Hepatocellular Carcinoma: Novel Molecular Approaches for Diagnosis, Prognosis, and Therapy Augusto Villanueva, 1,2 Beatriz Minguez, 2,3 Alejandro Forner, 1,2 Maria Reig, 1,2 and Josep M. Llovet 1,2,3,4 1 HCC Translational Research Laboratory, Barcelona-Cl´ ınic Liver Cancer Group, Institut d’Investigacions Biomediques Agusto Pi i Sunyer (IDIBAPS), Liver Unit, Hospital Clinic, Barcelona, Spain; email: [email protected], [email protected], [email protected], [email protected]2 Centro de Investigaci ´ on Biom ´ edica en Red de Enfermedades Hep ´ aticas y Digestivas (CIBEREHD), Instituto Carlos III, Spain; email: [email protected]3 Liver Cancer Program, Division of Liver Diseases, Mount Sinai School of Medicine, New York, New York 10029 4 Instituci ´ o Catalana de Recerca i Estudis Avanc ¸ats, Barcelona, Spain Annu. Rev. Med. 2010. 61:317–28 The Annual Review of Medicine is online at med.annualreviews.org This article’s doi: 10.1146/annurev.med.080608.100623 Copyright c 2010 by Annual Reviews. All rights reserved 0066-4219/10/0218-0317$20.00 Key Words liver cancer, genomics, personalized medicine, targeted therapy Abstract The genomic era is changing the understanding of cancer, although translation of the vast amount of data available into decision-making al- gorithms is far from reality. Molecular profiling of hepatocellular carci- noma (HCC), the most common cause of death among cirrhotic patients and a fast-growing malignancy in Western countries, is enabling the ad- vancement of novel approaches to disease diagnosis and management. Most HCCs arise on a cirrhotic liver, and predictably, an accurate ge- nomic characterization will allow the identification of procarcinogenic signals amenable to selective targeting by chemopreventive strategies. Molecular diagnosis is currently feasible for small tumors, but it has not yet been formalized by scientific guidelines. Molecular treatment is a reality: Sorafenib confers unprecedented survival benefits in patients at advanced stages. Genomic information from tumor and nontumoral tissue will aid prognosis prediction and facilitate the identification of oncogene addiction loops, providing the opportunity for more person- alized medicine. 317 Annu. Rev. Med. 2010.61:317-328. Downloaded from www.annualreviews.org by Duke University on 10/14/12. For personal use only.
1HCC Translational Research Laboratory, Barcelona-Clınic Liver Cancer Group, Institutd’Investigacions Biomediques Agusto Pi i Sunyer (IDIBAPS), Liver Unit, Hospital Clinic,Barcelona, Spain; email: [email protected], [email protected],[email protected], [email protected] de Investigacion Biomedica en Red de Enfermedades Hepaticas y Digestivas(CIBEREHD), Instituto Carlos III, Spain; email: [email protected] Cancer Program, Division of Liver Diseases, Mount Sinai School of Medicine,New York, New York 100294Institucio Catalana de Recerca i Estudis Avancats, Barcelona, Spain
Annu. Rev. Med. 2010. 61:317–28
The Annual Review of Medicine is online atmed.annualreviews.org
This article’s doi:10.1146/annurev.med.080608.100623
AbstractThe genomic era is changing the understanding of cancer, althoughtranslation of the vast amount of data available into decision-making al-gorithms is far from reality. Molecular profiling of hepatocellular carci-noma (HCC), the most common cause of death among cirrhotic patientsand a fast-growing malignancy in Western countries, is enabling the ad-vancement of novel approaches to disease diagnosis and management.Most HCCs arise on a cirrhotic liver, and predictably, an accurate ge-nomic characterization will allow the identification of procarcinogenicsignals amenable to selective targeting by chemopreventive strategies.Molecular diagnosis is currently feasible for small tumors, but it hasnot yet been formalized by scientific guidelines. Molecular treatment isa reality: Sorafenib confers unprecedented survival benefits in patientsat advanced stages. Genomic information from tumor and nontumoraltissue will aid prognosis prediction and facilitate the identification ofoncogene addiction loops, providing the opportunity for more person-alized medicine.
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OVERVIEW
Hepatocellular carcinoma (HCC) is the lead-ing cause of death among cirrhotic patients,and with 600,000 deaths per year, it is the thirdmost common cause of cancer-related mortal-ity worldwide (1, 2). HCC ranks sixth in termsof global incidence, showing a steady increasein Western countries that is mainly due to therising incidence of hepatitis C virus (HCV) in-fection in recent decades (3). Currently, onlyone third of newly diagnosed HCC patientswill be eligible for potential curative therapies(4).
Unlike other cancers, HCC usually arises ona previously damaged organ. Liver cirrhosis isthe underlying disease in >80% of cases. Thisfact, added to the number of different etiologiesresponsible for liver damage (e.g., viral hep-atitis, alcohol, iron overload and other causesof cirrhosis), accounts for the high molecularvariability of HCC. Surveillance programs incirrhotic patients have facilitated HCC diagno-sis at earlier stages (5). However, the develop-ment of effective chemopreventive strategies inthis population is hindered by the lack of a reli-able understanding of the genomic sequence ofevents in this premalignant milieu.
The unprecedented results of a recentlypublished phase III clinical trial show that so-rafenib, a BRAF/VEGFR/PDGFR kinase in-hibitor, significantly improves survival and timeto progression in patients with advanced HCC(6). This trial demonstrates the benefit of tar-geted therapies in HCC, highlighting the im-portance of oncogene addiction discovery inthis malignancy. Molecular combination ther-apies are currently being tested that block themain pathways involved in HCC pathogenesis,such as mTOR, c-MET, IGF, and FGF signal-ing, among others. Current genomic research,however, has pointed toward additional path-ways, particularly Wnt signaling, as drivers ofproliferation. This review focuses on the diag-nostic, prognostic, and therapeutic impact ofgenomics in HCC and provides some insightinto future challenges of molecular medicineapplied to this malignancy.
GENOMIC ASSESSMENT OF RISK:MOLECULAR EPIDEMIOLOGY
The current understanding of the epidemiol-ogy of HCC recognizes several predisposingfactors for development of this cancer: chronicHCV or hepatitis B virus (HBV) infection,some mutants or genotype variants of HCVand HBV, cirrhosis, and male gender. HBV-related factors such as HBeAg positivity, highviral load, genotype C, and HBV mutants haveshown to be independent predictors of HCCdevelopment (7, 8). Similarly, HCV genotype1b is claimed to increase the risk of HCC de-velopment in a recent meta-analysis (9). Asidefrom these clinical and virological variables, re-cent molecular data point to germline DNAalterations and specific molecular traits in thepreneoplastic cirrhotic tissue as important fac-tors to identify populations with increased riskfor HCC development.
The rapid advancement of cancer genomicshas enabled the identification of mutations ingermline DNA that define patients at high riskof developing cancer. Mutations in BRCA1 orBRCA2 are linked to increased risk of breast orovarian cancer (10), and mutations in genes in-volved in DNA mismatch repair predict highrisk for hereditary colon cancer (11). Recentgenome-wide association studies and meta-analyses led by an international consortium in-cluding thousands of patients provided robustgenomic data associating genetic variants ofBMP4, CDH1, RHPN2, and EIF3H with therisk of colon cancer (12, 13).
Data currently available to define popula-tions at risk for HCC on the basis of molec-ular analysis of polymorphisms lack some ofthe requirements of high-quality investigations,such as large sample size, biological plausibility,and independent validation (14). For instance,SNPs and haplotypes located in the SCBY14,CRHR2, and GFRA1 genes were identified ina case-control study in patients with chronicHCV infection, but thorough external valida-tion is required (15). In order to overcome het-erogeneity between studies and other method-ological limitations, meta-analytic approaches
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and multivariate analysis including key clini-cal variables will be necessary (16). A recentmeta-analysis evaluated the relationship be-tween polymorphisms in genes encoding isoen-zymes involved in cellular detoxification andrisk of HCC. Among 15 studies identified, theauthors concluded that carriers of the GSTM1-and GSTT1-null genotype might have a smallexcess of HCC (17), but again further studiesare needed to confirm these results.
More revealing data were reported in a re-cent case-control study that found a significantassociation between an epidermal growth factor(EGF) gene polymorphism and the risk of HCC(18). The authors found a fourfold risk of devel-oping this cancer in cirrhotic patients with theG/G genotype for the EGF gene. These datawere further validated in an independent co-hort obtaining similar results. Interestingly, theG/G genotype was strikingly correlated withhigher levels of EGF messenger both in vitroand in patient’s serum, increasing the biologi-cal plausibility of the hypothesis that links EGFto risk of HCC development. This hypothesisis supported by other confirmatory data. First,a recent study identified a gene signature fromthe cirrhotic tissue able to predict survival andlate recurrence (>2 years) in patients with HCCtreated with surgical resection (19). Notably,EGF was ranked among the top upregulatedgenes in the poor-survival signature. Second,there is evidence correlating EGF overexpres-sion with overt HCC (20) corresponding tothe observed antineoplastic effects of selectiveEGF receptor blockade in experimental modelsas well as in early-phase clinical trials (21, 22).Finally, using a carcinogen-induced rat modelof HCC, gefintinib (an EGF receptor tyro-sine kinase inhibitor) was able to decrease tu-mor incidence compared with placebo (23). Allthese data highlight EGF signaling as a po-tential target for chemoprevention in humanHCC.
Further genome-wide association studieswould be required for the identification ofgermline DNA alterations related to HCC de-velopment. In addition, specific gene signa-tures defining high risk of HCC development in
cirrhotic patients are currently being developedand tested.
MOLECULAR DIAGNOSIS
In the past, HCC was considered a neoplasmwith dismal outcome because almost all caseswere diagnosed at terminal stages. During thepast two decades there has been a change in thedetection and characterization of early HCC asa result of the wide implementation of surveil-lance programs in Japan and the West, in ad-dition to the application of cutting-edge imag-ing techniques (5). The diagnostic algorithmproposed by the American Association for theStudy of the Liver, which has been externallyvalidated (24), allows a confidential and non-invasive diagnosis of most HCCs above 2 cm,and of one third of tumors less than 2 cmin size, in cirrhotic patients (5). If imaging-based diagnosis is not feasible, biopsy shouldbe obtained, although the diagnostic accuracyof biopsy in small tumors is flawed (the false-negative rate is 30%–50%). The pathologicalresemblance between dysplastic nodules andwell-differentiated HCC makes pathologicalconfirmation difficult, even among expert hep-atopathologists (25). From a cost-benefit per-spective, the accurate diagnosis of small tumors(<2 cm) is critical because patients with theseearly tumors are candidates for aggressive treat-ments, such as surgical resection or local abla-tion (26). Conversely, for dysplastic nodules, ofwhich only one third will acquire the malignantphenotype, tight ultrasound-based followup isrecommended. Regrettably, the main serum tu-mor markers, such as alpha-fetoprotein (AFP),lens culinaris agglutinin-reactive AFP (AFP-L3), des-gamma carboxyprothrombin (DCP),and glypican-3 (GPC3) (27), showed subopti-mal results when tested in the surveillance andearly-detection mode.
Genome-wide gene expression microarrayand quantitative real-time reverse-transcriptasepolymerase chain reaction (qRT-PCR) studieshave attempted to identify a molecular signa-ture for HCC diagnosis. Genomic strategiesleading to gene discovery have been extensively
reviewed elsewhere (28). Initially, using high-throughput qRT-PCR in a training-validationscheme, a study conducted in 128 human sam-ples described a 13-gene signature able to iden-tify HCC lesions with high diagnostic accuracy(29). Soon after, the application of microarraytechnologies allowed the detection of 120 genesthat distinguish dysplastic nodules from HCCin HBV patients (30). Similarly, but restricted toHCV-related HCC, a three-gene signature (thegenes that encode GPC3, LYVE1, and survivin)has been proposed as an accurate molecular tool(>80% accuracy) to discriminate between dys-plastic nodules and small HCCs (<2 cm) (31).The performance of this signature was exter-nally validated in a different set of samples andinternally validated using a different genomicplatform [i.e., oligonucleotide microarray (32)].
The diagnostic performance of some mark-ers of early HCC identified by genomic stud-ies has been prospectively assessed by immuno-histochemistry. A promising marker is GPC3,which shows a sensitivity of 68%–72% and aspecificity superior to 92% (33, 34). Similarly,combinations of different protein markers—HSP70, GPC3, and GS—in 105 hepatocellularnodules performed acceptably (sensitivity andspecificity of 72% and 100%, respectively) (33),which was afterwards validated in a larger series(35).
Despite all these advances in the molecu-lar diagnosis of HCC, not a single biomarkerhas been adopted in the standard diagnostic al-gorithms. Despite their complex experimental-design requirements (i.e., large number of smallHCCs, external validation, easy detection inclinical routine), the inclusion of a moleculardiagnosis in guidelines of management is ex-pected in the medium term.
GENOME-WIDE PROGNOSISPREDICTION: INTEGRATIONWITH CLINICAL SYSTEMS
Cancer classification is aimed to establish prog-nosis, select the appropriate treatment for thebest candidates, and aid researchers to designclinical trials with comparable criteria. Staging
systems are currently available in HCC, but nomolecular data have been incorporated so far.In principle, these systems should incorporatevariables related to tumor burden, liver func-tion, and symptoms. The Barcelona-ClınicLiver Cancer (BCLC) staging and treatment al-gorithm has been widely endorsed in guidelinesfor trial design and clinical management (4, 5,36) (Figure 1). Briefly, patients are allocated toa specific therapy based on tumor burden (i.e.,size and number of nodules, presence of satel-lites or extrahepatic spread), liver function (i.e.,bilirubin, portal hypertension, Child-Pughclassification) and symptoms (i.e., ECOG-Performance status test). Patients at early stages(Stages 0–A) can achieve long median survivalrates (>60 months) if treated with potentiallycurative therapies such as surgical resection,liver transplantation, or percutaneous ablation.Patients at the intermediate stage (Stage B) canimprove survival from the median 16 monthsof natural history to 20 months followingchemoembolization. Patients at the advancedstage (Stage C) can improve survival withsorafenib, a multikinase inhibitor that recentlyexpanded median survival from 7.9 monthsto 10.7 months, establishing a new standardof care (6). Compared to other systems,TNM staging is hampered by the need forinformation on microscopic vascular invasion(37), which is only available in 20% of tumorsundergoing surgical treatment. Other scoresneed to be further validated and do not providea therapeutic algorithm, e.g., Japan IntegratedStaging (38) and the CLIP score (39).
Molecular biomarkers can have outcomeimplications, either as prognostic variables oras predictors of treatment response. Few cancerclassifications involve these parameters. Suchis the case of breast cancer, where Her2/neustatus distinguishes subgroups of patients withdifferent outcome and treatment response totrastuzumab (40). Similarly, EGFR mutationalstatus in non-small-cell lung cancer identi-fies responders to erlotinib (41), and wild-typeKRAS in colon cancer recognizes respondersto cetuximab (42). To some extent, a pa-tient’s molecular prognosis is determined by
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Very early / Early stage(BCLC-0/A)
Intermediate stage(BCLC-B)
Advanced stage(BCLC-C)
Single nodule / = 3 nodules, = 3 cmNo extrahepatic spread / No satellitesPerformance status 0 / Child-Pugh A
Genomic predictors of tumoraggressiveness (e.g., vascular
invasion, satellites, poordifferentiation)
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Figure 1Prognosis prediction in HCC will likely depend on both clinical staging systems and genomic profiles. Clinical staging captures tumorstatus, liver functional status, and health condition; genomic data capture gene profiles from tumor and nontumoral cirrhotic tissue.Clinical systems, e.g., the Barcelona-Clınic Liver Cancer (BCLC) staging system, accurately predict prognosis in most patients. Inaddition, genomic profiling of the tumor and the surrounding tissue will complement clinical data to decrease misclassification rates.However, the relative importance of each component will depend on the stage of the disease. The top panel summarizes the differentstages, therapy, and predicted survival in HCC according to the BCLC staging system. The lower panels show the relative weightfor survival prediction of genomic data from adjacent and tumoral tissue. In patients with very early HCC treated with surgicalresection, survival is mainly determined by the risk of developing a de novo HCC or liver disfunction, and this is genetically encoded inthe surrounding tissue (19). As cancer progresses, genomic data from the tumor become more informative than data from adjacenttissue in terms of survival prediction. This is because several pathological features related to patient survival (e.g., vascular invasion,satellites, poor differentiation) are encoded in the tumor (51). In these stages, patient survival no longer depends so stringently on therisk of developing a de novo HCC, since the risk of death is more related to local disease and distant progression. Interestingly, localprogression of the disease, i.e., intrahepatic metastasis (55), in advanced stages is genetically encoded in the adjacent tissue.
the identification of oncogenic addiction loopsand the patient’s susceptibility to ad hoc block-ade therapy. Albeit clinically relevant, no clearoncogenic addiction loops reporting responseto targeted therapies have been described inHCC, and the attempts to molecularly classify
HCC patients have been based on unbiasedwhole-genome approaches (43).
The unprecedented high-throughput ca-pacity of newly developed genomic plat-forms favors the assumption that genome-wide approaches could help identify molecular
determinants of this disease. Initial experiencesin leukemias and breast cancer clearly demon-strated the predictive power of microarray tech-nologies (44), but the molecular complexity ofHCC was greater than originally recognized.
Unsupervised clustering of microarray dataobtained from human HCC samples identifieddiverse groups of patients according to theirsimilarities in gene expression. Integrativeanalysis with other genomic parameters (i.e.,DNA copy number changes, point mutations,activation of signaling pathways) outlined atleast two clear-cut groups of HCCs char-acterized by either activation of the WNTsignaling pathway or overexpression of genesimplicated in cell cycling and proliferation(43). The fact that different research teamsidentified both classes, using different genomicplatforms, after studying a wide clinical rangeof human HCCs, guaranteed their robustness(45–47). Subsequent analysis suggested thatthe molecular cluster characterized by pro-liferation signals was polymorphic. Withinthis class, other signatures were obtainedafter supervised analysis of different signalingcascades, such as MET (48) and TGF-β (49).In addition, genomic data mining integratinginformation from tumors and cells from otherspecies—i.e., comparative functional genomics
(50)—identified a group of HCCs with signifi-cant similarities to an expression pattern foundin rat fetal hepatoblasts (51), suggesting thattumors with this hepatoblast signature could bederived from progenitor cells. Consequently,microarray data can be used not only for patientclassification but also to determine cellularlineages within tumors. Upon reemergence ofthe cancer stem cell theory (52), another studyalso addressed the cell-of-origin issue in HCCby means of gene expression data analysis.These authors identified a gene signatureenriched in genes functionally linked to organdevelopment [i.e., EpCAM signature (53, 54)].
Apart from obtaining new biological andlineage insights in HCC, all these studiesalso aimed to correlate molecular signatureswith patient outcome. In general, those signa-tures included in the proliferation class weresignificantly correlated with worse prognosis(Table 1). However, the lack of external repli-cation of any of these prognostic signaturesis a major drawback for clinical translation.Also, there are some concerns regarding thelow prognostic relevance of some of the clinicalvariables included in the multivariate analysisreported in most of these studies.
Gene expression data obtained from adja-cent nontumoral liver tissue has also proved
Table 1 Relevant studies predicting outcome based on whole-genome expression data
StudiesNumber of
patientsPredominant etiology
of cirrhosis EndpointSignature associated with
poor clinical outcomeGenomic information from tumorYamashita et al. (53, 54),Ye et al. (57)
Lee et al. (47, 51),Kaposi-Novak et al. (48),Coulouarn et al. (49)
138 HBV (57%) Overall survival Class A (47)/Hepatoblast(51)/Met+(48)/TGFB-Late (49)
Chiang et al. (46),Villanueva et al. (56)
232 HCV (61%) Tumor recurrence Proliferation
Genomic information from cirrhosisBudhu et al. (55) 210 HBV (96%) Intrahepatic metastasis Metastasis inclined
microenvironmentHoshida et al. (19) 307 HCV (53%) Overall survival, tumor late
recurrence (>2 years)Poor prognosis signature
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extremely useful for patient classification. In-deed, two seminal studies have clearly demon-strated the importance of the microenviron-ment for accurate prognosis prediction inHCC. The first study focused on HBV patientswith advanced tumors treated with surgical re-section and defined a signature that predictsearly recurrence (<2 years) (55). The secondstudy analyzed mostly HCV-related early HCCpatients treated with surgical resection andidentified a gene signature that predicted over-all survival (19). The signature was capturingbiological signals related to high risk of devel-oping a late recurrence (i.e., de novo tumorsin a cirrhotic liver). Notably, the two signaturesare complementary and reflect different dimen-sions of the so-called field effect: the first is a ge-nomic portrait of a metastatic-favoring milieu,whereas the second is more related to high riskfor HCC development (Table 1) (46–49, 51, 53,54, 56, 57).
We speculate that prognosis prediction inHCC rests on three pillars: (a) clinical param-eters, as described in the BCLC algorithm,(b) genomic data obtained from the tumor, and(c) genomic data obtained from the adjacentnontumoral tissue. Each prognostic componentmust be weighted according to the clinical stageof the disease, as illustrated in Figure 1. Webelieve that gene alterations of adjacent non-tumoral tissue are more relevant in early cases,whereas those of the tumor are more impor-tant in advanced cases. The outcome of earlyHCC properly treated by resection or localablation would mainly be determined by lateevents, particularly risk of late recurrence (denovo tumors), which might outweigh the riskof true metastases. Genomic predictors of denovo HCCs are coded within cirrhotic tissue,the preneoplastic milieu responsible for HCCdevelopment in the first place (19).
MOLECULAR THERAPIES:BEYOND SORAFENIB
Molecular targeted therapies represent thedawn of a new era in the management of can-cer patients. More than ten molecular therapies
have been approved in oncology as a result ofsurvival improvements in patients with breast,colorectal, non-small-cell lung, renal, and headand neck cancers. Molecular therapy also rep-resents a breakthrough in HCC now that so-rafenib, a multi-target therapy with blockadeactivity against RAS, VEGFR, and PDGFR,has achieved positive results (6).
The unquestionable survival benefits ob-tained in a phase III clinical trial had severalremarkable consequences. First, sorafenib rep-resents the first systemic therapy ever found tobe effective in advanced cases, after 30 years ofresearch. Second, it is the proof of concept thata molecular therapy is effective in this other-wise chemoresistant cancer. Third, the magni-tude of survival benefit measured by the haz-ard ratio (i.e., hazard ratio of 0.69) is similar tothat obtained with other consolidated molecu-lar therapies in oncology. As a consequence, thistherapy has been adopted in clinical guidelinesas the standard of care in patients with advancedand disseminated HCC (36).
The advent of sorafenib constitutes afirst step in the complex management of thisdisease, which should be complemented withother molecular approaches. Even thoughour understanding of HCC genomics is stillelementary, certain evidence implicates severalsignaling cascades in the molecular pathogen-esis of HCC, including several pathways suchas EGFR-RAS-MAPKK, IGF, mTOR, MET,FGF, Wnt-β catenin, and angiogenesis (VEGF,PDGFR) (58). Other pathways involved in hep-atocarcinogenesis, such as Jak-STAT, TGF-β,and Hedgehog, need further attention to definetheir relevance and therapeutic potential.
A plethora of targeted therapies have beentested in preclinical models of HCC aiming toestablish proof of principle for testing novelagents in humans. A comprehensive analysis ofthe current targeted therapies tested in clinicaltrials and their mechanism of action has beenreviewed elsewhere (59). Preliminary results inearly trials provide some insights on the anti-tumoral effect of drugs and drug combinationsblocking EGFR, VEGF and PDGFR (Table 2)(6, 21, 22, 60–66). Interpretation of these
Table 2 Phase II and III trials published using molecular targeted therapies in HCC
TrialNumber of
patients Treatment TargetResponse
rateSurvival in the
treatment arm (months)Phase IIILlovet et al. (6) 602 Sorafenib BRAF/MEK/ERK,
VEGFR, PDGFR2.3% 10.7
Cheng et al. (62) 271 Sorafenib BRAF/MEK/ERK,VEGFR, PDGFR
3.30% 6.5
Phase IIAbou-Alfa et al. (60) 137 Sorafenib BRAF/MEK/ERK,
VEGFR, PDGFR8% 9.2
Yau et al. (66) 51 Sorafenib BRAF/MEK/ERK,VEGFR, PDGFR
8% 5
Faivre et al. (67) 37 Sunitinib PDGFR, VEGFR,KIT
2.7% 8
Zhu et al. (68) 34 Sunitinib PDGFR, VEGFR,KIT
2.9% 9.8
Siegel et al. (64) 46 Bevacizumab VEGFR 13% 12.4Thomas et al. (22) 40 Erlotinib +
bevacizumabEGFR, VEGF 25% 15.75
Thomas et al. (65) 40 Erlotinib EGFR 0% 10.75Philip et al. (21) 38 Erlotinib EGFR 9% 13Ramanathan et al. (63) 57 Lapatinib EGFR, Her2/neu 5% 6.2Asnacios et al. (61) 45 GEMOX + cetuximab EGFR 20% 9.5
trials, however, is hampered by the lack ofa common selection criteria of the studypopulation—for instance, including patientswith so called unresectable HCC, as opposed tothe well-characterized subclasses BCLC stage Band C (69). Current research is now aiming tocombine molecular therapies, mainly sorafenibwith EGFR inhibitors (e.g., erlotinib), mTORinhibitors (e.g., everolimus), c-MET inhibitors,or IGF inhibitors. Toxicity will represent themain challenge of such combinations. In addi-tion, alternative antiangiogenic therapies (e.g.,bevacizumab, sunitinib, and brivanib) are be-ing heavily tested. Future research is expectedto identify new compounds to block impor-tant undruggable pathways, such as Wnt sig-naling, and to identify new oncogenes as targetsfor therapies through novel high-throughputtechnologies.
Trial design has also changed in response tothe results of the sorafenib phase III study. Re-cent guidelines have established a new frame-
work for the design of clinical trials in HCC(36). Survival remains the main endpoint tomeasure effectiveness in phase III studies, butrandomized phase II trials with a time-to-progression endpoint are proposed as pivotalfor capturing benefits from novel drugs. Be-cause of the heterogeneity of target popula-tions, an internal control arm is included inrandomized phase II studies to guarantee amore accurate identification of signals of ef-ficacy. In addition, a time-to-event endpointwas proposed. The survival benefits of so-rafenib were not followed by a correspondingimpact in tumor shrinkage (objective responses<3%), a phenomenon also observed with othermolecular therapies (e.g., temsirolimus in re-nal cancer and bevacizumab in liver metastasesof colorectal cancer). These drugs can delaytumor progression in a manner that affectssurvival outcomes. Thus, a time-to-event end-point (e.g., time to progression) might be moreappropriate.
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HCC is highly heterogenic from a ge-nomic perspective, and therefore, it is likelythat several drivers (i.e., oncogene addictionloops) are responsible for proliferation of dif-ferent tumor subclasses. The proper identifi-cation and characterization of new targets inexperimental models of HCC will facilitate
generation of mechanistic hypotheses and thedescription of potential biomarkers of responseto novel drugs. Ultimately, the translation ofthese results to clinical trials will allow a morepersonalized approach in which therapy is tai-lored to the molecular background of the indi-vidual tumor and its adjacent tissue.
DISCLOSURE STATEMENT
Prof. Josep M. Llovet receives consulting fees and research grants from Bayer HealthCare Phar-maceuticals, and consulting fees from Bristol-Meyers-Squibb and Novartis.
ACKNOWLEDGMENTS
Augusto Villanueva is a recipient of a Sheila Sherlock (European Association for the Study ofthe Liver) fellowship. Beatriz Mınguez is the recipient of a grant from Programa de Estanciasde Movilidad Postdoctoral en el Extranjero incluidas las ayudas MICINN/Fulbright (EX 2008-0632). Alejandro Forner is partially supported by a grant from the Instituto de Salud CarlosIII (PI 05/645). Maria Reig is funded by a grant from the BBVA Foundation. Josep M. Llovethas grants from National Institute of Health-NIDDK 1R01DK076986-01, National Institute ofHealth (Spain) grant I+D Program (SAF-2007-61898), and the Samuel Waxman Cancer ResearchFoundation.
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