Genetic profiling of hepatocellular carcinoma using next-generation sequencing

Accepted Manuscript Review Genetic profiling of hepatocellular carcinoma using next-generation sequencing Kornelius Schulze, Jean-Charles Nault, Augusto Villanueva PII: DOI: Reference:

S0168-8278(16)30249-5 http://dx.doi.org/10.1016/j.jhep.2016.05.035 JHEPAT 6133

To appear in:

Journal of Hepatology

Received Date: Revised Date: Accepted Date:

28 January 2016 5 April 2016 25 May 2016

Please cite this article as: Schulze, K., Nault, J-C., Villanueva, A., Genetic profiling of hepatocellular carcinoma using next-generation sequencing, Journal of Hepatology (2016), doi: http://dx.doi.org/10.1016/j.jhep.2016.05.035

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Title: Genetic profiling of hepatocellular carcinoma using next-generation sequencing Authors: Kornelius Schulze1; Jean-Charles Nault2,3,4; Augusto Villanueva5,6 Affiliations: (1) I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (2) Unité Mixte de Recherche 1162, Génomique fonctionnelle des tumeurs solides, Institut National de la Santé et de la Recherche Médicale, Paris, France (3) Liver unit, Hôpital Jean Verdier, Hôpitaux Universitaires Paris-Seine-Saint-Denis, Assistance-Publique Hôpitaux de Paris, Bondy, France (4) Unité de Formation et de Recherche Santé Médecine et Biologie Humaine, Université Paris 13, Communauté d’Universités et Etablissements Sorbonne Paris Cité, Paris, France (5) Division of Liver Diseases, Liver Cancer Program, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY (6) Division of Hematology and Medical Oncology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY

Correspondence: Augusto Villanueva, MD, PhD 1425 Madison Avenue, Box 1123, RM 11-70E New York, NY 10029, USA Conflict of interest: None relevant to this publication Acknowledgments: AV is the recipient of the American Association for the Study of Liver Diseases Foundation Alan Hofmann Clinical and Translational Research Award.

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KEY POINTS -

Next-generation sequencing has helped deciphering the mutational landscape of HCC, including main potential drivers (e.g. TERT, CTNNB1, TP53, ARID1A/2, AXIN1).

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Mutational signatures act as molecular fingerprints that will help to understand the mechanisms of carcinogenesis and identify new risk factors of HCC development.

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Telomerase reactivation is a key event of malignant transformation of hepatocytes. TERT promoter mutations are the most prevalent genetic event both in HCC and preneoplastic lesions (dysplastic nodules) and correlate with aberrant TERT expression.

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Hepatitis B virus and Adeno-associated Virus type 2 can induce direct oncogenesis via clonal integrations on CCNE1, CCNA2 or TERT.

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Clinical implementation of sequencing data in HCC patients is limited by the lack of drugs in advanced clinical development targeting the most prevalent mutations.

SUMMARY Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, both clinically and from a molecular standpoint. The advent of next-generation sequencing technologies has provided new opportunities to extensively analyze molecular defects in HCC samples. This has uncovered major cancer driver genes and associated oncogenic pathways operating in HCC. More sophisticated analyses of sequencing data has linked specific nucleotide patterns to external toxic agents and defined so-called ‘mutational signatures’ in HCC. Molecular signatures, taking into account intra- and inter-tumor heterogeneity, and their functional validation could provide useful data to predict treatment response to molecular therapies. In this review we will focus on the current knowledge of deep sequencing in HCC and its foreseeable clinical impact. 2

Introduction Hepatocellular carcinoma (HCC) is the second cause of cancer-related mortality worldwide [1]. Its mortality is rising and considering the dismal results of recent clinical trials testing systemic agents [2], it seems more difficult to treat than initially anticipated. Each HCC is composed of a unique combination of somatic alterations including genetic, epigenetic, transcriptomic and metabolic events that form its unique molecular fingerprint [3, 4]. Regarding genetic changes, the progressive accumulation of mutations in cancer cells is the result of spontaneous events in the context of enhanced cell division, exposure to viruses (e.g., hepatitis B), carcinogens (e.g., aflatoxin B1) and defects in the DNA repair processes [5]. Moreover, the strong association between cirrhosis and HCC could be partially explained by an accelerated acquisition of genetic alterations in senescent cirrhotic hepatocytes exposed to chronic inflammation and oxidative stress [6]. Genome wide sequencing using nextgeneration technologies has exponentially improved our ability to explore the cancer genome [7]. Identification of the key driver genes and mechanisms underlying mutation occurrence could help understand HCC pathogenesis and develop new therapeutic strategies [2]. Herein, we will review the main advances in our knowledge of the HCC genome obtained by nextgeneration sequencing (NGS) and its potential future impact in clinical practice.

Methodological insights of next generation sequencing It took almost 40 years between the identification of the structure of DNA [8] by Watson, Crick and Wilkins (Nobel Prize 1962) and the first draft of the complete sequence of the human genome after 10 years of work for almost 4 billion US dollars [9]. Next-generation sequencing (NGS) technologies developed in the beginning of the 21st century considerably accelerated our ability to explore the DNA structure at a significant lower cost (less than 3

$5,000 for a whole human genome performed in less than 24 hours) [10]. NGS generates from 10 to >100 of separate DNA sequences at each genomic position, referred as ‘massive parallel sequencing’. This means that several molecules of DNA are simultaneously sequenced at the same base of the genome (each sequence being called a ‘read’) [10]. The mean number of reads obtained across the genome defined the sequencing depth and varies from 60-80X (usual for whole exome) to >200X (ultra-deep sequencing that allow to identify subclonal mutations present only in a subset of tumor cells) [7, 10]. The comparison of the tumor genome with its non-tumor counterpart in the same individual allows identifying genetic alterations present only in the tumors, the so-called ‘somatic events’, including: 1) substitution of a single nucleotide or small deletion/insertion; 2) structural variations at the chromosome level like homozygous deletion, focal DNA amplifications, gains and losses of chromosomes and chromosomal translocation [11, 12]. NGS encompasses a set of different techniques, whole genome/exome sequencing, RNA sequencing and targeted sequencing of panel of genes that allow for such in-depth comparisons. Whole exome sequencing (WES) explores the complete sequence of the coding genome (i.e., exons), allowing the identification of single nucleotide variations, small deletions and insertions, and chromosomal gains and losses that potentially affect protein structure and function [7]. Whole genome sequencing (WGS) analyzes the complete sequence of the genome including coding region but also non-coding regions and allow also to detect chromosomal translocations [7]. RNAseq is also extremely accurate to study gene expression, as well as to identify aberrant fusions or chimeras, alternative splicing, aberrant gene editing and study RNA polyadenylation [7]. Moreover, next generation sequencing could be used to sequence a predefined panel of genes. This method is frequently used in clinical practice to quickly identify the presence of mutations in genes for which a therapy is available [13].

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The mean number of mutations in coding sequence per tumors vary from less than 10 in pediatric or hematological malignancies to more than hundred to thousands of mutations in lung cancer, melanoma or microsatellite instable colorectal cancer [14]. WES studies have revealed that the mean numbers of somatic mutations in coding sequence vary from 40 to 80 per tumors in HCC and occurs both in driver and passenger genes [11, 15, 16]. A cancer driver is defined as a molecular alteration either cell or non-cell autonomous, that contributes to tumor evolution at any stage, from cancer initiation to metastasis and resistance to therapy [17]. Conversely, mutations in passenger genes have no functional consequences and occurred randomly in the genome [18].

Mutational landscape in hepatocellular carcinoma The accumulation of alterations in cancer driver genes and associated pathways are major triggers for hepatocarcinogenesis and tumor progression. Specific discrepancies in HCC mutation rates of major cancer drivers are thought to be dependent on the clinical profile of each patient such as etiology of the liver disease, stage of cancer progression, selective pressure under treatment, and presence or not of an underlying chronic liver disease. Therefore, deciphering the mutational landscape of HCC could help to understand the initial events of hepatocarcinogenesis (‘cancer gatekeepers’) and reveal a more precise set of putative options for both chemoprevention and primary treatment. The current status of reported NGS analyses in HCC includes close to 1,400 human samples. Most of these samples were obtained from resection specimens from patients with early stages, which represent less than 30% of newly diagnosed HCC worldwide. This has provided new insights into the complex molecular pathogenesis of HCC, including the identification of

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novel oncogenic pathways and cancer driver genes [15, 16, 19-24]. Table 1 summarizes the most relevant NGS studies performed so far in HCC [15, 16, 19-24]. Aberrant TERT activation – via promoter mutations, viral integrations or focal amplifications – is the most common somatic alteration observed in HCC (~70%) (Figure 2) [15, 16]. Hence, we will discuss TERT in more details in a dedicated section of this review. Following TERT mutations, the most frequent somatic mutations affect CTNNB1 (~30%), coding for βcatenin, and TP53 (~30%) [15, 16, 19-24]. In addition to CTNNB1, inactivating mutations of other members of the WNT pathway such as AXIN1 (11%), AXIN2 (1%), APC (1%), or ZNRF3 (3%) are also recurrently described in HCC samples [15, 16]. Interestingly, and despite belonging to the same pathway, genetic alterations in CTNNB1 and AXIN1 are mutually exclusive [16]. Similarly, inactivating mutations of TP53 are rarely found in conjunction with CTNNB1, which seems to delineate clear-cut molecular pathways during HCC evolution. In addition to impaired cell cycle control, alterations in chromatin remodeling have emerged as a major de-regulated pathway in HCC, including recurrent inactivating mutations of ARID1A (13%) and ARID2 (7%) as well as mutations of the KMT2 gene family (mutations of KMT2D (6%), KMT2C (2%) and KMT2B (3%)), which codes for histone methyl transferases [15, 16, 20, 23, 24]. Like in most solid tumors, besides this set of relatively frequent mutations, NGS revealed a large number of low-frequency somatic mutations that affect multiple genes, including cell cycle control (ATM (6%), CDKN2A (9%), RB1 (4%)), PI3K/mTOR signaling (TSC2 5%, TSC1 3%, PIK3CA 2%, DAPK1 3%, MTOR 2%), MAP kinase signaling (RP6SKA3 7%, HGF 3%, NTRK3 3%, EPHA4 3%), apoptosis, hepatic differentiation (ALB 13%, APOB 9%, HNF1A 5%), epigenetic regulation, oxidative stress (NFE2L2 6%, KEAP1 4%), JAK/STAT (IL6ST 3%, JAK1 1%), and TGFß signaling (ACVR2A 4%) (Table 1) [15, 16, 19-24].

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Along with mutations, DNA copy number alterations (CNA) are frequent genetic events in HCC. Broad genomic deletions and gains have been identified affecting 1p, 4p-q, 6q, 8p, 13pq, 16p-q, 17p, 21p-q, 22q and at 1q, 5p, 6p, 8q, 17q, 20q, Xq [15, 16, 20, 23, 24]. Recurrent homozygous deletions affected genes such as AXIN1, CDKN2A/CDKN2B, CFH, IRF2, MAP2K3, PTEN, PTPN3, RB1, RPS6KA3, whereas high-level focal amplifications affect 6p21 and 11q13, locus for VEGFA (1%) and FGF3/4/19/CCND1 (4%), respectively. Highlevel CNA in these loci were further validated in other studies using fluorescence in situ hybridization [25]. Broader DNA gains have also been reported to involve JAK3 (3%), MET (1%), and MYC (<1%) [15, 16, 20, 23, 24]. There is data suggesting the direct involvement of CNA in tumor progression such as the association of focal amplifications in FGF19 with later disease stages [16], or the fact that its selective blockage shows anti-tumor effects in experimental models [26]. Similarly, VEGFA amplifications provide a non-cell autonomous mechanism for sorafenib sensitiveness [27]. Differences in mutation rates of cancer drivers and associated pathways among different studies may be partially due to clinical heterogeneity. Differential risk profiles affect the background etiology for liver disease, degree of liver dysfunction, and tumor stage. So far, it has been shown that HBV-related HCC are enriched in inactivating mutations of TP53 and KMT2B leading to a more frequent involvement of cell cycle control/apoptosis and epigenetic regulation in HBV-related cases. Moreover, patients infected with HBV in high endemic regions such as Sub-Saharan Africa or East Asia harbor significantly higher rates of the characteristic aflatoxin B1 (AFB1)-related R245S somatic TP53 mutation [16]. In contrast, TERT promoter mutations, CTNNB1 activating mutations, ARID1A inactivating mutations and alterations in SMARCA2, HGF, RB1, and CDKN2A are more frequent in alcohol-related HCC. In Japanese patients, ARID1A inactivating mutations were significantly enriched in non-HBV and non-HCV patients, suggesting a key tumor suppressor function of SWI/SNF complexes in 7

metabolic/toxic rather than virus-related HCC [15]. Interestingly, in a small subgroup of HCC without known risk factors, an enrichment of alterations in IL6ST and less frequent TERT promoter mutations were identified [16]. Specific mutation enrichment of tumors related to HCV, hemochromatosis, or non-alcoholic steatohepatitis (NASH) have not been identified so far. Despite certain associations between genetic and clinical features, such as TP53 mutations and HBV infection, they are unable to clearly distinguish between etiological or clinical subgroups. Likely, mutational signatures, which cover more than one single molecular aspect, might be superior to find more robust associations. Mutational signatures in hepatocellular carcinoma In addition to identifying mutations in specific genes and their potential contribution to the malignant phenotype, a more global view on mutational patterns has been recently developed at the Wellcome Trust Sanger Institute. The so-called ‘nucleotide or mutational signatures’ link distinct intrinsic processes such as defective DNA repair or exposure to external toxic agents (e.g., UV light, tobacco) with specific patterns of mutations at the nucleotide level. Taking into account a nucleotide substitution site and in addition its adjacent 3’ and 5’ nucleotide, the frequency of 96 possible nucleotide triplets can be used to describe a specific mutational pattern, named “mutational signature”, of a given tumor sample as shown in different malignancies [14, 28, 29]. An extensive pan-cancer study including more than 7,000 samples identified 8 mutational signatures associated with HCC (signatures 1A, 1B, 4, 5, 6, 12, 16, 17) [14]. Mutational signatures 1A/B are associated with age, signature 4 with smoking, and signature 6 with defects in DNA repair. Strikingly, signature 16 is exclusive to HCC, and like signatures 5, 12, and 17, a direct genotoxic agent cannot be discarded [14]. Recently, two large-scale NGS studies from Japan and France have focused on primary liver cancer and shed more light on global mutational patterns [15, 16]. The Japanese group discovered that distinct mutational 8

signatures are associated with ancestry groups in their patient cohorts from Europe, Japan, and American Asians [15]. In parallel, two novel mutational signatures (signature 23 and 24) were reported in the French study that included samples from France, Italy, and Spain [16]. Signature 23 was identified in an HCC sample from a woman containing black pigments of mineral silica in the non-tumor and non-cirrhotic liver tissue, but without any identifiable risk factor. The tumor was hyper-mutated harboring >6,000 mutations, which altogether suggest that a new mutagenic mechanism remains to be unraveled [16]. Mutational signature 24 was spotted in 5 male patients from Africa, harboring characteristic AFB1-related R245S somatic mutations in TP53. Thus, this signature 24 is indicative for the AFB1 exposure [16]. Finally, an aristolochic acid-like mutational signature (A:T to T:A transversions located mainly in the non-transcribed strand) has been identified in a subset of HCC, suggesting a carcinogenic role of aristolochic acid in liver cancer [30].

Genetic basis of telomerase reactivation in hepatocellular carcinoma Telomeres are short non-coding DNA repeats (TTAGGG) localized at the extremity of the chromosome and coated by sheltering proteins [31]. They protect coding regions from DNA losses induced by the shortening of the end of the chromosome due to the end replication problem observed during cell division [32, 33]. However, at each round of cell replication, telomeres shortened and, when they reach a critical point, cell senescence is triggered through induction of the P53/P21 and P16/RB checkpoints [33]. The telomerase complex allows the synthesis of telomeres and avoids cell death in physiological events that require cell proliferation such as embryological development or organ regeneration [33]. This complex is composed of the catalytic enzyme (the telomerase reverse transcriptase TERT), the RNA template (telomerase RNA component TERC) and dyskerin [31]. Interestingly, the only

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limiting factor of the telomerase complex is TERT, which is shutdown in most of adult cells [32]. Telomerase reactivation in HCC is a key event of malignant transformation that allows for unrestrained proliferation of tumor cells [34]. At the end of the nineties, several studies have demonstrated that telomerase was not expressed in normal and cirrhotic liver whereas more than 90% of the HCC harbored an increased activity of telomerase due to TERT re-expression [35-37]. However, the genetic basis of TERT reactivation was unknown until 2013 [34], when somatic mutations at 2 hot spots in the TERT promoter were identified in 60% of HCC patients [38, 39]. These mutations created an ETS/TCF transcription factor binding site and induced telomerase promoter activity and TERT transcription [40-42]. GABP was recently proposed as the transcription factor that binds to the mutated region [43]. Moreover, TERT promoter mutations were the only recurrent somatic variants observed in premalignant lesions developed on cirrhosis (low grade dysplastic nodules 6%, high grade dysplastic nodules 19%) and in early HCC (61%) [44]. It suggests that TERT promoter mutations were involved in tumor initiation and malignant transformation through telomerase reactivation whereas other somatic genetic alterations such as TP53 and CTNNB1 are more involved in later phases of tumor progression [15, 44, 45]. Moreover, TERT promoter mutations were not identified in hepatocellular adenoma, a rare benign hepatocellular tumor developed in young women taking oral contraception [46, 47]. However, in 3-5% of the cases, hepatocellular adenoma could derive in HCC due to the combination of CTNNB1 and TERT promoter mutations [38, 47, 48]. Interestingly, seminal NGS studies failed to identify these mutations in HCC because the TERT promoter, a non-coding region, was not sequenced by whole exome and the sequences of the non-coding regions obtained in whole genome studies were not properly analyzed because there were not considered relevant at that time [19, 20, 49] The discovery of frequent 10

somatic TERT promoter mutations as well as rare mutations in non-coding region of PLEKHS1, WDR74 and SDHD in several types of cancers has launched a strong interest about the “dark matter” of the tumor genome [50-52]. As mentioned earlier, focal amplifications of TERT have been reported in 3-5 % of HCC and were associated with mRNA overexpression [15, 16]. Finally, clonal integrations of HBV in the TERT gene were recurrently described in HBV-related HCC (10 to 15%) [53, 54]. Altogether, these three genetic alterations, TERT promoter mutation, TERT amplification and HBV integration in TERT, were observed in around 50 to 70% of the HCC and were mutually exclusive suggesting a robust functional redundancy [39].

Consequences of viral infection for the genome of hepatocellular carcinoma The most common mechanism leading to HCC in patients with chronic viral infection remains the occurrence of chronic liver disease and cirrhosis due to persistent inflammation and oxidative stress [3]. This mechanism explains most HCV-related HCC since these patients almost always develop HCC on cirrhosis. A direct oncogenic role of HCV proteins is still controversial, and since HCV is an RNA virus no integrations in the tumor genome have been described [55]. In contrast, a direct oncogenic effect of HBV has been described that explains the occurrence of HCC on non-fibrotic liver in HBV-infected patients [56]. More recently, the adeno-associated virus type 2 (AAV2) has also been associated with HCC development [57] (Figure 3). Hepatitis C related liver carcinogenesis Development of chronic liver injury explains most HCV-related HCC since these patients almost always develop HCC on cirrhosis. A direct oncogenic role of HCV proteins is still controversial, and since HCV is an RNA virus no integrations in the tumor genome have been 11

described [55]. However, an in vitro study has suggested that NS3, N4B and NS5B and HCV core protein could promote malignant transformation of fibroblasts [58]. Moreover, a mouse model expressing HCV structural protein developed HCC [59]. All these data suggest that HCV could have direct oncogenic properties even though it remains debated in the literature. Furthers details on HCV induced carcinogenesis are provided in specific reviews [60]. Direct oncogenic mechanisms of hepatitis B virus HBV is a DNA virus of 3,200 pair bases that codes for structural (viral surface proteins, HBeAg, HBcAg) and non-structural (viral polymerase, HBx) proteins. In normal hepatocytes, HBV is stocked in a circular form (covalently closed circular molecule, the cccDNA) in the nucleus but also is integrated into the human DNA [57]. Oncogenic properties of the HBx have been described in experimental models expressing HBx protein [61, 62]. In addition, HBx can also bind to the mitotic spindle, disturb chromosomal segregation and participate to the chromosomal instability observed in HBV related HCC [63, 64]. The other key mechanism observed in vivo that explains the direct oncogenic effect of HBV is known as insertional mutagenesis [65]. This has been described in other DNA virus that integrate in the human genome such as Merckel Cell Polyomavirus and Human Papilloma Virus [66]. Several studies highlighted that HBV integration in the tumor genome can induce human DNA deletion at the integration sites, promote inactivation of tumor suppressor genes and chromosomal instability [67]. Viral DNA sequences integrated in the tumor genomes could have also recombigenic activity [68]. In some cases, the integration of a viral DNA sequence that bears enhancer and promoter activities near a gene involved in carcinogenesis could modulate the expression and function of this gene and promote clonal proliferation and malignant transformation [66]. In HBV, this mechanism was initially described in the eighties by the group of Christian Brechot and Pierre Tiollais [69-71]. The recent analysis of HBVrelated HCC by NGS allowed drawing the precise landscape of clonal viral integrations in the 12

tumor genome [54, 72, 73]. HBV integrations in non-tumoral genome were described as nonclonal random events [54]. In contrast, in HBV-related HCC, only a fragment of the virus was clonally integrated in tumor hepatocytes near cancer genes [72]. The part of HBV integrated in the tumor genome frequently included the end of the Hbx gene and the beginning of the precore/core gene [54, 72, 73]. This suggests that these regions have functional consequences where inserted near coding regions. Recurrent clonal integrations of HBV in the HCC genome have been described in the TERT gene (10 to 15%), in KMT2B, a histone methyltransferase gene (5 to 10%) and in CCNE1, coding for cyclin E, a protein of the cell cycle (5%) [53, 54, 72, 73]. Clonal integrations in other cancer genes have been also reported in unique cases such as CCNA2, coding for cyclin A2 or RORA [70, 71]. Interestingly, fusion proteins involving a part of HBV and human genes such as CCNA2 have been described as potential oncogenes [74]. Adeno-associated virus Type 2, a new player in the field Adeno-associated virus type 2 (AAV2), a member of the parvoridae family, is transmitted through the air and considered as non-pathogenic [75, 76]. AAV2 is a defective DNA virus integrated in the human genome in quiescent state without ability to replicate[77]. AAV2 required the enzymatic machinery of a helper virus as adenovirus or herpes virus to produce virions [76]. Due to these properties, AAV2 is commonly used as a vector for gene therapy in humans [78, 79]. A combined approach of classical Sanger and next generation sequencing recently showed insertional mutagenesis of AAV2 in human HCC [57]. We found clonal somatic integrations of AAV2 in less than 5% of HCC, mainly developed on normal liver without a clear etiology. These clonal insertions targeted cancer genes as TERT, KMTB2, CCNA2, CCNE1, 4 genes also targeted by HBV integration, and TNFSF10 that codes for TRAIL, a protein that modulates apoptotic and proliferative pathways [57]. Only a small part of AAV2, the 3’inverse tandem repeat region, was integrated in the tumor genome. 13

Consequently, AAV2 was involved in liver carcinogenesis on normal liver by a mechanism of insertional mutagenesis [80]. However, the reasons of the difference between the high rate of AAV2 infection in humans (60 to 70% of positive antibody) and the low rate of AAV2 related HCC are still unknown [81, 82]. The same percentage of infections was also reported in Epstein Barr virus, a well-known cancer virus that could induce lymphoma, nasopharyngeal carcinoma and gastric cancer [66, 83].

Molecular heterogeneity in hepatocellular carcinoma The issue of cancer molecular heterogeneity has sparked notorious scientific debate in the last years. The concept assumes that somatic molecular alterations in cancer are not uniformly distributed throughout the whole tumor mass. One of the key issues is to determine if this diversity significantly impacts predictions based on single biopsies, and ultimately, clinical decision-making in the precision medicine era. Molecular heterogeneity expands the model of clonal cancer evolution, which was introduced in the mid-70s when Peter Nowell proposed a theory ‘for the evolution of tumor cell populations in terms of a stepwise genetic variation’ [84]. Different cellular subclones would emerge during tumor growth as a result of selective pressure from the microenvironment, carcinogenic exposure, or the random acquisition of novel mutations. It is plausible that not all subclones share the same malignant potential, and hence, those alterations that provide fitness advantages would be inevitably the ideal candidates for therapies. The advent of next-generation genomic technologies, and more recently, single-cell sequencing provided new means to study these events, which will help cataloguing heterogeneity and its influence in cancer progression and treatment resistance. There has been some controversy when defining heterogeneity, sometimes due to unclear terminology. To clarify key concepts, a group of experts met in 2015 to delineate the main themes and establish the basic premises that will shape the field in the near future [17]. This 14

included a consensus definition of key concepts such as trunk mutations. The term trunk or founder mutation refers to those that are present in every cancer cell, what is equivalent to a Cancer Cell Fraction (CCF) of 1. Hence, cells with a CCF < 1 can be considered subclonal. The panel acknowledged that this definition can be misleading since when considering data from a single biopsy the same mutation can be clonal in one sample and subclonal in another upon sequential sampling. Molecular heterogeneity in HCC has two dimensions. One relates to the distinction between multi-centric carcinogenesis (MC) versus intrahepatic metastasis (IM) in patients with multinodular disease. This behavior is almost unique to HCC, based on its frequent development on the background of chronic liver disease. The potential clinical implications of this difference have been largely neglected, despite studies showing different clinical outcome depending on whether multi-nodularity results from MC or IM [85]. An accurate distinction of both entities may also be critical in those patients that developed recurrence after surgical resection. Persistence of the underlying risk factor capacitates the remnant liver to develop IM (early recurrence) or de novo HCC (late recurrence) [86, 87]. Despite the 2-year cut-off is frequently used to differentiate between early and late recurrence, this is clearly not the best way to distinguish both entities; more refined criteria considering actual tumor clonality should be developed. Few studies have focused on the molecular features that differentiate MC from IM, mainly by defining a molecular signature of tumor clonality. Using comparative genomic hybridization to assess DNA copy number changes, one of the first clonality analysis in HCC identified significant higher number of chromosomal aberrations in relapsed tumors compared with second primary cases [88]. Later studies reported a frequency of IM based on somatic DNA changes that ranged from 26% [89] to 63% [85]. There is also speculation about the different contribution of the so-called ‘field effect’ to favor either MC or IM according to the etiology of the background liver disease (HBV versus HCV), but additional 15

data is still needed. A recent study used WES and low-depth WGS to analyze 43 lesions from 10 HBV-related HCCs and concluded that there is considerable heterogeneity in different tumor nodules from the same individual, at least at the DNA sequence level [90]. It remains to be addressed whether this heterogeneity affects main tumor drivers. The second dimension of HCC heterogeneity refers to intra-tumor molecular diversity, which has been extensively studied in other solid tumors [91]. For example, evidence from multiregional NGS studies highlights the importance of sub-clonal structure analysis in clinical trials for primary breast cancer [92]. A recent study also showed differences in biological fitness of cancer subclones in colo-rectal cancer patients, what significantly impacted response to EGFR inhibition [93]. Indeed, there is an increasing interest to ascertain how tumors evolve over time and upon treatment exposure. This is one of the main objectives of the TRACERx clinical trial, designed to explore the impact of plasticity and tumor evolution in therapeutic outcomes for lung cancer patients [94]. There are very few reports on intratumor molecular heterogeneity in HCC. A recent study performed multi-regional sampling on 23 HCC patients (i.e., 120 tumor areas) and analyzed histological and molecular features, mainly TP53 and CTNNB1 mutations [95]. Authors concluded that heterogeneity, either morphological or molecular, could be detected in the majority of cases (20/23, 87%). The design of more recent studies included less number of patients but a more thorough genomic analysis [96, 97]. One of them included sequencing (n=23 samples) and genotyping (n=286 samples) of a 3.5 single-nodule HCC; results showed an extreme genetic diversity that could not be explained under the current Darwinian model of tumor evolution [97]. This study needs further validation, but it points towards a rapid mutation accrual in HCC, which would justify significant heterogeneity even in very small tumors. In summary, there is still much uncertainty on the extent and consequences of molecular heterogeneity in HCC. Accurate

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assessment of driver heterogeneity will also be pivotal to understand resistance to molecular therapies.

Clinical implementation of sequencing data One of the most notorious successes of anticancer therapy came from the selective blockage of cancer drivers [98]. Some of them are the result of aberrant activation of tyrosine kinases due to somatic mutation. Well-known examples include erlotinib in EGFR-mutated lung cancer, vemurafenib in BRAF-mutated melanoma or crizotinib in lung cancer with ALK rearrangements. Unfortunately, biomarker-driven clinical trials haven’t dominated drug development in HCC. Probably this partially contributed to the dismal results of all phase 3 clinical trials reported since the approval of sorafenib in 2007 [2]. Targeted inhibition (e.g., EGFR, MTOR, and FGFR) was explored without a priori selection of those patients with known de-regulation of any of these candidates. It would be interesting to know if patients with inactivating mutations of the TSC1/2 complex, a well-known negative regulator of MTOR signaling, would have benefitted from the mTOR inhibitor everolimus [99]. In this regard, there is currently a phase 2 trial testing MEK inhibition with refametinib in patients with RAS-mutated HCC. However, mutations in RAS are rare events in HCC with a reported frequency of less than 3%. Other examples include tivantinib in patients with high MET expression on immunohistochemistry based on promising data in phase 2 [100], or FGFR4 inhibition in patients with aberrant FGF19/FGF4 pathway activity. It is still unclear if selective inhibition of any of the candidate HCC drivers identified using exome sequencing will result in meaningful clinical responses. Roughly, effective clinical implementation of sequencing data will require: 1) identification of a non-synonymous recurrent somatic mutation in human samples; 2) functional validation of the ‘driver’ properties of the candidate mutation using experimental cancer models; 3) availability of a 17

drug that selectively and effectively antagonizes the phenotypic consequences of the driver mutation, without significant toxicity in cirrhotics; 4) sufficient clonal dominance of the driver mutation (e.g., trunk) to ensure enough antitumor effect and increase patient’s survival. In HCC, one of the main bottlenecks is that there are no drugs available to counteract the most prevalent HCC mutations such as TERT promoter, TP53, CTNNB1, AXIN1, ARID1A or ARID2. As mentioned before, until there is a clear map of heterogeneity in HCC, it will be difficult to anticipate the role of clonal composition in HCC therapeutics. Most of the data reported so far on HCC mutation rate was obtained from surgical specimens. This means that our current understanding of HCC mutational landscape is biased towards patient at early stages, who are candidates to receive surgical therapies (BCLC-0/A as per current EASL guidelines) [101]. Paradoxically, novel targeted therapies are mainly being tested in patients at advanced stages (i.e., BCLC-C) where sequencing data on mutational burden are much scarcer. It is plausible that as cancer progresses mutation rates and signatures may also evolve, what could suggest a different mutational landscape in advanced HCC. This could particularly affect the prevalence of branch mutations, and hence, impact our understanding of key mechanisms of resistance to sorafenib. Data from other tumors demonstrate how patients that initially responded to inhibition of oncogenic addiction loops will invariably develop resistance. Hence, it is likely that therapies against multiple targets may be required to maximize treatment response, similarly to the approach followed with antiviral therapies. However, combinatorial therapies could be limited by toxicity, especially in the setting of cirrhosis. It may become critical to develop methods to capture the whole repertoire of mutations (trunk and branch) present in a giving tumor. Recent developments in the field of circulating nucleic acids (so-called ‘liquid biopsy’) may provide an opportunity to monitor tumor changes minimally invasively. As shown in a

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metastatic breast cancer patient, analysis of circulating DNA enabled detection of mutations from both the tumor and metastasis [102]. Mutation analysis may not only help identify cancer drivers, but they can also provide useful data to predict response to other treatment modalities such as immunotherapies, particularly immune checkpoint blockade. Anti-PD1/L1-based therapies have shown very exciting results in different solid tumors [103, 104]. Data in HCC are still scarce but there are some advance phase trials currently ongoing exploring some of these therapies [105]. Interestingly, response to these therapies was significantly better in patients with colorectal cancer and mismatchrepair status, what was associated with an increased number of somatic mutations [106]. The more mutations, the higher the likelihood of neoantigens, which seems to correlate with response to immune checkpoint blockage [107]. There is also data suggesting that certain oncogenic signals, such as CTNNB1 mutations, can induce immune evasion and resistance to anti-PD-L1 blockage [108]. Data from melanoma links CTNNB1 mutations with reduced dendritic cell recruitment and CD8 activation. Whether this could be a biomarker of lack of response to immune checkpoint inhibitors in melanoma and other types of cancer will require further validation.

Conclusions and future perspectives The tremendous impact that NGS had in biomedical research is indisputable. Like in previous occasions, technological breakthroughs had preceded major scientific discoveries. In the case of HCC, one of the most deadly malignancies known to humans, NGS has provided a comprehensive landscape of recurrence molecular alterations including somatic mutations, chromosomal alterations and viral integrations. Further analytical refinements of NGS data will soon allow to better understand tumor heterogeneity and its potential role in treatment 19

decision-making. Although more knowledge about tumor genetic aberrations is accumulating, it is becoming increasingly clear the need to functionally characterize these aberrations, so we can effectively discern between drivers and passengers. Clearly, the promise of tailored treatments based on individual genetic features is surely closer now thanks to NGS technologies.

20

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26

FIGURE LEGENDS Figure 1: Mutational landscape of HCC Graphical summary of the main mutated genes in HCC and their reported frequency. Not that some mutations may co-exist in the same patient. Data suggest that background etiology impact mutation rate. Most mutations affect 3 genes, TERT promoter, CTNNB1 and TP53. Figure 3: Viral mechanisms of liver carcinogenesis Direct and indirect mechanisms of viral related liver carcinogenesis are represented. Indirect mechanisms are related to the development of cirrhosis triggered by chronic inflammation and oxidative stress induced by chronic viral hepatitis. In vitro studies and mouse model have suggested that HCV proteins could have oncogenic properties even if the relevance in humans remains debated. The direct oncogenic mechanisms are mainly due to action of viral oncoproteins (Hbx in HBV), chromosomal instability induced by HBV integration and insertional mutagenesis (HBV and AAV2) with aberrant regulation of gene expression or induction of fusion protein between human DNA and HBV. The genes targeted by clonal viral integrations are represented in the blue box; some of them are targeted by both HBV and AAV2.

27

Table 1: Overview of the most relevant (based on number of samples analyzed) studies conducted using NGS in HCC. * This study only investigated HBV integration sites. Year Author

Sequencing approach

Number of samples

Etiology

Cirrhosis

Candidate driver

2012

Fujimoto et al.

Whole Genome + Validation set

27 + 120

HCV (52%) HBV (41%)

52%

Guichard et al.

Whole Exome + Validation set

24 + 125

Alcohol (37%) HBV (24%) HCV (19%) NASH (5%)

38%

2012

Huang et al.

Whole Exome + Validation set

10 + 100

HBV (100%)

2012

Sung et al.*

Whole Genome

88

HBV (92%)

66%

2013

Cleary et al.

Whole Exome

87

HBV (43%) HCV (21%) Alcohol (11%)

56%

2013

Kan et al.

Whole Genome

88

HBV (92%)

63%

2014

Ahn et al.

Whole Exome

231

HBV (72%) HCV (10%)

49%

2014

Totoki et al.

Whole Genome + Whole Exome

608

HCV (42%) HBV (23%)

no data

2015

Schulze et al.

Whole Exome

235

Alcohol (41%) HCV (26%) NASH (18%) HBV (14%)

47%

TP53 (52%) ATM (19%) IGSF10 (15%) CTNNB1 (11%) ARID1A (11%) CTNNB1 (33%) TP53 (21%) ARID1A (17%) AXIN1 (15%) RPS6KA3 (10%) CDKN2A (7%) TP53 (27%) ARID1A (13%) SAMD9L (6%) ARID2 (4%) TERT (20%) KMT2B (10%) CCNE1 (5%) SENP5 (3%) ROCK1 (2%) CTNNB1 (23%) TP53 (20%) CPA2 (9%) IGSF3 (9%) KEAP1 (8%) TP53 (35%) CTNNB1 (16%) LRP1B (11%) JAK1 (9%) AXIN1 (5%) TP53 (32%) CTNNB1 (23%) RB1 (8%) AXIN1 (7%) SELPLG (5%) FGF19 (5%) TERT (54%) CTNNB1 (31%) TP53 (31%) ARID1A (8%) AXIN1 (6%) TSC2 (5%) TERT (60%) CTNNB1 (37%) TP53 (24%) ARID1A (13%) ALB (13%) AXIN1 (11%) CDKN2A (9%)

2012

no data

De-regulated pathways

Wnt signaling (49%) p53 signaling (33%) Chromatin remodeling (23%) PI3K/Ras signaling (13%) Oxidative stress (6%)

Wnt signaling (63%) JAK/STAT signaling (46%) Apoptosis (46%) p53 signaling (43%) p53 signaling (37%) Wnt signaling (37%) Chromatin remodeling (34%) Cell cycle (22%) PI3K/Ras signaling (12%) p53 signaling (72%) Telomere maintenance (68%) Chromatin remodeling (67%) Wnt signaling (66%) PI3k/mTOR signaling (45%) Oxidative stress (19%) Telomere maintenance (60%) Wnt signaling (54%) PI3k/mTOR signaling (51%) p53 signaling (49%) MAP kinase signaling (43%) Hepatic differentiation (34%) Epigenetic regulation (32%) Chromatin remodeling (28%)

28

Figure 1 TELOMERE MAINTENANCE TERT

HBV Amp. integr.

Mutations

5%

60%

RB1 mut / del

8%

30% CDKN2A mut CCND1 amp

8%

7%

30%

CCNE1

AXIN2 mut

APC mut

5%

1%

1%

TSC1 mut

DAPK1 mut

5%

3%

3%

Gain of fucntion event Loss of fucntion event

3%

11%

(HBV integr.)

TSC2 mut

2%

AXIN1 mut ZNRF3 mut

CTNNB1 mut

PI3K / MTOR SIGNALING

PIK3CA mut

CHROMATIN REMODELLING

WNT SIGNALING

CELL CYCLE CONTROL TP53 mut

5%

MTOR mut

2%

Etiology enrichments Alcohol HBV

RAS / MAPK SIGNALING RPS6KA3 mut

FGF19 amp

7%

4%

NTRK3 mut

3%

EPHA4 mut

3%

MLL4

ARID1A mut

ARID2 mut

(HBV integr.)

13%

10%

7%

KMT2D mut

KMT2B mut

KMT2C mut

6%

3%

2%

JAK / STAT SIGNALING IL6ST mut

JAK1 mut

3%

1%

OXIDATIVE STRESS NFE2L2 mut

KEAP1 mut

6%

4%

Figure 2 Chronic  hepa,,s  C  

Core   E1   E2   NS2   NS3  

NS4 NS4 NS5 NS5 A   B   A   B  

Single strand RNA virus  

Normal   liver  

?  

Chronic  hepa,,s  B   Strand  -­‐   Strand  +  

  Indirect  mechanisms  of  carcinogenesis             Chronic       Cirrhosis   HCC   hepa??s       Inflamma?on       Necrosis  and  regenera?on   Reac?va?on  of       Oxyda?ve  stress   telomerase       Telomere  shortening         Direct  mechanisms  of  carcinogenesis     Poten/al  oncogenic  proper/es  of  NS3,  N4B  and  NS5B  and  HCV     NS4 NS5 core  protein       Core  NS3   B   B             of  carcinogenesis   Indirect  mechanisms              

Normal   liver  

HBV  

Chronic   hepa??s  

      Cirrhosis    

Inflamma?on     Necrosis  and  regenera?on     Oxyda?ve  stress     Telomere  shortening  

Direct  mechanisms  of  carcinogenesis   Inser/onnal  mutaganesis   Viral  oncoprotein     «  Cis  effect  »     Apoptosis     Mito?c  spindle   HBV  Cancer    gene   Genes  targeted  by   Chromosomal   Cell  cycle   HbX   viral  inser?on   Human   instability       DNA       Induc?on  of  chromosomal   RORA     of  tumor   instability  and  dele?on   DNA  repair   Transcrip?on   suppressor  genes       (HBV-­‐human)   TERT   Fusion  oncogenic  proteins  

   

MLL4   CCNE1   CCNA2  

Adeno-­‐associated     Virus  type  2      

TNFSF10  

Cap    

5’ ITR

Normal   liver   3’ ITR

Monostrand  DNA  

Reac?va?on  of   telomerase  

   

Partially double -stranded circular DNA virus  

Rep  

HCC  

   Direct  mechanims  of  carcinogenesis     Inser/onnal  mutagenesis     «  Cis  effect     »       HBV  Cancer     gene   Human     DNA    

HCC