutated or inactivated genes in human cancer and leads to upregulation of the PI3-kinase-PKB/ Akt growth and survival signaling pathway (Chalhoub and Baker, 2009). Loss of PTEN has been known for some time to compromise genome stability (Puc et al., 2005); however, recent data have revealed unexpected connections with HR and DNA repair. Remarkably, human HCT116 colon carcinoma cells genetically deleted for PTEN are both deficient for HR and sensitized to PARP inhibitors (Mendes-Pereira et al., 2009). Similar effects have been documented in PTEN-deficient murine astrocytes and human glioblastoma cell line (McEllin et al., 2010). The mechanism through which PTEN affects HR is not yet fully understood; however, two recent studies have shown that Akt, which is upregulated as a result of PTEN loss, can inhibit both strand resection and recruitment of essential HR factors such as BRCA1 and CtIP at radiation-induced DSBs (Tonic et al.,

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2010; Xu et al., 2010). The prospect that frequently activated oncogenic signaling pathways conventionally linked to cell proliferation and survival may interact with DNA damage response and repair processes suggest that impaired HRR may be widespread in sporadic cancer and that PARP inhibitors could have more generic application than currently thought.

VII. DNA DAMAGE SIGNALING AS A BARRIER TO TUMORIGENESIS Predisposition syndromes demonstrate that genomic instability can promote cancer; however, in recent years an alternative paradigm has emerged in which DNA damage and the resulting downstream signaling processes act as a tumor suppression mechanism (Halazonetis et al., 2008). This concept originated with the observation that premalignant or early stage lesions in several cancer types, including bladder, breast, lung, and colon, frequently showed evidence of DNA damage as judged by the presence of multiple surrogate or direct markers of damage signaling such as -H2AX, and phosphorylated, active forms of ATM, Chk2, and p53 (Bartkova et al., 2005). These markers were not present in proliferating normal tissue, and strikingly, their prevalence diminished again in the more malignant, later stages of disease (Bartkova et al., 2005). Crucially, the signs of DNA damage in early stage lesions preceded the appearance of overt genomic instability or of p53 mutations, both of which are frequent in fully malignant tumors (Bartkova et al., 2005). Based on these observations it was proposed that spontaneous DNA damage occurs early in the evolution of cancer and that the resulting DNA damage signaling processes direct incipient cancer cells to terminal, nonproductive fates such as senescence or apoptosis. It was furthermore postulated that this might provide a selective pressure for specific secondary genetic alterations, such as p53 mutation, that would allow cells to escape these fates and survive (Bartkova et al., 2005). Although these initial findings were largely correlative, subsequent studies have provided substantial support for this scenario (Bartkova et al., 2006; Di Micco et al., 2006). Thus, while activated oncogenes are known to drive the proliferation of malignant tumor cells, expression of the same oncogenes in naı¨ve, genetically normal cells in culture often results not in cell transformation but instead in growth inhibition through cell senescence or apoptosis (Braig and Schmitt, 2006) . Oncogene-induced senescence (OIS) in cultured cells is associated with physical DNA damage and a robust DNA damage response (Di Micco et al., 2006), providing a tractable model to test the role of downstream signaling processes in imposing this phenotype. Remarkably, depletion or inhibition of several key DNA damage signaling components,

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including ATM, Chk1, Chk2, and p53, relieved OIS to allow more vigorous cell proliferation and efficient transformation of oncogene-expressing cells (Bartkova et al., 2006; Di Micco et al., 2006). Although cell culture experiments obviously do not accurately recapitulate all features of tumorigenesis in vivo, it is telling that markers of cell senescence and DNA damage signaling are both present in early stage or premalignant lesions and both are lost as tumors progress to more malignant forms (Bartkova et al., 2006). Such studies have stimulated interest in two questions; firstly, how do oncogenes trigger DNA damage, and secondly, what are the genetic or epigenetic changes which enable tumor cells to survive and proliferate in the face of such damage? With respect to the first question, telomere erosion, reactive oxygen species, or acquired mutations in genes required for genome stability could all plausibly generate DNA damage during tumorigenesis or transformation; however, it has been argued that none of these occur sufficiently frequently to account for OIS either in vitro or in vivo (Negrini et al., 2010). Instead, current evidence favors the view that dominant, growthpromoting oncogenes, such as Ras or EGFR, which are frequently and recurrently mutated in sporadic cancers, disturb the DNA replication process directly in such a way as to generate DNA damage (Halazonetis et al., 2008). Exactly how oncogene-induced replication stress occurs remains unclear; however, Cdks are potential culprits. Ordered cycles of Cdk activation and deactivation are essential for the proper organization and activation of the replication program through the replication origin licensing system (Diffley, 2004). Because Cdk activity is key to uncontrolled proliferation, it is deregulated by oncogene activation and tumor suppressor loss by many different mechanisms. Amplification of Cdk activity as a result of oncogenic signaling therefore may erode these ordered cycles, leading to aberrant over- or under-replication and thus to DNA damage (Blow and Gillespie, 2008). According to this view, uncontrolled proliferation inevitably leads to spontaneous DNA damage and genomic instability (Halazonetis et al., 2008). With respect to mechanisms that may allow escape from oncogene-induced DNA damage, recent high-throughput sequencing studies have generally failed to find evidence for frequent mutations affecting DNA damage response genes, such as ATM–Chk2 and ATR–Chk1, in sporadic cancers, although clearly this does not rule out functional inactivation by epigenetic or other means (Negrini et al., 2010). By contrast, missense mutations that inactivate the p53 tumor suppressor protein are frequent in sporadic cancer and some genetic evidence is consistent with the idea that these could be selected to permit escape from DNA damage-induced OIS or apoptosis (Negrini et al., 2010). One possible explanation for their prevalence is that mutant p53 proteins can act dominantly, forming complexes with and inhibiting normal p53 or the related p63 and p73 proteins (Brosh and

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Rotter, 2009). In contrast, biallelic mutations would presumably be required to inactivate the function of most other DNA damage response genes. At all events, frequent loss of p53 means that many tumor cells lack an effective G1 checkpoint. As discussed below, this defect may render such tumors inherently vulnerable to therapeutic strategies based on checkpoint suppression. Although inactivation of p53 could plausibly mitigate many of the negative effects of oncogene-induced DNA damage, it does not offer an obvious explanation for why DNA damage signaling per se should be lost in the later stages of tumorigenesis (Bartkova et al., 2005). Also, since malignant tumors commonly exhibit high levels of genomic instability (Negrini et al., 2010), it seems unlikely that the ongoing generation of oncogene-induced DNA damage ceases as tumors progress, but more likely that its presence is simply no longer detected. Why should this be? As previously mentioned, several recent studies have revealed that upregulation of the PI3K-Akt pathway, which is common in cancer as a result of PTEN inactivation and other mechanisms (Chalhoub and Baker, 2009), inhibits both HRR and checkpoint activation by suppressing DNA damage processing (Tonic et al., 2010; Xu et al., 2010). One prediction of these findings is that tumor cells with high PI3K-Akt pathway activity will show a muted response to endogenous DNA damage, potentially providing another mechanism through which oncogene-induced DNA damage could be tolerated. However, a second prediction is that this would inevitably be associated with genomic instability and an increased mutation rate, since ongoing oncogene-induced DNA damage would continue undetected but unabated in cells with high PI3KAkt activity. Finally, it seems possible that tumors which have evolved through such a route might show an altered response to conventional genotoxic therapies, since presumably the recognition and signaling of therapeutic DNA damage would also be suppressed.

VIII. CHECKPOINT SUPPRESSION AS A THERAPEUTIC PRINCIPLE Radiation and genotoxic chemotherapies remain the mainstays of cancer treatment. Although new, molecularly targeted, drugs like imatinib have revolutionized treatment of rare cancers such as chronic myeloid leukemia (Agrawal et al., 2010), there seems little prospect that conventional therapies will be replaced in the treatment of other, more common malignancies in the foreseeable future. Such treatments are, however, of limited efficacy and toxic not only to tumor cells but also to normal tissues, leading to severe side-effects. The realization that radiation and essentially all genotoxic

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anticancer agents potently activate the ATM–Chk2 and ATR–Chk1 signaling pathways has stimulated much interest in whether these could be manipulated pharmacologically to enhance the efficacy of conventional therapies. Several considerations argue that this may be the case. Firstly, defects in DNA damage responses, whether inherent or experimentally imposed, can result in sensitization to genotoxic stress. Examples already mentioned include the acute radiosensitivity of ATM-deficient cells, the inherent vulnerability of BRCA1- and BRCA2-deficient cells to cross-linking agents, and the synthetic lethality that results when cells with HRR deficiency are subject to PARP inhibition. Secondly, enhanced DNA damage signaling has been linked to radio- and chemo-resistance in leukemia and gliomas (Bao et al., 2006; Nieborowska-Skorska et al., 2006), raising the possibility that this might be reversed. Finally, it has been argued that the frequent functional inactivation of p53 in human cancer creates a generic and exploitable distinction between tumor and normal cells in terms of checkpoint proficiency. In essence this hypothesis holds that loss of p53-mediated G1 arrest under conditions of genotoxic therapy will render tumor cells more dependent on checkpoints activated in S and G2 phases than their genetically normal counterparts. Obviously this strategy is predicated on the assumption that in tumor cells the primary function of these residual p53-independent checkpoints is protective; that is, that their inhibition will either escalate damage, promote the formation of more lethal lesions, or trigger some mechanism of cell death that would not otherwise occur in the absence of checkpoint suppression. Over the past decade considerable evidence has accumulated to support this concept. Thus, inhibition of Chk1 using either siRNA depletion or the selective chemical inhibitor, UCN-01, has been shown to potentiate cell killing by a wide range of genotoxic agents, including ionizing radiation, alkylating agents, nucleoside analogs, cisplatin, and topoisomerase inhibitors (Carrassa et al., 2004; Cho et al., 2005; Ganzinelli et al., 2008; Hirose et al., 2001; Karnitz et al., 2005; Koniaras et al., 2001; Wang et al., 1996; Yu et al., 2002). In many, although not all, of these studies Chk1 inhibition resulted in a greater degree of sensitization in tumor cells that were deficient for p53 than in their proficient counterparts, consistent with the idea that loss of G1 arrest indeed creates a therapeutic index. Sensitization has also been observed as a consequence of inhibiting ATM, ATR, and other downstream components involved checkpoint regulation such as the Cdk1-regulating kinases, Wee1 and Myt1 (Karnitz et al., 2005; Mukhopadhyay et al., 2005). In comparison, where tested, inhibition of Chk2 has in general been found to have little or no effect on cell survival under conditions of damage, emphasizing the fundamental functional distinction between Chk1 and Chk2 (Carrassa et al., 2004; Karnitz et al., 2005; Pan et al., 2009).

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Although these studies support the general principle that checkpoints are protective and help tumor cells to survive exposure to genotoxic stress, exactly how checkpoint suppression leads to cell death remains poorly understood. Evidence for amplified levels of damage, mitotic catastrophe with damaged or incompletely replicated DNA, and increased levels of apoptosis have all been reported (Carrassa et al., 2004; Cho et al., 2005; Ganzinelli et al., 2008; Hirose et al., 2001; Karnitz et al., 2005; Koniaras et al., 2001; Yu et al., 2002). This is likely to be a complex issue; however, since genotoxic agents with distinct mechanisms of action activate different combinations of DNA damage and replication checkpoint responses, while the effects of some genotoxins may also depend on cell cycle phase and thus vary among individual cells in a population. The cellular consequences of suppressing checkpoint responses and the mechanisms of induced cell death that contribute to sensitization are therefore likely to be equally variable and complex. Some of this complexity is illustrated by studies in Chk1 knockout DT40 lymphoma cells examining the relationship between checkpoint deficiency and cell survival under different conditions of genotoxic stress. Compared to wild-type, Chk1-deficient DT40 cells are markedly sensitive to ionizing radiation, the DNA polymerase inhibitor aphidicolin, and the nucleoside analog 5-fluorouracil (5-FU); however, the mechanism of cell death arising from checkpoint deficiency is different in each case. Thus, abrogation of the Chk1-dependent G2 checkpoint leads to division with lethal damage after irradiation, presumably because G2 arrest would normally provide a vital opportunity to repair such damage prior to mitosis (Zachos et al., 2003). By contrast, when DNA polymerase is blocked with aphidicolin, deficient fork stabilization/origin suppression and S-M checkpoints result in massive replication fork collapse and lethal premature entry to mitosis with unreplicated DNA (Zachos et al., 2003, 2005). 5-FU also inhibits DNA replication through its inhibitory action on thymidylate synthase (TS) leading to nucleotide pool depletion (Longley et al., 2003); however, sensitization in this case proved to stem from uncontrolled replication in the presence of drug due to loss of Chk1-mediated slowing of DNA synthesis, rather than fork collapse followed by premature entry to mitosis (Robinson et al., 2006). Failure to slow replication resulted in enhanced incorporation of 5-FU into cellular genomic DNA and a large increase in DSBs compared to Chk1 proficient cells (Robinson et al., 2006). Thus, the consequences of replication checkpoint suppression vary according to the nature of the genotoxic agent and the mechanism of DNA synthesis inhibition. The many preclinical “proof-of-principle” studies over the past decade or so have encouraged efforts within the pharmaceutical industry to develop drugs targeting the ATM–Chk2 and ATR–Chk1 pathways, some of which have already entered clinical trials. Thus far these efforts have concentrated

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mainly on Chk1, presumably in recognition of its role as the ultimate effector of both DNA damage and replication checkpoints and the fact that it is thought to be expressed and active in virtually all tumors (Dai and Grant, 2010). Drugs specifically targeting ATM and Chk2 are, however, also under development, although at an earlier stage, and also other checkpoint-regulating kinases such as Wee1 and Myt1 (Antoni et al., 2007; Ashwell et al., 2008; Dai and Grant, 2010). Thus far no selective inhibitors of ATR have been reported. UCN-01 was the first Chk1 inhibitor approved for clinical trials; however, undesirable pharmacological characteristics combined with lack of selectivity limited its utility and led to the development of several new and more selective agents (Ashwell et al., 2008; Dai and Grant, 2010; Fuse et al., 2005). Several of these have reached clinical trials while others remain under preclinical development. Current Chk1 inhibitors in Phase I clinical trials include AZD7762 (AstraZeneca), PF-00477736 (Pfizer), XL844 (Exelixis), and SCH 900766 (Schering-Plough). It is important to note that although each of these drugs was developed to inhibit Chk1, several also have significant, and in one case even greater, activity against Chk2. Whether the potential for dual inhibition of both Chk1 and Chk2 is relevant to the biological effects of these agents as reviewed below is currently unclear. Each of these agents has shown promising preclinical activities that recapitulate many of the effects of experimental Chk1 inhibition on DNA damage and replication checkpoint responses described above (Ashwell et al., 2008; Dai and Grant, 2010). In addition to monitoring effects on cell survival, several of these studies have sought evidence of drug efficacy in terms of Chk1 inhibition and to gain insight into potential mechanisms of synergistic cell killing. Biochemical readouts of Chk1 activity include turnover of Cdc25A phosphatase, phosphorylation of Cdc25 C on serine 216 (S216), and increased levels of inhibitory T14/Y15 phosphorylation of Cdk1. To document the biological consequences of Chk1 inhibition, nuclear foci of -H2AX and Rad51 provide a means of quantifying DNA damage and proficiency for HRR respectively, while premature entry to mitosis can be detected by flow cytometry when cells with incompletely replicated DNA become positive for histone H3 phosphorylated on serine 10 (pS10) (Zachos et al., 2005). AZD7762 is a potent ATP-competitive inhibitor of Chk1 and Chk2 that is currently in clinical trials in combination with gemcitabine and irinotecan for solid malignancies (Morgan et al., 2010; Zabludoff et al., 2008). In preclinical studies AZD7762 has been shown to synergize with ionizing radiation, irinotecan, and gemcitabine in a variety of tumor cell lines and xenografts with evidence of greater potency in p53-deficient cells (Morgan et al., 2010; Zabludoff et al., 2008). In each case AZD7762 treatment resulted in stabilization of Cdc25A, decreased levels of T14/Y15 phosphorylated Cdk1, and an increase in mitotic entry compared to DNA damaging

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agent alone, indicating efficient Chk1 inhibition and checkpoint override (Morgan et al., 2010; Zabludoff et al., 2008). Enhanced cell killing by AZD7762 in the context of ionizing radiation was also associated with increased and persistent -H2AX staining and a marked suppression of Rad51 focus formation, indicating that drug treatment resulted in higher levels of DNA damage and also likely resulted in suppression of HRR (Morgan et al., 2010; Zabludoff et al., 2008). Gemcitabine is an antimetabolite and DNA strand-terminator that inhibits DNA replication via inhibition of both ribonucleotide reductase and DNA polymerase, stalling replication forks and arresting cells in S-phase (Ewald et al., 2008). A more detailed analysis of gemcitabine chemosensitization revealed that AZD7762 treatment caused collapse of stalled replication forks, ectopic replication origin firing, and ultimately premature entry to mitosis with unreplicated DNA and apoptosis (McNeely et al., 2010). Interestingly, siRNA depletion of Cdk1 mitigated cell death under conditions of AZD7762 and gemcitabine treatment, suggesting that premature entry to mitosis as a result of S-M checkpoint failure was a direct cause of cell death (McNeely et al., 2010). Replication fork collapse also resulted in high levels of DSBs and ATM activation, and consistent with this, AZD7762 enhanced gemcitabine cytotoxicity particularly effectively in cells with DSB repair defects (McNeely et al., 2010). PF-00477736 is a potent ATP-competitive inhibitor of Chk1 (Ki 0.49 nM) with more modest activity towards Chk2 (Ki 47 nM) in vitro. It is in combination trials with gemcitabine for the treatment of advanced solid tumors (Ashwell et al., 2008; Dai and Grant, 2010). In preclinical studies PF-00477736 has been shown to abrogate both the G2 and intra-S-phase checkpoints in cells treated with camptothecin and gemcitabine respectively, and to enhance the cytotoxicity of gemcitabine and carboplatin in cell and xenograft assays (Blasina et al., 2008). Unexpectedly, PF-00477736 also synergizes strongly with docetaxel in cells and xenografts, releasing cells from mitotic arrest and enhancing apoptosis (Zhang et al., 2009). The mechanism of this synergy is not yet fully understood. High concentrations of docetaxel can induce DNA damage which could be a factor; however, other findings have shown that Chk1 is required for spindle checkpoint function and mitotic progression, raising the possibility that Chk1 inhibitors might enhance the effects of antimitotic drugs more generally (Carrassa et al., 2009; Peddibhotla et al., 2009; Zachos et al., 2007). XL-844 is a more potent inhibitor of Chk2 (Ki 0.07 nM) than Chk1 (Ki 2.2 nM). XL-844 is in combination trials with gemcitabine for the treatment of advanced solid tumors and lymphoma (Ashwell et al., 2008; Dai and Grant, 2010). In PANC-1 cells treated with gemcitabine XL-844 has been shown to override the S-phase checkpoint leading to increased levels of Chk1 phosphorylation (S317) and -H2AX, indicative of increased

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levels of damage, followed by subsequent premature entry into mitosis and decreased clonogenic survival (Matthews et al., 2007). Similarly, SCH900766 is a selective Chk1 inhibitor that is in combination trials with gemcitabine for the treatment of solid tumor and lymphoma and cytarabine for the treatment of acute leukemia. SCH-900766 has been shown to abrogate both the intra-S and G2 checkpoints resulting in sensitization of tumor cells to IR and alkylating agents, although full details of these effects are not yet in the public domain (Dai and Grant, 2010). Taken together, these studies validate Chk1 as a target whose pharmacological inhibition can potentiate tumor cell killing by a wide range of genotoxic agents in vitro. As depicted in Fig. 4, much remains to be learned about the detailed mechanisms involved in chemosensitization, however, Chk1 inhibition can clearly both amplify the extent of damage inflicted by a given agent and promote the formation of more lethal lesions, for example by triggering stalled replication fork collapse to form DSBs. In addition, evidence suggests that damage escalation as a result of Chk1 inhibition can enhance tumor cell killing both by conventional routes, for example by increasing apoptosis, but also by triggering novel mechanisms such as premature entry to mitosis with unreplicated DNA. Clearly, the challenge now will be to determine whether these preclinical principles established in the laboratory using novel Chk1 inhibitor drugs will have utility in the clinic.

IX. FUTURE PERSPECTIVES Genomic instability has long been recognized as a cardinal feature, and arguably an important cause of, cancer, however, in recent years it has also emerged as a potential Achilles heel that offers new therapeutic opportunities. Thus far this prospect has been most evident in cancers that arise in predisposed individuals, for example in BRCA1 and BRCA2 mutation carriers, where impairment of one particular form of DNA repair specifically in tumor cells creates sensitivity to both existing and novel treatments. Although similar loss-of-function mutations do not appear to be common in sporadic cancers, genomic instability is, perhaps because DNA damage response genes are inactivated epigenetically, or the proteins they encode are inhibited by oncogenic signaling processes. This raises the possibility that individual sporadic cancers might also be selectively targeted if the functional basis and consequences of genomic instability in individual tumors could be understood. Alternatively, it may be possible to improve the efficacy, or mitigate the undesirable side-effects, of existing genotoxic therapies by developing drugs that inhibit DNA damage responses controlled by the ATM–Chk2 and ATR–Chk1 pathways. Evidence suggests that inhibition of the Chk1 protein

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IR/topoisomerase inhibitors (DSBs)

ATM

Fork collapse leading to DSBs

DNA stand resection

ATR

Gemcitabine, 5-FU, Platinum compounds (stalled replication forks)

Chk1

Damage escalation, Suppression of HRR, Division with damage, Premature mitosis, Increased apoptosis

G2 arrest, Intra-S checkpoint, S-M checkpoint, Fork stabilization, Origin suppression,

Chk1 inhibition

Fig. 4 Chk1 inhibition potentiates cell killing by genotoxic agents by multiple mechansims Activation of Chk1 in response to DSBs and stalled replication forks in p53-deficient tumor cells triggers multiple cell cycle checkpoint responses that slow replication, delay the onset of mitosis, stabilize stalled replication forks, and suppress futile origin firing upon exposure ionizing radiation (IR) or a wide range of genotoxic anticancer agents.. Preclinical studies using siRNA depletion or a variety of selective inhibitor drugs, some of which are in clinical trials, has shown that Chk1 inhibition overrides these checkpoint responses leading to damage escalation and increased tumor cell killing through multiple mechanisms. Please refer to the text for further details and explanation.

kinase in particular may have therapeutic potential, particularly in tumors that have suffered loss of p53 function during their evolution. Such efforts are, however, in their infancy, and as understanding of the biological and molecular functions of these pathways deepens, additional rational therapeutic strategies based on genome stability defects will likely emerge.

ACKNOWLEDGMENTS The authors wish to thank Cancer Research-UK, the Beatson Institute for Cancer Research, and the Royal College of Radiologists (LMT) for financial support.

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microRNAs in Cancer: From Bench to Bedside Maria Angelica Cortez,* Cristina Ivan,* Peng Zhou,{ Xue Wu,z Mircea Ivan,z and George Adrian Calin* *Department of Experimental Therapeutics and The RNA Interference and non-codingRNA Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA { Department of Biological Science, Purdue University Calumet, Hammond, Indiana, USA z Department of Medicine, Microbiology and Immunology, Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, Indiana, USA

I. Introduction A. What Are miRNAs? B. miRNA Biogenesis and Mechanism of Action II. Alterations of miRNA Expression in Cancer III. Causes of miRNA Expression Variations A. Cancer Associated with Genomic Regions B. Mutations and Single-Nucleotide Polymorphisms C. Epigenetic Regulation of miRNA Expression D. Roles of Hypoxia-Inducible Factor and Hypoxia in miRNA Expression E. Regulation of miRNA Expression by Transcription Factors F. Regulation of miRNA Expression by Estrogens G. Posttranscriptional Regulation of miRNA Expression IV. Pathways Involving miRNA Alterations A. Self-Sufficiency in Growth Signals B. Insensitivity to Antigrowth Signals C. Evasion of Apoptosis D. Limitless Replicative Potential E. Angiogenesis F. Invasion and Metastasis V. Clinical Applications A. miRNAs Biomarkers for Cancer Diagnosis and Prognosis B. Potential Use of Circulating miRNAs in Cancer Diagnosis C. Therapy with miRNAs VI. Concluding Remarks References microRNAs (miRNAs) are master regulators of gene expression. By degrading or blocking translation of messenger RNA targets, these noncoding RNAs can regulate the expression of more than half of all protein-coding genes in mammalian genomes. Aberrant miRNA expression is well characterized in cancer progression and has prognostic implications for cancer in general. Over the past several years, accumulating evidence has demonstrated that genomic alterations in miRNA genes are correlated Advances in CANCER RESEARCH Copyright 2010, Elsevier Inc. All rights reserved.

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with all aspects of cancer biology. In this review, we describe the effects of miRNA deregulation in the cellular pathways that lead to the progressive conversion of normal cells into cancer cells as well as in cancer diagnosis and therapy in humans. # 2010 Elsevier Inc.

I. INTRODUCTION A. What Are miRNAs? microRNAs (miRNAs) are short (19–24 nt) noncoding RNAs (ncRNAs, RNAs that do not encode proteins) that play important roles in posttranscriptional gene silencing of target messenger RNAs (mRNAs) (Bartel, 2004). miRNAs are involved in virtually all biological processes, such as cell proliferation and apoptosis, development, differentiation, metabolism, immunity, neuronal patterning, stress response, aging, and cell-cycle control (Ambros and Lee, 2004; Bartel, 2004; He and Hannon, 2004; Kato and Slack, 2008; Plasterk, 2006). miRNAs are strongly conserved among invertebrates, vertebrates, and plants (Ambros, 2003), and researchers have identified more than 700 miRNAs in humans (Griffiths-Jones et al., 2008). More than 70% of miRNAs are transcribed from individual miRNA genes, introns or exons of protein-coding genes, or polycistronic transcripts that encode related miRNAs (Lee et al., 2004). Investigators have estimated that more than 50% of all protein-coding genes are regulated by miRNAs in mammalian genomes (Friedman and Jones, 2009; Lewis et al., 2005).

B. miRNA Biogenesis and Mechanism of Action An miRNA is transcribed in the nucleus as a long, capped, polyadenylated precursor primary precursor (pri-miRNA) by RNA polymerase II or III (Lee et al., 2002; Zeng et al., 2003). The resulting pri-miRNA is processed by the ribonuclease (RNase) III Drosha and the double-stranded DNA-binding protein DGCR8/Pasha (Ambros and Lee, 2004) to form a precursor miRNA (pre-miRNA) (Lee et al., 2003). The nuclear export receptor exportin 5/Ran GTP (Lund et al., 2004; Yi et al., 2003) actively transports pre-miRNAs to the cytoplasm, where they are processed by the RNase III endonuclease Dicer along with the double-stranded transactivation-responsive RNA-binding protein (TRBP), resulting in a small double-stranded RNA structure ( 22 nt). This miRNA duplex is unwound into mature single-stranded form and incorporated into the RNA-induced silencing complex (RISC), which guides the complex into the complementary 30 -untranslated region (UTR) of the target mRNA (Gregory et al., 2006). However, authors recently reported

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that miRNAs can also target the 50 -UTRs of target mRNAs and open reading frames as well as promoter regions (Kloosterman, et al., 2004; Lee et al., 2009; Lytle et al., 2007; Place et al., 2008). Negative regulation of gene expression occurs via either mRNA cleavage, when it is perfectly complementary to the 30 UTR of the target mRNA, or translational repression in cases of partial complementarity (Fig. 1; Bohnsack et al., 2004; Gregory et al., 2006; He and Hannon, 2004). In mammals, regulation mediated by miRNAs is accomplished by imperfect base pairing along with protein translational repression of the target gene (Mathonnet et al., 2007; Petersen et al., 2006). In addition, studies demonstrated that miRNAs can upregulate the expression of their targets; for example, miR-369-3p upregulates tumor necrosis factor-a (TNFa) expression (Vasudevan et al., 2007). Many miRNAs exhibit diverse temporal and spatial expression patterns. Additionally, the relative level of expression of a particular miRNA can vary by several orders of magnitude depending on the cell type. Researchers have developed combined experimental and computational methods to determine when, where, and in what quantity a specific miRNA exists and identify its biological function. This miRNA analysis is performed in two steps. First, the level of miRNA expression is measured using one of the several available high-throughput technologies (e.g., microarray, real-time polymerase chain reaction, microbeads analysis). Second, the miRNA expression is clustered to distinguish biologically meaningful information that can be used to classify and identify specific molecular pathways for a given disease. Because a single miRNA can target hundreds of mRNAs, aberrant miRNA expression is capable of disrupting the expression of several mRNAs and proteins (Chin and Slack, 2008). Therefore, alterations in miRNA expression are involved in the initiation of many diseases, including cancer.

II. ALTERATIONS OF miRNA EXPRESSION IN CANCER Initially identified in cases of B cell chronic lymphocytic leukemia (CLL) (Calin et al., 2002), investigators have since detected miRNA alterations in many types of human tumors. Researchers have broadly applied genomewide miRNA expression profiling using high-throughput technologies such as microarrays in the study of several cancer types. Based on the results of these studies, authors have reported disease-specific expression profiles with important diagnostic and prognostic implications in many human cancers, including B cell CLL (Calin et al., 2004), breast carcinoma (Iorio et al., 2005), primary glioblastoma (Ciafre et al., 2005), hepatocellular carcinoma (Murakami et al., 2006), papillary thyroid carcinoma (He et al., 2005), lung cancer (Yanaihara et al., 2006), gastric and colon carcinomas

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Pri-miRNA

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Fig. 1 miRNA biogenesis and mechanism of action. miRNAs are first transcribed by RNA polymerase II or III in the nucleus as primary transcripts (pri-miRNAs) and then processed by the RNase III Drosha and the double-stranded DNA-binding protein DGCR8 to produce premiRNAs. The pre-miRNAs (hairpins) are actively transported to the cytoplasm by exportin 5/Ran-GTP. In the cytoplasm, pre-miRNAs are processed by the RNase III endonuclease Dicer along with the TRBP, yielding a small double-stranded RNA (miRNA: *miRNA). The mature single-stranded miRNA (*miRNA) is incorporated in the RISC, which is guided to the complementary 30 -UTR of the target mRNA. miRNA-negative regulation occurs via either mRNA cleavage or translational repression.

(Michael et al., 2003), and endocrine pancreatic tumors (Volinia et al., 2006). In addition, miRNA expression profiles have displayed signatures related to tumor classification, diagnosis, and disease progression and have proven useful in determining the primary site for cancers of unknown origin (Calin and Croce, 2006; Lu et al., 2005; Rosenfeld et al., 2008; Yanaihara et al., 2006).

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miRNAs reportedly function as either oncogenes or tumor suppressors (Esquela-Kerscher and Slack, 2006). For instance, miR-10b, which is highly expressed in metastatic breast cancer cells, is a known oncogenic miRNA that suppresses HOXD10, which increases the expression of RHOC, a gene associated with tumor cell proliferation and metastasis (Ma et al., 2007). Also, researchers identified miR-21 as a potentially oncogenic miRNA whose expression is upregulated in various solid tumors as well as hematological malignancies (Krichevsky and Gabriely, 2009). miR-21 regulates important suppressor genes, such as PTEN (Meng et al., 2007) and PDCD4 (Asangani et al., 2008). In addition, not only single transcribed miRNAs but also clusters of miRNAs, such as miR-1792 (miR-17, miR18a, miR-19a, miR-20a, miR-19b-1, and miR-92-1), enhance tumorigenicity. Interestingly, miR-1792 is located on 13q31.3, a chromosomal region amplified in diffuse large B cell lymphomas (DLBCLs), follicular lymphomas, Burkitt lymphoma, and lung carcinomas (Ota et al., 2004). Furthermore, miR-1792 has a pleiotropic function, as it is able to promote proliferation, increase angiogenesis, and sustain cell survival via posttranscriptional repression of a number of target mRNAs (Olive et al., 2010). On the other hand, studies have identified several miRNAs that act as tumor suppressors. Among the most well-characterized tumor suppressor miRNAs are the miR-34 family members, which are important effectors of TP53 activation (Bommer et al., 2007; Chang et al., 2007). Ectopic expression of miR-34 genes has promoted cell-cycle arrest, induced cellular senescence, and inhibited proliferation (Hermeking, 2010). Also, expression of members of the let-7 family of tumor suppressor miRNAs is downregulated in many malignancies and inhibits cancer growth by targeting various oncogenes, such as RAS, and inhibiting key regulators of several mitogenic pathways, such as HMGA2 (Johnson et al., 2005; Peter, 2009). However, the initial categorization of miRNAs as oncogenes or tumor suppressor genes based on their levels of expression in tumors versus normal tissues has proven to be inaccurate, as experiments have shown that many of them have dual natures as both oncogenes and tumor suppressor genes according to cancer type. Nonetheless, further studies should elucidate the nature of deregulation of miRNA expression as well as its role in tumorigenesis.

III. CAUSES OF MIRNA EXPRESSION VARIATIONS Over the past few years, investigators have made much progress with respect to understanding the regulatory mechanisms of specific miRNAs. Currently, we can assume that the expression of virtually every miRNA is regulated and finely tuned by a variety of transcription factors in a fashion

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similar to the effect of regulatory mechanisms on the expression of conventional genes. In this section, we concentrate on regulatory mechanisms relevant to malignant cells, particularly specific transcription factors directly involved in tumorigenesis.

A. Cancer Associated with Genomic Regions Genomic variation in miRNA genes can affect their processing and, consequently, their ability to properly regulate the expression of target genes. miRNAs are frequently located in cancer-associated genomic regions (CAGRs) that are often subject to rearrangements, breakpoint regions, loss of heterozygosity sites, deletions, and amplifications in cancer cells and are aberrantly expressed in a variety of malignancies (Calin et al., 2004). The first evidence of the involvement of miRNAs in cancer came in a study of miR-15a and miR-16a, located on chromosomal region 13q14, which is deleted in more than half of all B cell CLL cases (Calin et al., 2002). miR-15a and miR-16a induce apoptosis by targeting the mRNA of the antiapoptotic BCL2 gene (Cimmino et al., 2005). Also, copy-number changes for some miRNA genes are common to several tumor types, such as ovarian cancer, breast cancer, and melanoma, whereas other such copy-number changes are unique to specific tumor types (Zhang et al., 2006). A study demonstrated frequent, marked overexpression, with occasional gene amplification, of the miR-1792 cluster in intron 3 of C13orf 25 gene on 13q31.3 in lung cancer cases (Hayashita et al., 2005). Moreover, specific miRNA expression signatures have proven to be associated with specific translocations in hematopoietic malignancy and solid tumor (Dixon-McIver et al., 2008; Garzon et al., 2008; Varambally et al., 2008). For example, the fusion gene AML1/ETO, which is produced by the t(8;21) translocation, promotes heterochromatic silencing of pre-miR-223 in patients with leukemia (Fazi et al., 2007).

B. Mutations and Single-Nucleotide Polymorphisms Although single-nucleotide polymorphisms (SNPs) are rare in miRNA genes, they can affect miRNA function in pri-miRNA transcription, primiRNA and pre-miRNA processing, and miRNA and mRNA binding sites (Saunders et al., 2007; Wu et al., 2009). In addition, several studies indicated that some SNPs in both miRNA genes and miRNA target genes increase the risk of certain cancers. Initially, investigators discovered a mutation in the miR-189 binding site of SLIT and SLITRK1, which is associated with Tourette syndrome (Abelson et al., 2005). Afterward, other studies demonstrated an association between the presence of SNPs in miRNA genes and

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cancer risk. Recent studies demonstrated that the presence of SNPs in primiRNAs is related to processing and the level of expression of mature miRNAs, such as that occurs in pri-miRNA regions of let-7e and miR-16 (Nicoloso et al., 2010; Ryan et al., 2010). Also, the presence of the SNP rs531564 in pri-miR-124-1 was associated with increased bladder and esophageal cancer risk (Yang et al., 2008; Ye et al., 2008). Researchers also showed an association of the pre-miR-196a-2 SNP rs11614913 with breast cancer risk (Hoffman et al., 2009). In contrast, the pre-miR-27a SNP rs895819 decreases the risk of breast cancer (Kontorovich et al., 2010). Furthermore, miRNA: mRNA base pairing is crucial in driving miRNAs toward target genes. Increasing evidence shows that SNPs can abolish or create new binding sites. For example, researchers found an SNP in binding sites in the complementary sites of let-7 in the 30 -UTR of KRAS gene, increasing the risk of lung cancer in moderate smokers (Chin et al., 2008). Importantly, the SNP rs2910164, which is located in the 3p strand of miR146a, is an example of a functional SNP in miRNAs. It promotes mispairing in the hairpin of the precursor, altering the expression of miR-146a and leading to an increased risk of papillary thyroid carcinoma (Jazdzewski et al., 2008). Alterations in miRNA expression caused by sequence variations such as SNPs may be another important factor contributing to cancer predisposition. Moreover, because specific miRNAs have numerous targets, inherited SNPs in miRNA genes may have important consequences on the expression of various target oncogenes and tumor suppressor genes involved in cancer pathogenesis. Nevertheless, examination of the impact of miRNA gene SNPs on cancer risk is only just a beginning, and new findings should elucidate the potential of these variations in affecting human cancer prognosis and progression.

C. Epigenetic Regulation of miRNA Expression DNA hypermethylation of tumor suppressor genes, global genomic hypomethylation, and aberrant histone modifications are the most common hallmarks of epigenetic alterations associated with cancer (Herman and Baylin, 2003). Emerging evidence indicates that epigenetic mechanisms contribute to the aberrant expression of miRNAs in cancer cells, especially the transcriptional inhibition of tumor suppressor miRNAs. Silencing of miRNAs with tumor-suppressive roles by epigenetic mechanisms includes promoterassociated CpG island methylation and repressive histone modifications (Agirre et al., 2009). Chim et al. (2010) found that the miR-34a promoter is methylated in 75% of lymphoma and 37% of melanoma cell lines compared with its unmethylated status in normal controls. Expression of miR124a is reduced in acute lymphoblastic leukemia (ALL) by hypermethylation

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of promoter and histone modifications (Agirre et al., 2009). Epigenetic silencing of specific miRNAs induces overexpression of their targeted oncogenes. For example, epigenetic silencing of miR-124a in acute lymphoblastic leukemia cells increases CDK6 expression, which contributes to abnormal proliferation of the cells via phosphorylation of retinoblastoma 1 (RB1) (Agirre et al., 2009). Another study showed that increased SOX4 expression in endometrial cancer cells is caused by aberrant methylation of the miR129-2 promoter and that restoration of this miRNA expression is associated with decreased SOX4 expression and reduced proliferation of cancer cells (Huang et al., 2009b). Like the widely discussed miRNA signatures in cancer, a similar concept regarding epigenetic miRNA signatures in cancer may contribute to its diagnosis and prognosis. Lujambio and colleagues proposed that the miRNA hypermethylation profile may be used to characterize tumor metastasis and found that the hypermethylation of miR-148a, miR-34b/c, and miR-9 is significantly associated with metastasis (Lujambio et al., 2008). Similar to the transcript factor-miRNA regulatory feedback loop, recent studies showed that some important parts of epigenetic machinery, including DNA methyltransferases, histone deacetylases, and histone methyltransferases, are direct targets of miRNAs. For example, authors reported that miR-29b induced global hypomethylation in acute myeloid leukemia (AML) by directly targeting DNMT3A and DNMT3B and indirectly targeting DNMT1 (Garzon et al., 2009). Also, DNMT3 is a direct target of miR-143, which is frequently downregulated in colorectal cancer cells. Restoration of miR-143 expression in colorectal cancer cells reduced their growth and colony formation in a soft agar assay (Ng et al., 2009b). The discovery that methylation is implicated in miRNA expression opens up the possibility of future use of epigenetic drugs as DNA-demethylating agents in cancer therapy.

D. Roles of Hypoxia-Inducible Factor and Hypoxia in miRNA Expression Hypoxia is a central feature of the cancer microenvironment (Harris, 2002) and well-documented contributor to the development of resistance to antineoplastic therapy (Giaccia et al., 2004; Semenza, 2004). The hypoxia-inducible factor (HIF) family of transcriptional regulators is widely acknowledged to coordinate molecular mechanisms of response to oxygen deprivation by directly regulating the expression of hundreds of genes. Recent data suggest that the wide spectrum of hypoxia- and HIF-triggered responses extend beyond protein-encoding genes. Increasingly, groups have

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reported hypoxia-regulated miRNAs, including miR-210, miR-373, miR103, miR-24-1, miR-181c, miR-26b, and miR-26a-2 (Camps et al., 2008; Crosby et al., 2009; Fasanaro et al., 2008; Kulshreshtha et al., 2007). Although at least one study has shown that more than 20 miRNAs respond to hypoxia (Kulshreshtha et al., 2007), miR-210 stands out as the common denominator in all the reported studies. Indeed, hypoxic induction of miR-210 is not limited to transformed cells, as this miRNA is also a key player in the response of endothelial cells to low levels of oxygen tension (Fasanaro et al., 2008), which affects angiogenesis. Apart from being the prototypical miRNA modulated by oxygen, miR-210 is very likely to significantly impact clinical outcomes of a variety of cancer types. Virtually universally overexpressed in tumor cells, especially in breast, pancreatic, and head and neck cancers, miR-210 expression is strongly correlated with the hypoxia metagene expression in vivo and negatively affects clinical outcomes (Camps et al., 2008). Studies have indicated that HIF1A is the leading candidate regulator of hypoxia-responsive miRNAs, particularly miR-210 and miR-373 (Camps et al., 2008; Crosby et al., 2009; Fasanaro et al., 2008; Huang et al., 2009a; Kulshreshtha et al., 2007). The researchers in these studies employed multiple strategies, including transduction of active forms of HIFs and, conversely, inactivation by using short hairpin RNA lentiviruses or small interfering RNA duplexes. Additionally, chromatin immunoprecipitation analysis indicated recruitment of endogenous HIF1 to specific hypoxia response element (HRE) sequences in the miR-210 promoter, and luciferase-based reporters driven by fragments of select HRE promoters. Similar findings were reported for miR-373, another miRNA widely overexpressed in cancer cells (Crosby et al., 2009). Consistent with HIF role in the expression of miRNAs, miR-210 is particularly overexpressed in clear cell renal cell carcinoma cases (Juan et al., 2010). These tumors are known to have abnormally high levels of HIF expression because of genetic inactivation of the tumor suppressor VHL (Ivan and Kaelin, 2001). What are the biological and biochemical implications of upregulation of miR-210 expression induced by hypoxia? Although relevant data are just now emerging, several groups have reported that miR-210 links hypoxia with reactive oxygen species generation, decreased Krebs cycle activity, and electron transport in mitochondria via downregulation of iron-sulfur cluster scaffold homolog (ISCU) expression (Chen et al., 2010; Fasanaro et al., 2009; Favaro et al., 2010). ISCU is critical for the assembly of FeS clusters at least at the level of mitochondrial complex 1 and aconitase enzyme activity; therefore, downregulation of ISCU expression in response to miR210 overexpression results in decreased mitochondrial energy metabolism and increased reliance of glycolysis. The importance of this pathway is supported by clinical data showing that a variety of cancer types with low ISCU and high miR-210 expression exhibit worse prognoses (Favaro et al., 2010).

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Other metabolic players are emerging, such as the phosphate dehydrogenase GPD1L (Fasanaro et al., 2009). However, their roles in the response to hypoxia remain elusive. Thus, these studies were the first to show that miR-210 is a nodal point linking the microenvironment, metabolism, and the clinical course in cancer cases.

E. Regulation of miRNA Expression by Transcription Factors Not surprisingly, some of the most important positive regulators of prooncogenic miRNAs expression are transcription factors encoded by protooncogenes. One of the best documented cases of this regulation involves MYC and the miR-1792 cluster. MYC upregulates the expression of this cluster, and this mechanism contributes to robust angiogenesis and growth in tumors. Although the picture is far from complete, miR-1792 seems to exert these effects largely by targeting antiangiogenic thrombospondin 1 and related proteins (Dews et al., 2006). Another target of miR-1792 relevant to cancer is the transcription factor E2F1. MYC activation of miR-1792 leads to downregulation of E2F1 expression, providing a regulatory loop potentially aimed at limiting MYC-triggered proliferation (O’Donnell et al., 2005). E2F1 has been at the center stage of research on miRNA expression regulation. Recently, multiple studies reported the existence of negative regulatory loops between all E2F family members and several miRNAs as a safety mechanism for prevention of excessive proliferation (Coller et al., 2007; O’Donnell et al., 2005; Sylvestre et al., 2007; Woods et al., 2007). One study of different subtypes of AML showed a mutual negative feedback loop between E2F1 and miR-223 involved in granulopoiesis (Pulikkan et al., 2010). E2F1 inhibits miR-223 transcription, whereas repression of E2F1 mediated by miR-223 prevents myeloid cell-cycle progression (Pulikkan et al., 2010). Authors reported on another feedback loop in gastric cancer cases in which the E2F1-induced miR-106b25 oncogenic cluster inhibits E2F1 expression (Petrocca et al., 2008). Regulation of miRNAs by transcription factors in cancer cells occurs in a cancer- and tissue-specific fashion, one example being induction of miR-449a/b by E2F1 in testes, lungs, and trachea but rarely in other cancer cells (Lize et al., 2010). Because it is the most frequently mutated transcription factor in cancer cells, the fact that TP53’s impact on miRNA expression has been a focus of intensive investigation is hardly surprising. The first documented TP53induced miRNAs were the members of the miR-34 family, which have evolutionarily conserved TP53 binding upstream of the coding sequences

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(Corney et al., 2007; He et al., 2007; Tarasov et al., 2007). miR-34 family members are increasingly viewed as more than bystanders during TP53 activation, with involvement in reprogramming of critical gene expression and regulation of apoptosis and the cell cycle. Thus, these miRNAs may account in part for well-established biological effects of TP53, potentially to an extent similar to classic targets of this tumor suppressor. In fact, Guessous et al. (2010) reported evidence that miR-34a acts as a bona fide suppressor by downregulating the expression of oncogenes such as MET and NOTCH1 1 and 2 and inhibiting glioma xenograft growth. Following the discovery of an miR-34–based response to TP53 activation, studies identified additional miRNAs that behave in a similar fashion. For example, expression of the homologous miRNAs miR-192 and miR-215 is upregulated in a TP53-dependent manner after exposure to genotoxic stress and lower in colon tumors than in normal colon tissue (Braun et al., 2008), potentially reflecting loss of wild-type TP53. miR-192 and miR-215 induce cell-cycle arrest by coordinately targeting several transcription factors involved in mediation of G1-S and G2-M checkpoints, which is consistent with their status as biologically relevant targets of TP53. Also, investigators showed that these TP53-induced miRNAs were involved in TP53 regulation of hypoxia signaling (Boominathan, 2010; Yamakuchi et al., 2010). For example, miR-107 is an miRNA with TP53-induced expression in colon cancer cells that potentially suppresses hypoxia signaling, tumor angiogenesis, and growth by targeting hypoxia-inducible factor HIF1B. Consistently, in human colon tumor specimens, expression of miR-107 has been inversely associated with expression of HIF1B. Although activation of miRNAs by TP53 has been extensively studied, the repression of particular miRNAs can be relevant to the function of TP53. Reports demonstrated that TP53 inhibits the level of miR-1792 cluster transcripts under hypoxic conditions, and overexpression of these transcripts significantly suppresses hypoxia-induced apoptosis. Yan and colleagues identified relevant TP53 and TATA-binding protein binding sites in miR-1792, observing that transcriptional repression results from competition for binding sites between the two factors (Yan et al., 2009). In addition to specific transcriptional regulation of miRNA expression, recent data indicated that TP53 broadly affects miRNA expression levels (Suzuki et al., 2009). Specifically, TP53 interacts with the Drosha processing complex by associating with the RNA helicase p68 and facilitates the processing of pri-miRNAs to pre-miRNAs with growth-suppressive functions, including miR-16-1, miR-143, and miR-145. Transcriptionally inactive TP53 mutants interfere with functional assembly of the Drosha complex with RNA helicase p68, leading to attenuation of miRNA processing activity and thus potentially contributing to a reported general decrease in miRNA expression in cancer cells. Also, preliminary evidence indicates that TP53 as well as the

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related tumor protein p73 and tumor protein p63 interact with and consequently regulate the major components of miRNA processing, including Drosha-DGCR8, Dicer-TRBP2, and Argonaute proteins. Additionally, promoters of Dicer and retinoblastoma-binding protein 6 contain candidate TP53-response elements (Boominathan, 2010). Such miRNAs, and potentially other noncoding transcripts, continue to expand the already complex TP53 network, and their role will likely become more apparent in the near future. Which, if any, of the TP53-responsive miRNAs are essential for the function of TP53 as a tumor suppressor and guardian of the genome remains to be established.

F. Regulation of miRNA Expression by Estrogens Estrogens are widely accepted as major contributors to breast cancer development. Ligand-activated estrogen receptor (ER) a and b regulate transcription by directly binding to estrogen response elements located upstream of the target genes or indirectly by tethering to nuclear proteins such as JUN and Sp1 transcription factor (Kushner et al., 2000). Several miRNA microarray analyses have revealed specific, although somewhat discrepant, miRNA expression patterns after estrogen-based treatment of ERa-positive breast cancer cell lines (Bhat-Nakshatri et al., 2009; Castellano et al., 2009; Kovalchuk et al., 2007; Maillot et al., 2009). Some estrogenregulated miRNAs are associated with estrogen response elements, whereas several others are located in the intergenic regions of estrogen-regulated genes. A few miRNAs are regulated by secondary estrogen responses via estrogen-regulated transcript factors and are likely associated with epigenetic alteration (Bhat-Nakshatri et al., 2009). In studies of chronic (6–12 weeks) exposure to estradiol (E2), in a mammary carcinogenesis model in female rats (Kovalchuk et al., 2007), expression of a group of miRNAs (including miR-22, miR-99a, miR-127, miR-29c, and miR-499) was downregulated, whereas expression of miR-20a/b, miR-21, miR-17-5p, and miR-106a/b was upregulated. Interestingly, following even longer exposure to E2, the spectrum of miRNA expression changed significantly, as expression of only miR-139 was downregulated, whereas expression of miR-21, miR-103, miR-107, miR-129-3p, and miR-148a was upregulated. In a study on human breast cancer cells, Maillot et al. (2009) noted that 23 of 125 miRNAs tested were repressed in an E2-dependent manner in MCF-7 cells after treatment with E2. Of note, several E2-repressed miRNAs, especially miR-26a and miR-181a, also suppressed E2-dependent cell proliferation.

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Expression of one of the best documented cancer-associated miRNAs, miR-21, is significantly upregulated in ERa-positive breast cancer cells (Iorio et al., 2005). However, the direct impact of E2 on its expression is still controversial, as different groups have reported contrasting effects of this exposure (Bhat-Nakshatri et al., 2009). A variety of additional factors seem to be involved in the effects of estrogens on miRNA expression, such as the AKT (Bhat-Nakshatri et al., 2009). Additionally, upregulation of primiR-1792 under E2 stimulation is thought to be mediated by direct interaction between MYC and its promoter in an E2-dependent manner (Castellano et al., 2009).

G. Posttranscriptional Regulation of miRNA Expression In addition to specific transcription factors, the multistep miRNA maturation process can be targeted by regulatory mechanisms. Frequently observed lack of correlation among expression of pri-miRNAs, premiRNAs, and mature miRNAs indicates the existence of an extensive posttranscriptional regulation mechanism (Thomson et al., 2006). Early evidence of such mechanisms emerged with a study showing that Dicer processing of pre-miR-138-2 was blocked by an unknown inhibitory factor in the cytoplasm (Obernosterer et al., 2006). In another study, Dicer processing was blocked by nuclear sequestration of pre-miRNA in miR-31, miR-105, and miR-128a in several cancer cell lines (Lee et al., 2008). Drosha processing of primary let-7 is selectively inhibited in embryonic cells by the RNA-binding protein Lin-28, which interacts with let-7’s conserved loop region (Newman et al., 2008; Viswanathan et al., 2008). Widespread downregulation of miRNA expression caused by blockade during Drosha processing has occurred in mice during their development and in a wide range of primary tumors (Thomson et al., 2006). Furthermore, Argonaute, a well-known RISC slicer with RNase activity, is reported to be involved in miRNA posttranscriptional regulation via its enhancement of the production or stability of mature miRNAs (Diederichs and Haber, 2007). The global efficiency of miRNA biogenesis can be affected by well-known physiological or pathological factors, and large-scale alterations in posttranscriptional regulation of miRNA expression may contribute to cancer development. A high cell density can globally activate miRNA biogenesis in both nontransformed and cancer cells. This broad enhancement of miRNA expression is associated with elevated processing of pri-miRNAs by Drosha and increasingly efficient incorporation of mature miRNAs into RISC (Hwang et al., 2009). Kumar et al. (2007) reported that global repression of miRNA maturation by infection with short hairpin RNAs targeting

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components of miRNA processing machinery (Drosha, DGCR8, and Dicer1) promoted cellular transformation and tumorigenesis in human cancer cell lines and animal models, which was consistent with observations that cancer cells exhibit generally reduced expression of miRNAs.

IV. PATHWAYS INVOLVING miRNA ALTERATIONS Recent reports suggested that multiple miRNAs work in concert to regulate related targets in common pathways. Indeed, genes with diverse functions in multiple pathways can be simultaneously regulated by miRNAs. miRNA expression is globally lower in cancer cells than in normal tissue cells; thus, aberrantly expressed miRNAs act in cross-talk pathways to promote tumorigenesis. Tumorigenesis is a multistep process during which cancer cells acquire characteristics such as self-sufficiency in growth, insensitivity to growth-inhibitory signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, invasion, and metastasis (Hanahan and Weinberg, 2000). Herein, we review the participation of miRNAs in these processes in cancer cells.

A. Self-Sufficiency in Growth Signals Cancer cells constitutively activate different pathways that sustain cell proliferation and survival, making them independent from extracellular growth factor signals. Interestingly, modulation of cancer cell interactions with their microenvironments is necessary for cancer self-sufficiency in growth signals (Guo et al., 2006). Deregulation of miRNA expression in cancer cells can result in aberrant regulation of growth factor and receptor expression during growth signaling. One of the most well-established pathways by which cancer cells avoid growth factor dependency is activation of RAS signaling. RAS is a key molecule in cellular growth-regulatory pathways and is mutated in several types of malignancies. Importantly, RAS is regulated by let-7, one of the first miRNAs identified, whose expression is downregulated in many cancers (Johnson et al., 2005). Interestingly, let-7 targets the oncogene HMGA2 (Lee and Dutta, 2007), which contributes to the growth of cancer cells in an anchorage-independent manner. Researchers showed that underexpression of let-7 is an indicator of poor prognosis for lung cancer (Esquela-Kerscher and Slack, 2006) and head and neck squamous cell carcinoma (Childs et al., 2009). Also, downregulation of RAS by treatment with all-trans retinoic acid relies on transcriptional induction of let-7 expression by NFKBIA gene enhancer in AML (Garzon et al., 2007).

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Moreover, let-7 has induced tumor regression in in vivo lung cancer models (Esquela-Kerscher et al., 2008). These studies suggest clinical relevance for let-7 because of antiproliferative effects and potential in cancer therapy development. In addition to let-7, miR-21 is related to RAS oncogenic signaling. miR-21 is induced by the transcription factor complex JUN, which participates in RAS downstream signaling and is negatively controlled by the miR-21 target PDCD4 (Talotta et al., 2009). Therefore, induction of miR-21 expression by JUN represents a positive feedback loop that sustains JUN activity in response to RAS signaling (Talotta et al., 2009). miR-143 targets the RAS family member KRAS, suppressing cell proliferation (Chen et al., 2009). KRAS is mutated in various malignancies, and studies showed that its expression was inversely correlated with miR143 expression (Chen et al., 2009). Interestingly, by inhibiting KRAS expression, miR-143 also inhibits constitutive phosphorylation of ERK1/2 (Chen et al., 2009), which is located in an important pathway of cellular growth signal transduction. Authors reported that by repressing the oncogene ERBB2/3, miR-125a, and miR-125b also negatively regulate ERK1/2 and AKT phosphorylation (Scott et al., 2007). Also, miR-143, along with miR145, targets ERK5, another member of the ERK family. ERK5 is known to promote cell growth and proliferation in response to growth factors and tyrosine kinase activation. In addition, the activity of several transcription factors, such as MYEF2, FOS, FOSL1, PSAP, MYC, and NFKB1, are regulated by ERK5 (Terasawa et al., 2003). In addition to acting on ERK5 expression, miR-145 suppresses the insulin receptor substrate IRS1, a docking protein for IGF1R that plays a critical role in transformation events by functioning as an antiapoptotic agent to enhance cell survival (Shi et al., 2007). Growth signaling involves the interaction of growth factors and/or cytokines with transmembrane receptors. Cancer cells overexpress surface receptors and consequently develop hypersensitivity to growth factors at low concentrations. As demonstrated with growth factors, miRNA expression deregulation can result in aberrant of cell surface receptors. Investigators recently demonstrated that miR-205 targets ERBB3, a member of the tyrosine kinase receptor (TKR) family, and inhibits activation of the downstream mediator AKT (Iorio et al., 2009). Interestingly, researchers showed that miR-7 also suppresses AKT activation, which plays a critical role in EGF signaling (Webster et al., 2009). Indeed, miR-7 targeted this receptor, which frequently is mutated or exhibits upregulated expression in cancer cells. ERBB2 is another important receptor that is overexpressed in several cancers whose expression is regulated by miR-331. Consequently, by regulating the expression of this receptor, miR-331 also blocks the downstream PI3K and AKT signaling pathways (Epis et al., 2009). Furthermore, members of the miR-34 family and miR-199 suppress MET receptor expression, which is

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related to this oncogenic pathway in papillary renal carcinoma cells (Migliore and Giordano, 2008). Finally, miR-140 targets the expression of PDGF growth factor receptor, which is a known oncogenic factor, especially in ovarian cancer (Eberhart et al., 2008).

B. Insensitivity to Antigrowth Signals The ability of cancer cells to become insensitive to antigrowth signals is associated with alterations in the mechanisms that regulate the cells’ transit through the G1 phase of the cell cycle. One of the most important regulators of antigrowth signals is TGFB1. Besides controlling several pathways, TGFB1 prevents phosphorylation and inactivation of the tumor suppressor RB1 (Hannon and Beach, 1994). Dephosphorylation of proteins in the RB family promotes growth arrest via sequestration of E2F and inhibition of cell cycle progression (Iaquinta and Lees, 2007). Studies demonstrated that expression of TGFB1 and RB1 is regulated by miR-20a and miR-106a, respectively (Volinia et al., 2006). Interestingly, E2F’s transcription factor activity is also controlled at the posttranscriptional level by miR-20a along with miR-17-5p, miR-92, and miR-106b (O’Donnell et al., 2005; Petrocca et al., 2008; Sylvestre et al., 2007). miR-20a, miR-106a, and miR-106b are members of the highly homologous clusters miR-1792, miR-106a92, and miR-106b25, respectively (Tanzer and Stadler, 2004). Reciprocally, E2F-activating transcription factors can regulate the expression of these clustered miRNAs, which target apoptotic and growth-inhibitory proteins such as BCL2L11 (apoptosis facilitator) and CDKN1A (p21) (Petrocca et al., 2008; Sylvestre et al., 2007; Woods et al., 2007). Furthermore, miR1792 is important for integration of signals during the G1 phase of the cell cycle, protecting cells against MYC-induced apoptotic E2F responses and leading to uncontrolled cellular proliferation (Coller et al., 2007). Researchers recently demonstrated that let-7a induces cell-cycle arrest at the G1/S phase by suppressing E2F2 and cyclin D2 expression in prostate cancer cells (Dong et al., 2010). Other clusters, such as miR-106b93 and miR-221-222, are also involved in the insensitivity of cancer cells to external inhibitory signals by repressing important antigrowth signals such as CDK. Expression of these miRNAs is upregulated in several types of cancer (Fornari et al., 2008; Kim et al., 2009; le Sage et al., 2007), and researchers showed that these miRNAs directly repress all members of the Cip/Kip family of CDK inhibitors (p57Kip2, p21Cip1, and p27Kip1) (Kim et al., 2009). Also, other miRNAs regulate the expression CDK proteins. For example, miR-34a expression is induced by TP53 activation and mediates cell-cycle arrest at the G1 phase by suppressing multiple targets, including CDK4, CDK6, cyclins D1 and E2, and

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MET (He et al., 2007). miR-15a and miR-16 target G1 cyclins such as cyclins D1, D2, and E1, inducing cell-cycle arrest at the G1–G0 phase (Bandi et al., 2009).

C. Evasion of Apoptosis The ability to evade apoptosis is one of the main characteristics of tumorigenesis. Several studies demonstrated that miRNAs play significant roles in apoptosis regulation in different types of cancer cells. miRNAs can act in both proapoptotic and antiapoptotic regulatory pathways according to the cell type and specific proapoptotic and antiapoptotic target genes. Accordingly, expression of the majority of proapoptotic miRNAs is downregulated in cancer cells (Subramanian and Steer, 2010). Among the proapoptotic miRNAs are miR-101 and miR-1, which target the BCL2 homologous protein MCL1 (Su et al., 2009) and heat shock proteins HSPD1 and HSPA4 (Xu et al., 2007), respectively. Interestingly, the most important examples of proapoptotic miRNAs are associated to TP53 regulation. TP53 is the most extensively studied tumor suppressor and is mutated in almost 50% of all human cancers. TP53 is known to be the guardian of the genome, with a critical role in both cell cycle and apoptosis regulation. DNA damage or genotoxic stress can activate TP53, which modulates the transcription of several target genes and expression of more than 30 miRNAs (Subramanian and Steer, 2010). For example, TP53 activates miR-34a (He et al., 2007), which targets important genes involved in apoptosis and cell proliferation, such as CDK4, MYCN, SIRT1, E2F3, and E2F5 (Wei et al., 2008; Welch et al., 2007; Yamakuchi et al., 2008). Reciprocally, miR-34 family members are essential for the proper execution of TP53-dependent cellular responses (He et al., 2007). Other major proapoptotic miRNAs whose expression is induced by TP53 activation include the miR-15a/miR-16-1 cluster, which represses the antiapoptotic BCL2 protein expression and activates the intrinsic apoptotic pathway APAF-1/CASPASE-9/PARP (Calin et al., 2008). Interestingly, investigators showed that members of the miR-29 family activate TP53 by repressing PIK3R2 and CDC42 (Park et al., 2009b). In addition, overexpression of miR-29b downregulates the expression of MCL1 and sensitizes cancer cells to TRAIL (Mott et al., 2007), and promotes the expression of proapoptotic genes silenced by methylation by targeting the DNA-methylating genes DNMT3A and DNMT3B (Fabbri et al., 2007). Conversely, the fact that MYC, the major regulator of cell proliferation and apoptosis, is associated with antiapoptotic miRNA regulation is not a surprise. MYC can transactivate the miR-1792 cluster, which targets proapoptotic genes such as E2F1 (O’Donnell et al., 2005), p21, and

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BCL2L11 (Inomata et al., 2009). In addition to the miR-1792 cluster, other miRNAs exhibit antiapoptotic functions by targeting several tumor suppressor genes. One of the most well-known antiapoptotic miRNAs is miR-21. Expression of this miRNA is upregulated in many cancer types and it represses the expression of apoptosis-related genes such as PTEN (Meng et al., 2007), PDCD4 (Asangani et al., 2008), and TPM1 (Zhu et al., 2007). Also, miR-221 and miR-222 repress genes that promote apoptosis, such as KIT (Felli et al., 2005), p27 (le Sage et al., 2007) and CDKN1C (p57) (Fornari et al., 2008). In addition, miRNAs regulate genes in the apoptosis signaling pathway such as miR-133, which represses caspase-9 expression (Xu et al., 2007), and miR-155, which is responsible for silencing of TP53 functions by directly repressing TP53INP1 (Gironella et al., 2007), an important mediator of TP53 antioxidant and proapoptotic activities (Cano et al., 2009). A schematic of the main miRNAs involved in apoptosis is shown in Fig. 2.

D. Limitless Replicative Potential Cancer cells have unlimited replicative potential. In contrast, normal cells became senescent when they complete the limited doubling in response to a multitude of different stimuli, such as DNA-damage signaling, oxidative stress, telomere attrition, and oncogene activation (Kuilman et al., 2008; Pascal et al., 2005). Several mechanisms regulate cellular senescence and the responses to these stimuli, including miRNA regulation. Regarding this, researchers found that loss of miR-138 expression may contribute to gain of human telomerase reverse transcriptase (hTERT) protein expression in thyroid carcinoma cells, inducing consequent telomerase deregulation (Mitomo et al., 2008). In an miRNA-screening library study, researchers found that miR-373 and miR-372 repressed the expression of LATS2, which interacts with a negative regulator of TP53 and may function in a positive feedback loop with TP53 that responds to cytoskeleton damage. Therefore, miR-373 and miR-372 are capable of facilitating transformation of primary cells harboring oncogenic RAS and wild-type TP53 expression via neutralization of TP53-mediated CDK inhibition and thus preventing premature senescence induced by oncogene activation (Voorhoeve et al., 2006). Because TP53 is a key regulator of senescence, the miRNAs that are activated by TP53 are also important in this process. For example, miR-34 family members participate in senescence via the E2F signaling pathway (Kumamoto et al., 2008; Tazawa et al., 2007). Recently, authors reported strong induction of miR-34a and miR-146a expression during senescence in primary human TIG3 fibroblasts after constitutive activation of the small nuclear ribonucleoprotein SNRPE (Christoffersen et al., 2010). Moreover,

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Fig. 2 miRNAs activity in proapoptotic and antiapoptotic pathways. DNA damage or genotoxic stress activates TP53, the main regulator of the proapoptotic pathway. TP53 then activates the transcription of several miRNAs (e.g., miR-34a) that target important genes involved in apoptosis such as MYCN, E2F3, E2F5, and the miR-15/miR-16-1 cluster, which represses BCL2 and activates the intrinsic apoptotic pathway APAF-1/CASPASE-9/PARP. miR29 family members activate TP53 by repressing PIK3R2 (p85) and CDC42 expression. In the antiapoptotic pathway, MYC transactivates the miR-1792 cluster, which targets proapoptotic genes such as E2F1, CDKN1A, and BIM. miR-21 suppresses the expression of PTEN, PDCD4, and TPM1, and miR-221/miR-222 represses the expression of proapoptotic proteins such as c-Kit, p27, and CDKNIC, inhibiting apoptosis. Expression of the mediator of TP53 function, TP53INP1, is downregulated by miR-155, which suppresses cell-cycle arrest and apoptosis. Finally, miR-133 represses the expression of caspase-9, impairing apoptosis.

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they demonstrated that during oncogene-induced senescence miR-34a is regulated independently of TP53 and targets the important proto-oncogene MYC, coordinately controlling a set of cell-cycle regulators. Furthermore, investigators observed downregulated expression of 15 miRNAs in senescent cells and breast tumors harboring wild-type TP53 suggesting that expression of these miRNAs is repressed by TP53 in an E2F1-mediated manner (Brosh et al., 2008).

E. Angiogenesis During tumor progression, normal endothelium quiescence is lost, and proliferation is activated by proangiogenic factors, resulting in promotion of neoangiogenesis (Suarez and Sessa, 2009). Neoangiogenesis is the process by which new blood vessels form through the growth of existing blood vessels (Carmeliet, 2005). Angiogenesis is driven in part by hypoxia, which stimulates tumor-cell production of angiogenic factors such as bFGF, PGF, and VEGF (Kerbel, 2008). VEGF is one of the most important angiogenic factors, as it is highly expressed in most tumors and promotes angiogenesis by enhancing the survival, migration, and invasion of endothelial cells (Suarez and Sessa, 2009). In addition, several reports demonstrated that miRNAs are important modulators of tumor-induced neoangiogenesis (Fig. 3). For example, investigators identified a group of miRNAs including miR-16, miR-15b, miR-20a, and miR-20b as potential modulators of VEGF under hypoxic conditions (Hua et al., 2006). Interestingly, miR-126 had the opposite biological effect on VEGF regulation according to the cellular context. In a study, miR-126 directly repressed VEGF expression in vitro and in vivo and induced cell-cycle arrest at the G1 phase in lung cancer cells (Liu et al., 2009). Contrarily, researchers found that miR-126 expression was upregulated during angiogenesis and repressed negative regulators of the VEGF pathway in endothelial cells (Fish et al., 2008). Nonetheless, miR126 targets the components of MAPK and PI3K signaling pathways, SPRED1 and PIK3R2, making it a key positive regulator of angiogenic signaling in endothelial cells (Fish et al., 2008). Other miRNAs also promote angiogenesis in cultured endothelial cells. For example, endothelial cells exposed to serum overexpress miR-130a, which targets the antiangiogenic homeobox genes HOXA5 and GAX (Chen and Gorski, 2008). Interestingly, miR-210 directly modulates the TKR ligand EFNA3, a repressor of VEGF-dependent endothelial cell migration and tubulogenesis (Fasanaro et al., 2008). Cocultured endothelial cells overexpress miR-296 in response to VEGF stimulation, which promotes angiogenic signaling by degrading VEGF receptor and PDGF receptor via HGF substrate repression (Wurdinger et al., 2008). In addition, miR-27a

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Fig. 3 miRNA regulation of angiogenesis. miRNAs play roles in proangiogenic and antiangiogenic pathways. The hypoxia-inducible miR-210 acts as a proangiogenic factor by regulating the expression of EFNA3 (Ephrin A3), which is a repressor of VEGF-dependent endothelial cell migration. miR-126 positively regulates angiogenesis by regulating SPRED1 and PIK3R2, which are components of MAPK/PI3K signaling pathway. Because of growth factor exposition, endothelial cells overexpress miR-130a, which represses expression of the antiangiogenic homeobox genes HOXA5 and GAX, thus promoting angiogenesis. VEGF stimulation induces overexpression of miR-296, which represses and thus promotes angiogenic signaling. miR-378 also participates in the proangiogenic pathway by repressing the tumor suppressors SuFu and Fus-1. Also, the miR-1792 cluster promotes neoangiogenesis by targeting secreted antiangiogenic factors such as thrombospondin 1 and CTGF. Additionally, miR-221/miR-222 suppresses the expression of c-Kit and eNOS, impairing angiogenesis.

represses the expression of the zinc finger gene ZBTB10 and consequently induces its targets, such as the specificity proteins Sp1, Sp3, and Sp4, which promote the transcription of both survival and angiogenic genes (i.e., survivin, VEGF, VEGFR) (Mertens-Talcott et al., 2007). Other miRNAs that promote angiogenesis are miR-378, which targets two tumor suppressor genes (SuFu and Fus-1) and enhances cell survival and angiogenesis (Lee et al., 2007), and the miR-1792 cluster, which targets antiangiogenic proteins including the secreted factors TSP1 and CTGF (Dews et al., 2006). Recently, a study demonstrated that miR-107 can mediate TP53 regulation of hypoxic signaling and tumor angiogenesis (Yamakuchi et al., 2010).

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In addition, the authors showed that miR-107 is a potential regulator of one of the subunits of HIF1. In contrast, studies have demonstrated that miRNAs inhibit angiogenesis. For example, miR-221 and miR-222 target c-Kit and eNOS, which are important regulators of proangiogenic endothelial cell function (Poliseno et al., 2006). Recently, researchers demonstrated that miR-519c is a pivotal regulator of tumor angiogenesis and plays an important role in HIF1Amediated angiogenesis. miR-519c suppresses HIF1A, leading to reduced tumor angiogenesis. Studies in mice demonstrated that miR-519c-overexpressing cells exhibited dramatically reduced HIF1A levels, which was followed by suppressed tumor angiogenesis, growth, and metastasis in the mice (Cha et al., 2010).

F. Invasion and Metastasis Invasion and metastasis play important roles in the spread of cancer to distant sites. Cell invasion consists of migration and penetration of cancer cells into surrounding tissue, whereas metastasis results from cancer cells reaching the bloodstream and colonizing in distant organs. Several proteins, transcription factors, and miRNAs have roles in these processes. Interestingly, some miRNAs that affect processes in tumorigenesis, such as neoangiogenesis and apoptosis, also play roles in invasion and metastasis. For example, miR-21 is one of the most upregulated miRNAs in cancer cells and is a key regulator of invasion and metastasis. Besides controlling cell survival and proliferation, this miRNA promotes cell motility and invasion by targeting PTEN, a known tumor suppressor that inhibits cell invasion by blocking the expression of several matrix metalloproteinases (MMPs), such as MMP2 and MMP9 (Meng et al., 2007). In addition, miR-21 promotes invasion, intravasation, and metastasis by directly modeling the cell cytoskeleton via TPM1 suppression and indirectly regulating the expression of the prometastatic receptor UPAR (Zhu et al., 2007). Recent studies demonstrated that miR-21 targets the protein kinase C substrate MARCKs which is involved in cell adhesion and motility via regulation of the actin cytoskeleton (Li et al., 2009), TIMP3 and RECK (Gabriely et al., 2008). Also, besides repressing the expression genes involved in cell-cycle arrest at the G1 phase, apoptosis, and senescence, the pleiotropic putative tumor suppressor miR34a regulates tumor cell scattering, migration, and invasion by downregulating MET and its downstream signaling cascades (Li et al., 2009). Other miRNAs are also implicated in invasion and metastasis. For example, Ma and colleagues observed that miR-10b targets the homeobox transcription factor HOXD10 and consequently upregulates expression of the G-protein RHOC, which is involved in metastasis and is repressed by HOXD10 (Ma et al., 2007). These researchers demonstrated that miR-10b is modulated by the metastasis-promoting transcription factor TWIST.

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TWIST is a key molecule in metastasis that induces epithelial-to-mesenchymal transition (EMT) (Yang et al., 2004), which is the conversion of polarized immotile epithelial cells into motile mesenchymal cells. This process primarily occurs during embryonic development and is implicated in the promotion of tumor invasion and metastasis (Thiery, 2002). Recently, researchers identified several miRNAs as regulators of EMT. Specifically, expression of members of the miR-200 family (miR-200a, miR-200b, miR-200c, miR-141, and miR-429) as well as miR-205 was significantly downregulated in EMT (Gregory et al., 2008). In addition, members of the miR-200 family may be downstream factors in the TGFB1 pathway during EMT. Interestingly, in comparison, miR-155 contributes to RHOA suppression and therefore is related to cell migration and invasiveness (Kong et al., 2008). Studies demonstrated that miR-200c and miR-200b target the main inductors of EMT, ZEB1 and ZEB2, respectively (Christoffersen et al., 2007; Hurteau et al., 2007). Interestingly, other studies demonstrated that ZEB1 regulates transcription of miR-200 family members, a characteristic of a reciprocal negative feedback loop (Bracken et al., 2008; Burk et al., 2008). Importantly, members of the let-7 family also target important genes, such as RAS, MYC, and HMGA2, which use the RAS/MAPK kinase pathway to promote EMT (Watanabe et al., 2009). In addition to the miR-200 family, other miRNAs act as metastasis suppressors. For example, low expression of miR-335 or miR-126 in human primary tumors is significantly associated with poor metastasis-free survival. Experimentally, knockdown of SOX4 and TNC decreased invasion in vitro and metastasis in vivo, indicating that these proteins are critical effectors of metastasis activated by loss of miR-335 (Tavazoie et al., 2008). Also, miR-7 has an implicated role in tumor invasion and metastasis in that it inhibits the tumor invasion promoter PAK1 (Reddy et al., 2008). Moreover, miRNAs that target important genes involved in cell adhesion and motility signal pathways are implicated to be metastasis suppressors. Examples include miR-126, which targets CRK, a member of the adaptor protein family (Crawford et al., 2008), and miR-183, which suppresses the expression of ezrin, a member of the ERM family of cell migration and metastasis-mediating proteins (Wang et al., 2008). In addition, miR-122 represses ADAM17, RHOA, and RAC1 (Coulouarn et al., 2009; Tsai et al., 2009). Conversely, several miRNAs are known to promote metastasis. Of note is that most miRNAs that promote invasion and metastasis are located in the regulatory pathways of suppressor genes. miR-373 and miR520c, which belong to the same family, have proinvasive and promigratory effects in that they suppress CD44 expression. CD44 is a cell surface receptor and acts as a metastatic suppressor. Also, ectopic expression of miR-182 stimulates migration of melanoma cells in vitro and in vivo by directly repressing the transcription factor FOXO3, which functions as a trigger

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for apoptosis by inducing expression of genes necessary for cell death (Segura et al., 2009). Recently, studies showed that miR-9 directly targets E-cadherin and activates the b-catenin signaling which contributes to upregulation of VEGF expression and thus increases the rate of tumor angiogenesis (Ma et al., 2010b). Overexpression of miR-9 in nonmetastatic breast cancer cells enables them to form pulmonary micrometastases in mice. Moreover, miR-9 expression correlates with MYCN amplification and, in turn, is activated by MYC and MYCN. Taken together, these remarkable findings are important for understanding malignant transformation and may have implications for treatment of advanced cancer (Fig. 4).

V. CLINICAL APPLICATIONS A. miRNAs Biomarkers for Cancer Diagnosis and Prognosis miRNAs are active players in human tumorigenesis that can be potentially used as novel tools for cancer treatment and risk stratification. A growing number of studies identified miRNAs as diagnosis and prognosis markers in cancer. One of the most important causes of death in patients with cancer is metastasis. For example, miR-10b is highly expressed in cultured metastatic cancer cells as well as human metastatic breast tumors, making it an interesting target of therapy for metastasis (Ma et al., 2007). Recently, investigators demonstrated that the miR-10b antagomir apparently is a promising antimetastasis agent that does not act in a cytotoxic fashion against primary tumor cells but instead blocks their ability to form metastases (Ma et al., 2010a,b). Importantly, downregulation of the miR-1792 cluster in T cells promotes decreased persistence of tumor-specific T cells and tumor control. Thus, genetically engineered T cells expressing miR-1792 miRNAs may be a promising approach to cancer immunotherapy (Sasaki et al., 2010). In addition, several studies demonstrated that miR-21 expression is upregulated in many types of cancer, including breast (Yan et al., 2009) and gastric (Motoyama et al., 2010) cancer, making this miRNA an interesting target in cancer treatment. Recently, Hwang et al. (2010) demonstrated that low miR-21 expression was associated with increased survival following adjuvant treatment in a study of two independent cohorts of pancreatic tumor samples. In addition, they found that anti-miR-21 treatment increased anticancer drug activity in vitro, suggesting that miR-21 is useful as an adjuvant in personalized cancer therapy. Furthermore, by regulating the expression of B7-H3 protein, the miR-29 family is implicated in the escape of solid tumors of the immune system (Xu et al., 2009). Therefore,

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Blood vessel

Tumor cell

Tumor cell

EMT

Metastasis

Invasion

miR-21

PTEN

Metastasis TIMP3 RECK

MMP2 TPM1

MMP9

Invasion miR-200 family

Let-7 family

CRK

Ezrin

miR-126

miR-183

miR-373 miR-520c

miR-182

EMT

ZEB1 ZEB2 RHOC

RAS HMGA2

CD44

FOXO3

MYC HOXD10

Metastasis miR-10b Twist

Fig. 4 Modulation of invasion and metastasis signaling by miRNAs. Invasion and metastasis are complex cellular processes involving EMT and the participation of miRNA regulation. Several miRNAs are implicated to promote invasion and metastasis. For example, miR-21 is a key regulator of invasion and metastasis that controls cell survival, proliferation, motility, and invasion by targeting PTEN, MMP2, MMP9, TPM1, TIMP3, and RECK. miR-10b promotes metastasis and RHOC overexpression by regulating TWIST and HOXD10. miR-373 and miR520c, which belong to the same family, have proinvasive and promigratory effects by targeting

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this family is an interesting group of immunoinhibitory molecules with potential utility in cell-mediated immunotherapy and antibody-based targeted therapeutic strategies (Xu et al., 2009). Perdesen et al. (2009) showed that treatment with anti-TNF drugs such as eternacept and infliximab was sufficient to reduce both miR-155 expression and proliferation of DLBCL cells. In addition, they observed a substantial decrease in the tumor burden in DLBCL xenografts in response to treatment with eternacept. However, the use of downregulated miRNAs may be an effective approach to cancer therapy based on these small miRNAs. For instance, because miR-15 and miR-16-1 are natural BCL2 regulators, they may be used in therapy for BCL2-overexpressing cancers (Calin and Croce, 2006). Interestingly, downregulation of miR-34a expression is an independent prognostic marker for relapse of non-small-cell lung cancer (NSCLC) (Gallardo et al., 2009) and has predicted response of CLL associated with TP53 inactivation (Zenz et al., 2009). These studies identified miR-34 as a novel prognostic marker with potential applications in therapy for NSCLC and CLL. Using a chemically modified synthetic miR-143, Akao et al. (2010) achieved a significant suppressive effect on colorectal tumor xenografts, suggesting that miR-143 is an interesting candidate agent for the treatment of colorectal tumors. Finally, studies showed that let-7 administration was effective against lung (Kumar et al., 2008) and pancreatic (Torrisani et al., 2009) cancers in mouse models, suggesting that this miRNA is useful in miRNA-based replacement therapy. These findings support the use of miRNA-based cancer therapeutics and suggest that miRNAs are candidate biomarkers for cancer diagnosis (Table I).

B. Potential Use of Circulating miRNAs in Cancer Diagnosis In addition to studies of miRNA expression profiling of primary tumor samples, the usefulness of circulating miRNAs as diagnostic markers was also indicated by several studies. With the exception of leukemia cases, in which malignant cells are easily obtainable, solid tumor samples are obtained for profiling via either biopsy or surgery. Therefore, studies demonstrating the the metastatic suppressor CD44. Also, miR-182 stimulates cell migration by directly repressing the transcription factor FOXO3, which functions as an apoptotic promoter. Conversely, various miRNAs suppress invasion and metastasis. Members of the miR-200 family target ZEB1 and ZEB2, which are the main inducers of EMT. ZEB1 regulates transcription of miR-200 family members, characterizing a reciprocal negative feedback loop. Let-7 miRNA family members target important genes such as RAS, MYC, and HMGA2, which promote EMT via RAS/MAPK kinase pathway. miR-126 targets a member of the adaptor protein family CRK and miR-183 suppresses the expression of ezrin, a member of metastasis-mediating proteins family.

Table I

Deregulated miRNAs with Potential Prognostic and Therapeutic Implication

miRNAs deregulated in tumor/chromosomal region

Location/organization

Upregulated miR-10b/2q31.1

Intergenic/single

miR-1792 cluster/13q31.3

Intronic/cluster

miR-21/17q23.1

Intergenic/single

miR-29a/7q32.3 miR-29b-1/7q32.3 miR-29c/1q32.2

Intergenic/cluster Intergenic/cluster

miR-155/21q21.3

Intergenic/single

Target genes and tumor type

Potential prognostic and therapeutic implication

HOXD10/breast cancer (Ma et al., 2007) E2F1/B cell lymphomas (He et al., 2005) HIF1A/lung cancers (Hayashita et al., 2005) PTEN/human hepatocellular cancer (Meng et al., 2007) PDCD4/colorectal cancer (Asangani et al., 2008) RECK/gastric cancer (Gabriely et al., 2008) MARCKS/prostate cancer (Li et al., 2009) DNMT3A and DNMT3B/ non-small cell lung cancer (Fabbri et al., 2007)

Systemic treatment with miR-10b antagomirs suppressed breast cancer metastasis in mice (Ma et al., 2010a) Downregulation of miR-1792 expression in T cells diminished the persistence of tumor-specific T cells and tumor control, suggesting a role in cancer immunotherapy (Sasaki et al., 2010) miR-21 was highly expressed in breast and gastric cancer cells, which may indicate poor prognosis. (Motoyama et al., 2010, Yan et al., 2009) Low miR-21 expression was associated with increased survival following adjuvant treatment in pancreatic cancer (Hwang et al., 2010)

TP53INP1/pancreatic cancer (Gironella et al., 2007)

The ability of miR-29 to control B7-H3 protein expression had implications in immune escape by solid tumors, suggesting a role in cancer immunotherapy (Xu et al., 2009) Anti-TNF regimen was sufficient to reduce miR-155 expression and restored SHIP1 expression in DLBCL cells with an accompanying reduction in cell proliferation, suggesting anti-TNF therapy as an adjuvant treatment of DLBCL (Pedersen et al., 2009) (continues)

Table I (continued) miRNAs deregulated in tumor/chromosomal region Downregulated miR-15a/13q14.2 miR-16-1/13q14.2 miR-34a/1p36.22 miR-34b/11q21.1 miR-34c/11q21.1

Location/organization

Target genes and tumor type

Potential prognostic and therapeutic implication

Intergenic/cluster

BCL2/CLL (Calin and Croce, 2006)

Intergenic/single

E2F3/neuroblastoma (Welch et al., 2007) BCL2/NSCLC cancer (Bommer et al., 2007)

miR-15 and miR-16-1 were natural antisense Bcl2 interactors, suggesting a role in therapy for BCL2-overexpressing tumors (Calin and Croce, 2006) Low levels of miR-34a expression were correlated with a high probability of relapse in NSCLC cells, which could be used as prognostic marker (Gallardo et al., 2009)

miR-143/5q32 miR-145/5q32

Intergenic/cluster

ERK5/CLL (Akao et al., 2007) KRAS/colorectal cancer (Chen et al., 2009)

let-7 family

Intergenic/cluster

RAS/lung cancer (Johnson et al., 2005) HMGA2/ovarian cancer (Shell et al., 2007)

Low miR-34a expression in CLL cells was associated with TP53 inactivation, which could be used to predict response (Zenz et al., 2009) miR-143 had a significant tumor-suppressive effect in human colorectal cancer cells suggesting that chemically modified synthetic miR-143 is a candidate for treatment of colorectal tumors (Akao et al., 2010) An SNP in a KRAS miRNA complementary site was significantly associated with increased risk of NSCLC, suggesting a role for let-7 miRNAs in lung cancer susceptibility (Chin et al., 2008) Restoring let-7 expression in cancer-derived cell lines strongly inhibited cell proliferation, but failed to impede tumor growth (Torrisani et al., 2009)

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diagnostic and prognostic usefulness of circulating miRNAs are of great interest. To date, the majority of the published studies using circulating miRNAs have used serum or plasma samples. Initially, the serum levels of miR-21 were associated with relapse-free survival in patients with DLBCL (Lawrie et al., 2008). Because miR-21 expression is upregulated in several cancers, it is a potential diagnostic biomarker for them. Another study found that measurement of serum levels of miR-141 made distinguishing patients with prostate cancer from healthy subjects possible (Mitchell et al., 2008). The same study indicated the presence of specific circulating tumor-derived miRNAs in the bloodstream in a murine prostate cancer xenograft model. Chen et al. (2008) demonstrated that by analyzing serum directly or RNA extracted from serum, they could identify unique miRNA expression profiles in patients with lung cancer, colorectal cancer, or diabetes and distinguish them from profiles in healthy subjects. In that study, the expression profiles showed 28 miRNAs exclusive to the healthy subjects and 63 miRNAs exclusive to the patients. In another study, circulating miRNAs such as miR-21, miR-92, miR-93, miR126, and miR-29a were significantly overexpressed in patients with ovarian cancer but not in healthy subjects (Resnick et al., 2009). Wong et al. (2009) reported that plasma levels of miR-184 were significantly higher in patients with squamous cell carcinoma of the tongue than in healthy individuals. Moreover, they observed that plasma miR-184 levels were significantly reduced in these patients after primary tumor resection. In another study, expression of miR-17-3p and miR-92 was significantly upregulated in plasma samples obtained from patients with colorectal cancer but significantly reduced after surgery (Ng et al., 2009a). In the same study, miR-92 expression distinguished colorectal cancer from gastric cancer, inflammatory bowel disease, and normal tissue. In comparison, another study found that the levels of circulating mRNAs were predictive of malignancy and survival in patients with renal cell carcinoma (Feng et al., 2008). Recently, a study demonstrated that plasma miR-17-5p, miR-21, miR-106a, and miR-106b concentrations were significantly higher in patients with gastric cancer than in normal subjects, indicating the potential of these miRNAs as complementary cancer markers (Tsujiura et al., 2010). In addition to serum and plasma, a few studies have assessed miRNAs in other body fluids as diagnostic markers for cancer. In one study, miR-126 and miR-152 indicated the presence of bladder cancer at a specificity of 82% and a sensitivity of 72% (Hanke et al., 2009). Another study demonstrated that miR-125a and miR-200a expression was present in saliva at significantly lower levels in patients with oral squamous cell carcinoma than in normal subjects (Park et al., 2009a). Several other studies have provided further evidence that miRNAs in body fluids are useful as biomarkers for cancer diagnosis. Nonetheless, investigators must assess studies of large populations and certain aspects of the experimental reliability before using miRNA in

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serum or plasma as a biomarker. Likewise, given that most of the current approaches to cancer screening are invasive and cannot detect early-stage disease, determining when tumor-related circulating miRNAs can be detected in the bloodstream during cancer progression.

C. Therapy with miRNAs Although the use of miRNAs as cancer drugs is still at the preclinical stage, several studies have demonstrated the potential use of miRNAs or compounds that interact with them as new therapeutic agents for cancer. Because a single miRNA targets genes involved in the same pathway, use of RNA inhibition techniques may be more advantageous than other techniques (Spizzo et al., 2009). Several studies of antisense-mediated inhibition of oncogenic miRNAs and replacement of miRNAs with mimics in demonstrated the potential use of these molecules in cancer therapy. Specifically, Anti-miRNA oligonucleotides (AMOs) are antisense oligonucleotides directed against miRNAs. Researchers showed that use of AMOs especially those with 2’-O-methyl modifications, is a powerful technique for miRNA targeting (Weiler et al., 2006). For example, modified AMOs decreased cell growth by inhibiting miR-21 expression in an in vivo model of breast cancer and in glioblastoma cases (Si et al., 2007). Also, AMOs conjugated with cholesterol give rise to antagomirs (Krutzfeldt et al., 2007). In a neuroblastoma model, researchers treated tumors subcutaneously induced in mice with antagomir-17-5p for 2 weeks, which resulted in tumor-growth inhibition and complete tumor regression in 30% of the cases (Fontana et al., 2008). Alternatively, in vivo studies have shown that lock nucleic acid (LNA)-based oligonucleotides are very promising in therapy for cancer (Vester and Wengel, 2004). One study showed that miR-21 could be silenced in vitro using LNA-modified antisense oligonucleotides, leading to significantly reduced cell viability accompanied by elevated intracellular caspase levels (Chan et al., 2006). Also, studies of African green monkeys demonstrated that administration of three doses of 10 mg/kg LNA-antimiR efficiently silenced miR-122, leading to a long-lasting, reversible decrease in total plasma cholesterol level without any evidence of associated toxic effects or histopathological changes in the liver (Elmen et al., 2008). Recently, Ma et al. (2010a,b) demonstrated that systemic treatment of breast cancer with miR-10b antagomirs suppressed metastasis in mice. Also, silencing of miR-10b with antagomirs in vitro and in vivo significantly increased the levels of the functionally important miR-10b target HOXD10. Although administration of miR-10b antagomirs in mice did not reduce primary mammary tumor growth, it markedly suppressed the formation of lung metastases in a sequence-specific manner. Ebert et al.

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(2007) developed an alternative strategy to the use of single anti-miRs with new molecules called miRNA sponges. These molecules are synthetic mRNAs that contain multiple binding sites for an endogenous miRNA, preventing interaction with their endogenous targets. Preliminary in vitro studies showed that miRNA sponges derepressed miRNA targets with efficiency comparable with modified AMOs. Researchers have also investigated small molecule inhibitors that specifically target miRNAs. For example, treatment with the compound azobenzene reduced miR-21 biogenesis in HeLa cells and did not produce any cytotoxic effects at a concentration of 10 mmoL (Gumireddy et al., 2008). Nonetheless, in vivo studies are necessary to confirm the use of this compound in miRNAbased cancer therapy. Because expression of most miRNAs is downregulated in cancer cells in general, clinical restoration of the expression of specific miRNAs abnormally expressed represents an interesting approach to treat cancer. Studies showed that treatment with miRNA mimics induced cell death and significantly reduced tumorigenic potential for miR-15a/miR-16 cluster in a leukemia cell model (Calin et al., 2008) and for members of the miR-29 family in a lung cancer model (Fabbri et al., 2007). Furthermore, a study demonstrated that in an established murine orthotopic lung cancer model, intranasal let-7 administration reduced tumor formation in vivo in animals with expression of a KRAS oncogene mutation (Esquela-Kerscher et al., 2008). The use of viral vectors is another potential approach to cancer therapy. Kota et al. (2009) demonstrated that miR-26a expression in hepatocellular carcinoma cells induced cell-cycle arrest associated with direct targeting of cyclins D2 and E2. Systemic administration of this miRNA in mice using an adeno-associated virus inhibited cancer cell proliferation, induced apoptosis, and dramatically protected against disease progression without having any toxic effects. These studies established the basis for use of miRNAs as therapeutic molecules in clinical trials of cancer and that the use of miRNAs as adjuvants in cancer therapy appears to have great potential. Nonetheless, further studies are necessary to assess the impact of specific miRNA-mediated therapies for prevention of off-target effects and improvement of delivery efficiency while preventing inflammatory responses.

VI. CONCLUDING REMARKS miRNAs represent a layer of complexity in gene expression regulation. Since the discovery of these tiny molecules, authors have reported a tremendous amount of published data. Several studies have shed light on miRNA biogenesis, function, and genomic variations. One of the most important

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insights was the implication of miRNA deregulation in many diseases, including cancer. Importantly, studies demonstrated that miRNAs participate in the primary phenotypic changes in cancer. In addition, the potential clinical applications of miRNAs in cancer were assessed by several studies, suggesting that they are potential candidate biomarkers in cancer diagnosis and therapy. Further studies will improve our understanding of the nature of miRNAs and of their potential use in converting untreatable tumors into treatable ones and increasing cancer cure rates.

ACKNOWLEDGMENTS G.A.C. is supported as a fellow at The University of Texas M. D. Anderson Research Trust, as a fellow of The University of Texas System Regents Research Scholar, and by the Ladjevardian Regents Research Scholar Fund. Work in Dr Calin’s laboratory is supported in part by an NIH RO1 grant, by a DOD Breast Cancer Idea Award, by a Breast Cancer SPORE Developmental Research Award, by an Ovarian Cancer SPORE Developmental Research Award, by a CTT/3I-TD grant, and by 2009 Seena Magowitz—Pancreatic Cancer Action Network—AACR Pilot Grant. M.I. is an American Cancer Society Research Scholar.

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Index

A

C

Aflatoxin B1 (AFB1), 24, 44 Alanine transaminase (ALT) level, 27, 29 Angiogenesis, 132–134 Antigrowth signals, 128–129 Anti-miRNA oligonucleotides (AMOs), 142 Apoptosis evasion, 129–131 ATM-Chk2 and ATR-Chk1 pathways. See also DNA damage signaling activation of key substrates, 75 mechanism, 75–77 alterations, in cancer Chk1 conditional deletion, 91–92 homozygous and hypomoErphic mutations, 88 HRR deficiency, 89 Li Fraumeni syndrome, 89 Seckel syndrome, 90 functions G2 impairment, 82–83 intra-S checkpoint activation and responses, 83–84 mechanisms, 81–82 multiple DNA damage and replication checkpoints, 80 ATR-Chk1 signaling, 77 Axin-1 regulator, 53 AZD7762 inhibitor, 100–101

-Catenin mutations, 53 Chk1 conditional deletion of, 91 inhibition of, 102–103 inhibitors AZD7762, 100–101 PF-00477736, 101 UCN-01, 100 Chk2 defiiency, on tumorigenesis, 89–90 inhibitors AZD7762, 100–101 XL-844, 101–102 Circulating miRNAs, 138, 141–142 Claspin, 78

B Bim-mediated apoptosis, 13 Biogenesis and action mechanism, miRNAs, 114–116 Biomarkers, cancer diagnosis, 136, 138–140 BRCA1 vs. BRCA2 protein, 85 Burkitt’s lymphomas (BLs), 2

D Deregulated miRNAs, 139–140 Diffuse large B cell lymphomas (DLBCLs), 117, 138 DNA damage signaling checkpoint suppression Chk1 and Chk2 inhibitors, 100–102 Chk1-deficient DT40 cells studies, 99 damage response defects, 98 genotoxic agents, 99 sensitization, 98 HRR defects, in cancer therapy, 92–95 recombination, 85–87 repair, 85 resection, 84–85 as tumorigenesis barrier, 95–97 DNA double-strand breaks (DSBs), 75–76 DNA repair, 85 DT40 cells, 83

159

160

E EBNA1 protein, 6–7 E2F1 factor, 122 Epigenetic regulation, 119–120 Epithelial-to-mesenchymal transition (EMT), 135 Epstein-Barr virus (EBV) dnEBNA1 eviction, 7 lymphomagenesis model immune detection, 9–10 implications, 10–13 predictions, 13–14 presence, in lymphomas gene expression variation, 3–4 genomic map, 2–3 PTLD vs. BLs, 5–6 selective advantage, to tumors, 5 primary infection, 2 proapoptotic protein Bim, 8 replication of, 6 ERK family, 127 Estrogens, 124–125

F Familial multiplex HCC, 59–60

G Gemcitabine, 101 Gender disparity, in HCC, 58–59 Genetic aberrations HCC, 54–55 hepatocytes, 51 Genetic variations and HCC candidate genes -catenin mutations, 53 p53 mutation, 52–53 proto-oncogenes activation, 53–54 cellular pathways, deregulation of, 56–57 gender disparity, 58–59 genome-wide analysis, 54–55 host factors, 51–52 predisposition gene(s) identification, 59–60 SNP analysis, 57–58 viral, 50–51 Genome-wide association study (GWAS), 59–60 Glycine N-methyltransferase (GNMT) gene, 54

Index GWAS. See Genome-wide association study (GWAS)

H HBeAg, 34–35 HBx protein, 48–50 Hepatitis B virus (HBV). See also Hepatocellular carcinoma (HCC) global prevalence of, 24–25 HCC development, 26 mutations in, 36–37 pre-S gene deletion of, 39 structure of, 22–23 Hepatocellular carcinoma (HCC) development factors, 26 genetic variations and candidate genes, 52–54 cellular pathways, deregulation of, 56–57 gender disparity, 58–59 genome-wide analysis, 54–55 host factors, 51–52 predisposition genes identification, 59–60 SNP analysis, 57–58 viral, 50–51 incidence rate, 24–25 molecular carcinogenesis chronic inflammation, 47–48 proteins, 48–50 nonviral factors, 44–45 primary prevention, 45–46 risk factors, 24 therapeutic options for, 24 viral factors genotype, 31–34 natural mutants, 36 potential interactions, 39, 41–42 precore and CP mutants, 36–40 pre-S deletion, 39 risk prediction nomogram, 43 hepatocarcinogenesis, role in, 43 subgenotype, 34–35 viral load, 27–31 HIF1A regulator, 121 Hodgkin’s lymphomas (HL), 12 Homologous recombinational repair (HRR) defects BRCA1/BRCA2, 92

161

Index poly (ADP-ribose) polymerase 1 (PARP1), 93–94 PTEN tumor suppressor, 94 HRE. See Hypoxia response element (HRE) Hypoxia, 120–122 Hypoxia-inducible factor (HIF), 120–122 Hypoxia response element (HRE), 121

I Imatinib, 97 Insulin-like growth factor (IGF) signaling pathway, 56–57 Intra-S checkpoint, 80 Invasion and metastasis, 134–137 Iron-sulfur cluster scaffold homolog (ISCU), 121 ISCU. See Iron-sulfur cluster scaffold homolog (ISCU)

L Latent membrane proteins (LMPs), 12–14 Li Fraumeni syndrome, 89 Lymphomagenesis, EBV-induced model immune detection, 9–10 implications cell proliferation, 12 cellular oncogene activation, 11 gene expression, 10–11 predictions Bim-mediated apoptosis, 13 genetic alterations, 13–14 Lymphomas evolution. See Epstein-Barr virus (EBV)

M Metastasis. See Invasion and metastasis microRNAs (miRNAs) alteration pathways angiogenesis, 132–134 antigrowth signals, 128–129 apoptosis evasion, 129–131 growth signals, 126–128 invasion and metastasis, 134–137 replicative potential, 130, 132 biogenesis and action mechanism, 114–116 clinical applications biomarkers, 136, 138–140 in cancer diagnosis, 138, 141–142

therapy, 142–143 definition, 114 expression alterations, in cancer detection, 115–116 functions of, 117 expression variations, causes of epigenetic regulation, 119–120 estrogens, 124–125 genomic variation, 118 hypoxia and HIF, 120–122 mutations and SNPs, 118–119 posttranscriptional regulation, 125–126 transcription factors, 122–124 HCC expression, 55 miR-210, 121 Molecular carcinogenesis, HBV-related HCC chronic inflammation, 47–48 proteins, 48–50 Mutations and SNPs, 118–119 mycER, 13 MYC regulator, 129–130

N Nasopharyngeal carcinomas (NPCs), 3, 12 Noncoding RNAs (ncRNAs), 114 Nonhomologous end-joining (NHEJ), 84 Non-small-cell lung cancer (NSCLC), 138 Nuclear factor-kB activation of, 48 function of, 47–48

O Occult HBV infection, 43 Oku-BL cells, 7–8 Olaparib, 94 Oncogene-induced senescence (OIS), 95–96

P p53 mutation, 52–53 Poly (ADP-ribose) polymerase 1 (PARP1), 93–94 Posttranscriptional regulation, 125–126 Post-transplant lymphoproliferative disorders (PTLDs) early-onset and late-onset, 5–6 Oku-BL cells and, 7–8 Predisposition genes, 59–60 Pre-S gene deletion, 39

162 Proapoptotic protein Bim, 8 PTEN tumor suppressor, 94–95

R RAS-association domain family (RASSF1) effector, 57 Ras/MAPK signaling pathway, 57 RAS signaling, 126–127 Recombination, 85–87 REVEAL-HBV study, 27–29

S Seckel syndrome, 90 Single nucleotide polymorphisms (SNPs), 57–58, 118–119 SNPs. See Single nucleotide polymorphisms (SNPs) Strand resection, 84–85 Suppression Chk1 and Chk2 inhibitors, 100–102 Chk1-deficient DT40 cells studies, 99 damage response defects, 98 genotoxic agents, 99 sensitization, 98

T TGFB1 regulator, 128 Tipin, 78 TopBP1 protein, 78 TP53 induced miRNAs, 122–123 tumor suppressor, 129 Transarterial chemoembolization (TACE), 24 Transcription factors, 122–124 Transformed cells, 2 Tumorigenesis

Index barrier oncogene-induced senescence (OIS), 95–96 PI3K-Akt pathway, 97 miRNA alteration, 126 TWIST factor, 135

V VEGF factor, 132 Viral factors, for HBV infection genotype disease progression, influence on, 34 phylogenetic analyses, 31–32 retrospective studies, 32–33 hepatocarcinogenesis, role in, 43 natural mutants, 36 potential interactions case studies, 42 genomic algorithms, 39, 41 genotype B vs. C, 41 precore and CP mutants, 36–40 pre-S deletion, 39 risk prediction nomogram, 43 subgenotype, 34–35 viral load case studies, 30–31 DNA level study, 27–29 REVEAL-HBV study, 27 Viral genetic variations, 50–51

W Wnt-signaling pathway, 56

X X protein, 36