Hepatocellular carcinoma (HCC) consistently ranks among the most common cancers worldwide and is, currently, the third leading cause of cancer-related death accounting for at least 600,000 deaths annually.1,2 Although in the traditionally high-HCC regions such as Southeast Asia and sub-Saharan Africa, the HCC rate has stabilized and slowly declined due to generalized vaccination programs, the incidence and mortality rates of HCC have doubled in the United States and Europe in the past four decades and are predicted to continue rising.3,4 Although several confounding factors (e.g., immigration from high-incidence countries) contribute to these high numbers in the Western world, HCC is currently among the fastest growing causes of cancer-related deaths in the United States. Small HCCs can be cured by resection and/or liver transplantation. However, at the time of diagnosis, <20% of the patients are eligible for these treatment options.5 These observations make it clear that liver cancer is a major health problem in the United States and Europe and highlight the critical need for both improved understanding and treatment options of this deadly disease. The major etiologic agents responsible for chronic liver disease, cirrhosis, and, ultimately, HCC are known and well characterized (e.g., infections with hepatitis B virus [HBV] and hepatitis C virus [HCV] as well as ethanol abuse). Other etiologic factors include nonalcoholic fatty liver disease (NAFLD) and other metabolic disorders that have become particularly relevant in Western countries due to a sharp increase in prevalence and a high number of HCCs without underlying cirrhosis.6
Molecular mechanisms of liver diseases that are associated with increased risk of HCC as well as cellular alterations that precede HCC have been identified.7,8 Research into the molecular pathogenesis of HCC is currently focused on the interrelationship of abnormal genomics, epigenomics, proteomics, and metabolomics as well as downstream alterations in pertinent molecular signaling pathways. The principal objective of this research is to integrate these new omic data with clinicopathologic features of HCC in order to discover new diagnostic tools, improve treatment options, and implement effective prevention strategies.9
Recent introduction of next-generation whole (epi-)genomic technologies permits simultaneously detection of the expression of tens of thousands of genes in small samples from normal and diseased tissues.10 High- throughput microarray-based technologies and the recent advent of the next-generation sequencing (NGS) provide a unique opportunity to define the descriptive characteristics (i.e., “phenotype”) of a biologic system in terms of the genomic readout (e.g., gene expression, coding mutations, insertions and deletions in DNA, splicing variants, copy number variations [CNVs], and chromosomal translocations). Integrated analysis of biologic systems has caused a paradigm shift in biologic research (i.e., from the classic reductionism to systems biology).11 Fundamental to the systems approach is the hypothesis that disease processes are driven by aberrant regulatory networks of genes and proteins that differ from the normal counterparts. Application of multiparametric measurements promises to transform current approaches of diagnosis and therapy, providing the foundation for predictive and preventive personalized medicine.12
In this chapter, we discuss the molecular hallmarks of hepatocarcinogenesis in the context of next-generation high-throughput genomic technologies, explore implications for clinical and translational efforts, and outline individualized approaches designed for future research into liver cancer.
Генетические изменения в раке печени
A detailed map of the structural variation in the human cancer genome has been generated during the last decade.11 This map reveals that tumor development is the consequence of intragenic mutations in approximately 140 genes, belonging to 12 recurrent signaling pathways regulating three core cellular processes (i.e., cell fate, cell survival, and genome maintenance in the majority of human cancers).13 Hepatocarcinogenesis can, therefore, be considered a multistep process of epigenetic and genetic alterations disrupting these core processes primarily by p53, WNT, β-catenin, MYC, ErbB family, and chromatin modifications.13
Structural variation and chromosomal aberrations in tumors are traditionally regarded as evidence of gene deregulation and genome instability and may facilitate identification of crucial genes and regulatory pathways that are perturbed in diseases.14 Large genome-wide association studies (GWAS) recently identified liver disease– specific susceptibility loci, including in HCC.15,16 Interestingly, some of the revealed genetic changes could not only be specifically linked to etiologic risk factors (e.g., PNPLA3 in NAFLD) but also predicted distinct metabolic phenotypes in affected indicudals and were associated with adverse biologic traits in tumors.17,18 The classical approaches to identify somatic alterations in liver cancer are genome-wide high-throughput microarray technology for single nucleotide polymorphism (SNP) genotyping and array-based comparative genomic hybridization (aCGH). These technologies enable high-throughput analysis of DNA copy number and yield comprehensive information pertinent to determining the molecular pathogenesis of human HCC. Meta-analysis of comparative genomic hybridization studies of chromosome aberrations in human HCC shows that specific chromosomal gains and losses correlate with etiology and histologic grade.19 In HCC, the most frequent amplifications of genomic material involve 1q (57.1%), 8q (46.6%), 6p (22.3%), and 17q (22.2%), whereas losses are most common in 8p (38%), 16q (35.9%), 4q (34.3%), 17p (32.1%), and 13q (26.2%). Deletions of 4q, 16q, 13q, and 8p correlate with HBV infection in the absence of of HCV infection. Chromosomes 13q and 4q are significantly underrepresented in poorly differentiated HCC, and gains of 1q correlate with other high-frequency alterations.20 Amplifications and deletions often occur on chromosome arms at sites of oncogenes (e.g., MYC on 8q24) and tumor suppressor genes (e.g., RB1 on 13q14) as well as at several loci that contain genes with known and/or suspected oncogenic functions (e.g., FZD3, WISP1, SIAH-1, and AXIN2, all of which modulate the WNT signaling pathway). In these meta-analyses, etiology and poor differentiation of HCC correlated with specific genomic alterations. In preneoplastic dysplastic nodules (DNs), amplifications are most frequent in 1q and 8q, whereas deletions occur in 8p, 17p, 5p, 13q, 14q, and 16q.20 Gain of 1q appears to be an early event in DN development, possibly predisposing affected cells to acquisition of additional chromosomal aberrations. However, whereas these studies revealed interesting mechanistic clues for hepatocarcinogenesis, the substantial molecular diversity of alterations in these loci remain a major obstacle and the functional validation of individual genes and the identification of driver genes remains challenging. A systematic strategy to identify potential driver genes by integrating whole-genome copy number data with gene expression profiles of HCC patients was recently introduced.21 Using regional pattern recognition approaches, the authors discovered the most probable copy number–dependent regions and 50 potential driver genes. At each step of the process, the functional relevance of the selected genes was evaluated by estimating the prognostic significance of the selected genes. Further validation using small interference RNA-mediated knockdown experiments showed proof-of-principle evidence for the potential driver roles of the genes in HCC progression (i.e., NCSTN and SCRIB). In addition, systemic prediction of drug responses using the Connectivity Map,22 a compendium of functional connections between drugs and genes, implicated the association of the 50 genes with specific signaling molecules associated with hepatocarcinogenesis (mTOR, AMPK, and EGFR). It was concluded that the application of an unbiased and integrative analysis of multidimensional genomic data sets can effectively screen for potential driver genes and provide novel mechanistic and clinical insights into the pathobiology of HCC. Using a similar approach, Roessler et al.23 applied an integrative approach combining information from high-resolution aCGH and gene expression profiling with clinical data from HCC patient to identify CNV in HCC with functional relevance for tumor progression. The investigation was restricted to genes that showed (1) recurrent CNVs, (2) correlation of the CNVs and the transcriptome, and (3) a selective association to patient’s outcome to distinguish “drivers” from passengers. The authors could demonstrate significant differences in CNVs between patients with good and poor outcome, generated a 10-gene signature as a molecular predictor of patient survival, and validated the signature in several independent cohorts. In extension of this work, the authors recently demonstrated that gene expression profiles of patients with chromosome 8p loss correlate with increased interleukin (IL)-6 signaling.24 Modulation of the chromosome 8p tumor-suppressor genes Src homology 2 domain–containing 4A (SH2D4A) and Sorbin and Src homology 3 domain–containing 3 (SORBS3) were associated with cell growth and clonogenicity in liver cancer. Both tumor suppressors cooperatively inhibited STAT3 signaling and, thus, provided a molecular basis for inhibition of STAT3-mediated IL-6 signaling in HCC. Both these studies elegantly illustrate the power of multilayer integrative analyses to identify the functional significance of genomic alterations in human HCC.
Эпигенетические изменения в раке печени
Epigenetic alterations such as DNA methylation are important in tumor development for many cancers.25 Changes in DNA methylation patterns are believed to be early events in hepatocarcinogenesis preceding allelic imbalances and ultimately leading to cancer progression thereby adding considerable complexity to the pathogenesis of liver cancer.26
Global hypomethylation and promoter hypermethylation in certain cancer-related genes are known drivers of hepatocarcinogenesis associated with both biologic behavior and prognosis.27 Additionally, methylation patterns can be used to classify patients according to different etiologic factors (e.g., HBV, HCV, alcohol).28,29 Moreover, distinct methylation patterns strongly correlate with clinical characteristics of HCC patients.27 Methylation patterns in a 807 cancer-related gene panel could successfully separate primary HCC samples according to their biologic subtype.30 Consistent with previous studies, patients harboring tumors with progenitor cell origin displayed the worst clinical outcome.31 Confirmation of a multistep, epigenetic-driven sequence of molecular alteration in hepatocarcinogenesis could further be demonstrated in HBV-related liver cancers. A stepwise hypermethylation of cytosine-phosphate-guanine (CpG) islands of nine well-described HCC-associated genes was seen from cirrhotic nodules, DNs (low and high grade) to early carcinoma (eHCC) and finally progressed HCC (pHCC).32
More recently, integrative genome-wide methylation analyses of 71 human HCC patients was combined with data from microarray analysis of gene reexpression in four hepatoma cell lines following exposure to DNA methylation inhibitors.33 A total of 13 candidate tumor suppressor genes were identified using this approach and, subsequently, SMPD3 and NEFH were functionally validated as tumor suppressor genes in HCC. Furthermore, it was shown not only that SMPD3 not only affects tumor aggressiveness but also that reduced SMPD3 levels are an independent prognostic factor for early recurrence of HCC.
The prognostic impact of epigenetic alterations in HCC as well as the potential role of DNA methylation markers as biomarkers for HCC were recently explored in 304 HCC tissues.34 A methylation-based prognostic signature was identified using genome-wide methylation arrays covering 96% of known CpG islands and 485,000 CpG. These data were combined with transcriptome microRNA profiling on the same samples for integrative analyses that demonstrated a signature consisting of 36 methylation probes that accurately discriminated survival in HCC patients with different etiologies. The study further confirmed a high prevalence of genes deregulated by aberrant methylation in HCC (e.g., Ras association [RalGDS/AF-6] domain family member 1, insulin-like growth factor 2, and adenomatous polyposis coli) and other solid tumors (e.g., NOTCH3), as well as describing potential candidate epidrivers (e.g., septin 9 and ephrin B2). Thus, the study confirms the utility of methylation analyses for mechanistic and predictive applications in liver cancer.
Although genetic changes in chromatin modulators are among the most common alterations in HCC (see the following text), the role of epigenetic alterations beyond DNA methylation (e.g., modification of histones, such as acetylation, methylation, phosphorylation, ubiquitylation, and sumoylation) are not well studied in HCC.35 In fact, modifications of both repressing (e.g., H3 lysine 27 and histone H3 lysine 9) and activating histone marks (e.g., H3 lysines 4) have significant impact on expression of critical genes associated with hepatocarcinogenesis.36 Applying chromatin immunoprecipitation together with high-throughput sequencing (CHIP-seq) profiling in HCC cell lines was recently used to assess genome-wide chromatin occupancy of the suppressive H3K27ME3 chromatin mark.37 The data revealed that claudin14 (CLDN14) is a direct target for EZH2-mediated H3K27ME3 in HCC and confirmed low expression of CLDN14 in advanced tumor stages. Furthermore, low CLDN14 was an independent predictor of survival of HCC patients. Depletion of CLDN14 substantially restored motility and invasive capacities in EZH2-silenced HCC cells and supported cell epithelial-mesenchymal transition in a Wnt/β- catenin–dependent manner. Results from these preliminary studies highlight the need for additional studies applying whole epigenomic approaches in HCC.
MicroRNAs are epigenetically active small RNAs that are critically involved in regulating protein expression.38 Distinct microRNA expression patterns contribute to the definition of the cellular phenotype, including regulation of proliferation, cell signaling, and apoptosis. Not surprisingly, aberrant expression of microRNAs is associated to cancer initiation, propagation, and progression. Several microRNAs are frequently deregulated in HCC and associated with certain clinicopathologic features.39 Numerous studies demonstrated that microRNAs have essential roles in HCC progression by directly contributing to cell proliferation, apoptosis, and metastasis of HCC and by targeting a large number of critical protein-coding genes involved in hepatocarcinogenesis.40 Profiling of microRNA expression by microarray revealed subclasses associated with clinicopathologic features as well as mutations in several oncogenic pathways such as β-catenin and HNF1A.41 Furthermore, microRNA profiling of 89 HCC samples using a ligation-mediated amplification method revealed three distinct clusters of HCCs reflecting the clinical behavior of the tumors, and the association of the microRNA family miR-517 with increased tumorigenicity of HCC cells in vitro and in vivo.42
Ji and colleagues confirmed the therapeutic potential of microRNA-based treatment modalities in HCC and demonstrated that miR-26 levels are associated with response to adjuvant therapy with interferon -α and developed a simple and reliable companion diagnostic (miR-26-DX) to select HCC patients for adjuvant interferon-α therapy as a first step to successfully translate information from large-scale analyses into the clinics.
Мутационный ландшафт генетических альтераций — следующее поколение
Sophisticated NGS technologies have now been applied in cancer research for a complete and cost-efficient analysis of cancer genomes at a single nucleotide resolution and advanced into valuable tools in translational medicine.14 Implementation of NGS to solid tumors like HCC is challenging as the proportion of normal cells or the stromal composition within a given sample contributes to the genomic signature and therefore may require additional coverage (i.e., read depth).44 Also, HCC often arises in the background of a chronically diseased liver with underlying cirrhosis, fibrosis, or HBV or HCV infection, which may complicate the tumor/normal variant discovery when compared to the peritumoral liver tissue or even blood.45 Accordingly, sequential molecular alterations during human hepatocarcinogenesis from dysplastic lesions to eHCC and ultimately pHCC are not clearly defined. This represents a major challenge in the clinical management of patients at risk. Although MYC activation is associated with early stages of malignant conversion into HCC, detailed molecular sequences that drive premalignant lesions into progressed HCC still remain to be clarified.46 Nault et al.47 recently investigated TERT promoter mutations in a series of 268 liver samples, including 96 nodules developed in 58 patients with cirrhosis and 114 additional cirrhosis. The results demonstrated that TERT promoter mutations progressively increased during stepwise hepatocarcinogenesis and were identified in 6% of low-grade DNs, 19% of high-grade DNs, 61% of eHCCs, and 42% of small and pHCC. The authors concluded that somatic TERT promoter mutations could be useful as a new biomarker predictive of transformation of premalignant lesions into HCC. Also, integrative transcriptome sequencing of tumor-free surrounding liver (n = 7), low- (n = 4) and high-grade (n = 9) dysplastic lesions, eHCC (n = 5), and pHCC (n = 3) from eight HCC patients with hepatitis B infection was recently performed.48 The results indicate that molecular profiles of dysplastic lesions and eHCC are quite uniform. In contrast, a sharp increase in heterogeneity on both messenger RNA and DNA levels is observed in progressed HCC. These molecular alterations result in massive deregulation of key oncogenic molecules such as transforming growth factor β1 (TGFβ1), MYC, phosphatidylinositol 3-kinase (PI3K)/AKT, and suggest that activation of prognostically adverse signaling pathways is a late event during hepatocarcinogenesis (Fig. 56.1). Subsequently, it was shown that the molecular alterations in advanced HCC are relatively wide-ranging with numbers of mutations ranging from 5 to 121 per tumor.49–51 Thus, it has to be assumed that complex interactions of multiple mutations in individual tumors eventually generate HCC.52–55 Although no clear oncogenic addiction has been demonstrated in HCC, a high number of mutations in p53 and Wnt/β-catenin signaling have been detected (Table 56.1). Thus, results from in-depth analyses strengthen current notions that p53 and Wnt/β-catenin signaling are the most common molecular changes involved in HCC development.49 Also, 10% to 28% of HCCs harbor alterations in genes associated with chromatin-remodeling pathways, suggesting a causative association with hepatocyte transformation and highlighting the key role of epigenetics in hepatocarcinogenesis.50,56 However, recent whole- exome/whole-genome sequencing studies of 87 and 88 human HCCs57,58 have confirmed that β-catenin (10% and 15.9%) and TP53 (18% and 35.2%) are the most frequently mutated oncogene and tumor suppressor, respectively.57,58 The study by Kan et al.58 also detected several drugable mutations, including activating mutations of Janus kinase 1 (9.1%), which might provide an option for novel individualized therapeutic interventions. Interestingly, Nault et al.59 identified somatic mutations activating telomerase reverse-transcriptase as both the earliest and the most frequent mutations in human preneoplastic lesions (25%) as well as HCCs (59%) and also associated with activating CTNNB1 mutations. Further, exome sequencing of 243 HCCs identified mutational signatures associated with specific risk factors and found several patterns related to alcohol and tobacco consumption and exposure to aflatoxin B1.50 Finally, disruption of several genes and pathways that might be useful for the selection of suitable patients in a setting of target-enriched clinical trials were identified.
The most integrative view on hepatocarcinonesis was provided by a recent study of The Cancer Genome Atlas (TCGA) Research Network.60 The TCGA investigators performed multilayer molecular analyses of whole-exome sequencing and DNA copy number analyses, DNA methylation, RNA, microRNA, and proteomic expression on 363 HCC cases. Whereas the mutational landscape of alterations was comparable to previous studies, significant alterations by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular subtyping of five data platforms identified three subclusters of tumors associated with distinct prognostic traits. Furthermore, the authors also revealed potential actionable targets in WNT signaling, MDM4, mesenchymal-epithelial transition (MET), vascular endothelial growth factor A (VEGFA), myeloid cell leukemia 1 (MCL1), isocitrate dehydrogenase 1 (IDH1), telomerase reverse transcriptase (TERT), and immune checkpoint proteins, cytotoxic T-lymphocyte antigen 4 (CTLA-4), programmed cell death protein 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1). These comprehensive data will provide a powerful repository for future investigations on molecular hepatocarcinogenesis.
Микросреда рака печени
HCC develops in the background of a chronic liver disease and, in more than 80% of the cases, in a preexisting liver cirrhosis. For a complete understanding of the molecular mechanisms of hepatocarcinogenesis, this primary liver disease causing a chronic inflammatory liver microenvironment has to be appreciated.61 Recent research efforts have focused on the identification of key factors that contribute to the disruption of the liver microenvironment and the generation of an adverse niche(s) that promote hepatocarcinogenesis. Among the most prominent factors involved in the inflammation-fibrosis-cancer axis is the nuclear factor kappa B (NF-κB) pathway.62 The dominant role of this pathway in hepatocarcinogenesis is well documented (reviewed in Li et al.54). However, genetic deletion of the NF-κB master regulator NEMO significantly enhanced liver cancer development in a mouse model, indicating that inhibition of NF-κB may not only exert beneficial effects but also negatively impact hepatocyte viability, especially when NF-κB inhibition is pronounced.
Фигура 56.1. Последовательная эволюция рака печени. Схема упрощает наше понимание развития гепатоцеллюлярной карциномы (HCC). The current concept considers hepatocarcinogenesis a multistep process that develops on the basis of a chronically altered microenvironment (i.e., cirrhosis) and progresses from dysplastic nodules (high grade and low grade) over early HCC to progressed HCC (upper panels). On the molecular level the different stages are characterized by progressive activation of signaling pathways related to oxidative stress, immune response, and proliferation (middle panel). However, the activation of prognostically adverse signaling occurs late during the evolution of liver cancer. During this process, a progressive loss of differentiation with concomitant acquisition of malignant and invasive properties is observed (lower panel). eHCC, early HCC; pHCC, progressed HCC; LGDN, low-grade dysplastic nodule; HGDN, high- grade dysplastic nodule; PI3K, phosphatidylinositol 3-kinase; TGFβ, transforming growth factor β; EMT, epithelial-mesenchymal transition.
The importance of the microenvironment is further highlighted in a recent study demonstrating that transplantation of hepatic progenitor cells gave rise to cancer only when introduced into a liver with chronic damage and compensatory proliferation.64 Interestingly, similar to observations in human hepatocarcinogenesis, progenitor-like cells quiescently resided within dysplastic lesions for several months prior to appearance of HCC. During this period, the progenitor cells acquired autocrine IL-6 signaling that stimulated their in vivo growth and malignant progression, suggesting a general mechanism of progenitor cell–induced HCC. Notably, IL-6 is a highly abundant cytokine in the liver actively contributing to immune surveillance while concomitantly promoting growth and differentiation of epithelial tumor cells via paracrine and autocrine regulatory loops.65,66 IL-6 is also associated with the treatment response to targeted therapies and progression in a variety of cancers (e.g., by promoting immune evasion of cancer cells).67,68 The dual role of IL-6 signaling for both cancer cells and nonparenchymal (e.g., immune cells) cells, therefore, substantially contributes to cross-talk between tumor and the microenvironment.69 This notion is supported by genomic analyses showing that gene sets associated with a poor prognosis in liver cancer contained several downstream targets of IL-6.70
The microenvironment does not, however, only contribute to tumor initiation. Although gene expression profiles of tumor tissue failed to yield significant association with survival, a 186-gene signature generated from the surrounding nontumoral liver tissue was highly correlated with the outcome of patients in a cohort of more than 300 HCCs.70 Consistently, this poor-prognosis signature contained gene sets associated with inflammation such as interferon signaling, NF-κB, and tumor necrosis factor α. Further, gene set enrichment analysis showed that the downstream targets of IL-6 were strongly associated with the poor-prognosis signature, again confirming the importance of this signaling in hepatocarcinogenesis.
Several other immune-related and prooncogenic molecules revealed unexpected tumor-suppressing/tumor- promoting effects when activated in parenchymal and nonparenchymal cell types (e.g., immune cells vs hepatocytes) and different etiologic context (e.g., inflammation, fibrosis, cirrhosis), which underlines the critical importance of the interaction of signals from the microenvironment and the tumor cells for tumor initiation and progression.71,72 Together, these studies demonstrate the complexity of molecular mechanisms influencing the development and progression of liver cancer. The results of these studies also highlight the rationale for therapeutic interventions based on the tumor–microenvironment interaction (e.g., by inhibition of checkpoint molecules such as PD-1/PD-L1 and CTLA-4).73
Таблица 56.1. Основные функциональные сигнальные пути и молекулы в гепатоцеллюлярной карциноме
|Functional process (frequency)||Сигнальный путь||Ассоциированные молекулы (частота%)||Манера действия в опухолях||Фенотипические черты|
|Cell cycle (4%–35%)||p53, RB–E2F||TP53, CKN2A/B, CCND/E1, CDKs, ATM, RB1||Ингибирование||Потеря при прогрессировании опухоли, агрессивный фенотип, механизмы восстановления ДНК повреждений|
|Wnt–β-катенин||CTNNB1, AXIN1+2, APC||Активация||Ранняя и поздняя стадия, геномная стабильность, активация в опухолевых клетках|
|TGF-β||SMADs||Ранний: ингибирование Поздний: активация||Плохой прогноз, метастазирование, опухоль-инициирующие клетки|
|Development and differentiation (2%–55%)||Hippo–YAP||Контроль роста благодаря YAP и MST1 / 2||Активация||Черты стволовых клеток, инициация опухоли, химиорезистентность|
|NF2||Контроль роста благодаря YAP, EGFR||Черты стволовых клеток, инициация опухоли|
|SALL4||NURD комплекс, PTEN||Ингибирование||Развитие и прогрессирование, плохой прогноз, опухолевые клетки|
|IGF||IGF1R, IRF2, IRS, SHP, PI3K||Пренеопластические поражения, ранняя стадия|
|SH2, MAPK, MEK, ERK||Активация||Метастатический потенциал, инвазия|
|Oncogenic signaling (0%–35%)||EGFR||AKT, STATs, RAS/RAF||Агрессивный фенотип, перепрограммирование|
|PI3K–mTOR||PIK3CA, AKT, RPS6KA3, PTEN, TSC1, RAPTOR, RICTOR||Активация||Плохой прогноз, плохо дифференцированные опухоли и более ранние рецидивы|
|VEGF||VEGFR, HIF1α||Агрессивный фенотип, плохой прогноз, метастазирование|
FGF19, FGFRs, SHP2
|Активация||Развитие или прогрессирование|
|PDGF||ROS, PI3K, STAT3, MMPs||Цирроз печени, развитие|
|Immune response (2%–9%)||NF-κB||IKb, IKK, NEMO, p65, IL-20||Активация||Хроническое воспаление, прогрессирование опухоли|
|TWEAK/Fn14||NOTCH, WNT||Активация||Инициирующие клетки, выбор судьбы клетки|
|IL-6 сигналинг||STAT3, LIN28, IL-6R, IL-6, JAK1||Активация||Полученный прародитель, ответ на адъювантную терапию интерфероном|
|Posttranscriptional modifications (not reported)||Редактирование РНК||AZIN1, ADARs, ODC||Активация||Цирроз, развитие и рецидив, плохой прогноз|
Genome maintenance (10%–28%)
ARID1 and ARID2, MLL, BAP1, EZH2
|Активация||Часто мутирует (SWI/SNF), плохой прогноз|
|Стабильность теломер||TERT||Активация||Ранние генетические изменения, связанные с активацией WNT– β-катенина|
|Stress response/ mitochondria (0%–22%)||Окислительный ответ||NRF2, KEAP1, CUL3||Активация||Окислительное фосфорилирование, прогрессирование опухоли, поздняя стадия|
Классификация и прогноз гепатоцеллюлярной карциномы
The goals of translational gene expression analyses generally include discovery of subsets of tumors (class discovery), which enables diagnostic classification (class comparison), prediction of clinical outcome or response to treatment (class prediction), as well as mechanistic analysis. Verification and validation of primary results are essential for discovery of oncogenic pathways and identification of therapeutic targets. The goal of all staging systems is to separate patients into homogeneous prognostic groups to allow the selection of the most appropriate surveillance and to select a specific therapy for each subtype. Although much work has been devoted to establishment of prognostic models for HCC by using clinical information and pathologic classification, many issues still remain unresolved.74 More than 20 studies on prognostic HCC gene expression profiling, as well as several reviews, have appeared during the last 10 years.6 However, results were quite heterogeneous and, besides disruption in general cancer-related processes (i.e., proliferation, apoptosis, neoangiogenesis, as well as prometastatic and proinflammatory gene sets), the overall similarity was low, limiting a successful implementation into clinical practice. A potential explanation is that interpretation of molecular-profiling studies of HCC poses more challenges than other human tumors, mainly because of the complex pathogenesis of this cancer.75 As already emphasized, HCC arises in diverse settings ranging from infection with HBV or HCV, to chronic metabolic diseases as varied as diabetes, NAFLD, and hemochromatosis. These different disease stages represent complex assortments of genetic and epigenetic aberrations as well as altered molecular pathways.45,76
A recent study was designed to generate a composite prognostic model by evaluating 22 prognostic gene expression signatures generated from tumor as well as cirrhotic tissue in a cohort of 287 patients with early-stage HCC (Barcelona-Clinic Liver Cancer [BCLC] 0/A).77 Overall, most previously reported signatures retained their prognostic capacity in this independent data set. A total of 17 of these 22 signatures adequately subclassify patients according to their prognostic traits. It is noteworthy that none of the signatures reflecting a progenitor cell of origin (EpCAM, hepatoblastoma-C2, CK19-rat, and CK19-human signature) possessed prognostic value. However, these signatures had not been generated for classification of early stages of HCC. Another important finding from this study was that gene expression profiles obtained from paired biopsies from the center and the periphery of the same tumor in 15 tumor specimens showed a high (>80%) transcriptomic concordance. Although these observations provide at least some evidence for stability of gene expression signatures in paired biopsies and suggest a low influence of sampling error, more in-depth analyses are needed to define the intratumoral genetic heterogeneity of HCC that is likely to contribute to the high tumor recurrence and chemoresistance.78,79 In this context, Xue et al.80 investigated the clonal relationship of 43 lesions and 10 matched control samples (blood or nontumorous liver) from 10 patients with HBV-associated HCC by performing whole-exome and low-depth, whole-genome sequencing. Interestingly, the common genetic alterations in lesions from a single patient varied from 8% to 97%, indicating variation in the extent of intratumor heterogeneity. Branched evolution was evident, with somatic mutations, HBV integrations, and CNVs identified in both the trunks and branches of the phylogenetic trees in several lesions. Remarkably, satellite nodules showed a high (approximately 90%) genetic concordance with primary lesions. Together, this study confirmed that the extent of intratumor heterogeneity in HCC varies considerably from patient to patient. Beside interesting mechanistic insights into progression of HCC, these investigations might also have broad implications for the selection of specific targeted treatments. In this case, analyses of multiple lesions as well as sequential tumor biopsies might increase the diagnostic accuracy.
An interesting attempt to classify HCC was recently introduced by combining phenotypic and molecular information in a large series of 343 resected HCC samples.81 Results demonstrate that different histologic subtypes are associated with clinical and molecular features. Alterations in CTNNB1 and TP53 are mutually exclusive and are associated with distinct morphologic features. Tumors harboring CTNNB1 mutations tend to be larger in size, well differentiated, and without inflammatory features, whereas TP53-mutated tumors are poorly differentiated, pleomorphic, and show vascular invasion. Furthermore, the scirrhous HCC subtype showed alterations in TSC1/TSC2 and more stem-like features, whereas the steatotic subtype showed frequent IL- 6/JAK/STAT activation. Interestingly, a novel macrotrabecular-massive subtype showed a poor clinical outcome, high α-fetoprotein (AFP) levels, and FGF19 amplifications. The authors concluded that HCC phenotypes are tightly associated with molecular alterations that may help to translate the current understanding of HCC biology into clinical practice.
Молекулярная основа холангиокарциномы
In comparison with HCC, molecular pathology of intrahepatic cholangiocellular carcinoma (ICC) is less well investigated. Most of the studies on cholangiocarcinogenesis focused on the investigation of few candidate genes.82 In a seminal work on both genomic and genetic features of ICC, gene expression profiles of 104 surgically resected cholangiocarcinoma (CCA) samples were collected and analyzed from patients in Australia, Europe, and the United States.83 The authors discovered two new prognostic subclasses of patients defined by a 238-gene classifier as well as KRAS mutations and increased levels of EGFR and HER2. Also, promising therapeutic strategies in different ICC cell lines that resembled the different prognostic subtypes were validated. This study also addresses the importance of the stromal component of ICC by laser capture microdissection of epithelial and stromal compartments from 23 tumors. Although tumor epithelium was defined by deregulation of the HER2 network and frequent overexpression of EGFR, c-MET, and pRPS6 as well as proliferation, the stroma was predominantly enriched for inflammatory gene sets. In another study, gene expression analyses of 149 ICC from formalin-fixed paraffin-embedded samples were performed.84 Gene set enrichment analysis and functional characteristics of the patients again revealed two broad molecular subclasses, “proliferation” and “inflammation,” defined by differential expression of 1,565 significant genes. The proliferation class was associated with aggressive tumor biology as well as a poor prognosis and characterized by molecular enrichment of oncogenic pathways (e.g., RAS/RAF/MAPK, VEGF, and PDGF). The inflammation class displayed a better prognosis and enrichment of immune-related signaling, including IL-10 and STAT3. Furthermore, a subgroup of ICCs in the proliferation class shared features of several previously published prognostic HCC signatures with a possible progenitor cell origin, supporting the hypothesis that these tumors may be derived from a common origin or precursor cell(s). This hypothesis is supported by recent work by Woo et al.,85 who applied an integrative oncogenomic approach to address the clinical and functional implications of the overlapping phenotype of combined hepatocellular-cholangiocarcinoma (CHC), a histopathologic intermediate form between HCC and cholangiocarcinoma (CC).85 Furthermore, another study confirmed that CHC represents a heterogeneous group of tumors ranging from a more stem-like type characterized by features of poor prognosis to a classical type with common lineage of HCC and ICC components.86 Integration of genomics, transcriptomics, and metabolomics approaches of a large cohort of HCC and ICC from Thailand further showed that ICC and HCC share recurrently mutated genes.87 One of the identified subtypes was characterized by predominant alterations in TP53, ARID1A, and ARID2, mitotic checkpoint anomalies, and shared key drivers PLK1 and ECT2, whereas another subtype could be linked to obesity, T-cell infiltration, and bile acid metabolism. Results of these studies indicate that ICC and HCC, although clinically treated as separate entities, share common molecular traits as well as actionable alterations that could help to guide precision therapy for primary liver cancer. Several recent consortial studies utilized integrative analyses to delineate the landscape of molecular alterations in CCAs.88–90 Although several of the most abundant recurrent genetic alterations could be identified in both HCC and CCA (e.g., TP53, CDKN2A, ARIDs), other molecular alterations (e.g., IDH1/ IDH2, FGFR fusion genes, K/NRAS) were predominantly seen in CCA. Overall, distinct genetic alterations are present in fluke-positive CCAs (i.e., TP53 and ERBB2) and fluke- negative CCAs (e.g., PD-1, IDH1/ IDH2, BAP1, FGFR/PRKA rearrangements).
Заключение и перспектива
Next-generation technologies have provided an extraordinary opportunity for integrative analyses of the cancer (epi-)genome as well as the transcriptome. Predictive and prognostic gene- expression profiling not only has advanced our understanding of cancer biology but also has begun to influence decision making in clinical oncology and ultimately may allow for the development of more effective therapies. The success of these new analytical approaches, comparative and/or integrative functional genomics, suggests that integration of independent data sets will enhance our ability to identify robust predictive markers. Despite the success of these approaches in preclinical translational studies, the clinical application of gene expression profiling is still immature. Although current signatures accurately classify HCCs according to their natural biology, they are unable to predict the response to currently used therapies.75 Furthermore, the notion that HCC with progenitor cell features display a particular aggressive behavior might indicate that tumor heterogeneity and resulting chemoresistance might be generated in molecularly plastic cancer stem cells (CSCs).91 Because CSCs by definition are a rare subpopulation of cells, their molecular profile might be diluted by the bulk of tumor cells, which further hampers therapeutic progress.92 However, based on the exciting results of recent studies and the advent of NGS technologies that offer unprecedented depths and resolution, it seems reasonable to predict that the genomic technologies will play an increasingly important role in clinical oncology. Furthermore, molecular dissection of rare genetic alterations within the tumor cell population as well as the cellular composition of tumors and the corresponding tumor microenvironment (so-called cyber sorting) might facilitate to select patients that are likely to benefit from a specific (immune)oncologic intervention. The immediate focus undoubtedly will be on incorporating these whole-genomic technologies into clinical trials. To achieve this ambitious goal, systematic and standardized collections of tissue/blood specimens from HCC patients (e.g., mandatory and sequential biopsies) for subsequent prospective molecular analyses are urgently needed to ultimately improve the diagnosis and treatment of liver cancer patients.
- El-Serag HB. Epidemiology of viral hepatitis and hepatocellular carcinoma. Gastroenterology 2012;142(6):1264– 1273.e1.
- Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA Cancer J Clin 2015;65(2):87–108.
- El-Serag HB. Hepatocellular carcinoma. New Engl J Med 2011;365(12):1118–1127.
- Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975-2014, featuring survival. J Natl Cancer Inst 2017;109(9):djx030.
- Bruix J, Sherman M; for American Association for the Study of Liver Diseases. Management of hepatocellular carcinoma: an update. Hepatology 2011;53(3):1020–1022.
- Marquardt JU, Galle PR, Teufel A. Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies. J Hepatol 2012;56(1):267–275.
- Bruix J, Boix L, Sala M, et al. Focus on hepatocellular carcinoma. Cancer Cell 2004;5(3):215–219.
- Thorgeirsson SS, Grisham JW. Molecular pathogenesis of human hepatocellular carcinoma. Nature Genet 2002;31(4):339–346.
- Zender L, Villanueva A, Tovar V, et al. Cancer gene discovery in hepatocellular carcinoma. J Hepatol 2010;52(6):921–929.
- Quackenbush J. Microarray analysis and tumor classification. N Engl J Med 2006;354(23):2463–2472.
- Hood L, Heath JR, Phelps ME, et al. Systems biology and new technologies enable predictive and preventative medicine. Science 2004;306(5696):640–643.
- McDermott U, Downing JR, Stratton MR. Genomics and the continuum of cancer care. N Engl J Med 2011;364(4):340–350.
- Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013;339(6127):1546– 1558.
- Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 2010;11(10):685–696.
- Krawczyk M, Mullenbach R, Weber SN, et al. Genome-wide association studies and genetic risk assessment of liver diseases. Nat Rev Gastroenterol Hepatol 2010;7(12):669–681.
- Kumar V, Kato N, Urabe Y, et al. Genome-wide association study identifies a susceptibility locus for HCV- induced hepatocellular carcinoma. Nat Genet 2011;43(5):455–458.
- Trépo E, Romeo S, Zucman-Rossi J, et al. PNPLA3 gene in liver diseases. J Hepatol 2016;65(2):399–412.
- Jiang DK, Sun J, Cao G, et al. Genetic variants in STAT4 and HLA-DQ genes confer risk of hepatitis B virus- related hepatocellular carcinoma. Nat Genet 2013;45(1):72–75.
- Moinzadeh P, Breuhahn K, Stützer H, et al. Chromosome alterations in human hepatocellular carcinomas correlate with aetiology and histological grade—results of an explorative CGH meta-analysis. Br J Cancer 2005;92(5):935– 941.
- Poon TC, Wong N, Lai PB, et al. A tumor progression model for hepatocellular carcinoma: bioinformatic analysis of genomic data. Gastroenterology 2006;131(4):1262–1270.
- Woo HG, Park ES, Lee JS, et al. Identification of potential driver genes in human liver carcinoma by genomewide screening. Cancer Res 2009;69(9):4059–4066.
- Lamb J. The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer 2007;7(1):54–60.
- Roessler S, Long EL, Budhu A, et al. Integrative genomic identification of genes on 8p associated with hepatocellular carcinoma progression and patient survival. Gastroenterology 2012;142(4):957–966.e12.
- Ploeger C, Waldburger N, Fraas A, et al. Chromosome 8p tumor suppressor genes SH2D4A and SORBS3 cooperate to inhibit interleukin-6 signaling in hepatocellular carcinoma. Hepatology 2016;64(3):828–842.
- Feinberg AP. Phenotypic plasticity and the epigenetics of human disease. Nature 2007;447(7143):433–440.
- Feinberg AP, Ohlsson R, Henikoff S. The epigenetic progenitor origin of human cancer. Nat Rev Genet 2006;7(1):21–33.
- Calvisi DF, Ladu S, Gorden A, et al. Mechanistic and prognostic significance of aberrant methylation in the molecular pathogenesis of human hepatocellular carcinoma. J Clin Invest 2007;117(9):2713–2722.
- Hernandez-Vargas H, Lambert MP, Le Calvez-Kelm F, et al. Hepatocellular carcinoma displays distinct DNA methylation signatures with potential as clinical predictors. PloS One 2010;5(3):e9749.
- Lambert MP, Paliwal A, Vaissière T, et al. Aberrant DNA methylation distinguishes hepatocellular carcinoma associated with HBV and HCV infection and alcohol intake. J Hepatol 2011;54(4):705–715.
- Andersen JB, Factor VM, Marquardt JU, et al. An integrated genomic and epigenomic approach predicts therapeutic response to zebularine in human liver cancer. Sci Transl Med 2010;2(54):54ra77.
- Lee JS, Heo J, Libbrecht L, et al. A novel prognostic subtype of human hepatocellular carcinoma derived from hepatic progenitor cells. Nat Med. 2006;12(4):410–416.
- Um TH, Kim H, Oh BK, et al. Aberrant CpG island hypermethylation in dysplastic nodules and early HCC of hepatitis B virus-related human multistep hepatocarcinogenesis. J Hepatol 2011;54(5):939–947.
- Revill K, Wang T, Lachenmayer A, et al. Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology 2013;145(6):1424–1435.e1–25.
- Villanueva A, Portela A, Sayols S, et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology 2015;61(6):1945–1956.
- Esteller M. Epigenetics in cancer. N Engl J Med 2008;358(11):1148–1159.
- Gao SB, Xu B, Ding LH, et al. The functional and mechanistic relatedness of EZH2 and menin in hepatocellular carcinoma. J Hepat 2014;61(4):832–839.
- Li CP, Cai MY, Jiang LJ, et al. CLDN14 is epigenetically silenced by EZH2-mediated H3K27ME3 and is a novel prognostic biomarker in hepatocellular carcinoma. Carcinogenesis 2016;37(6):557–566.
- Lujambio A, Lowe SW. The microcosmos of cancer. Nature 2012;482(7385):347–355.
- Coulouarn C, Factor VM, Andersen JB, et al. Loss of miR-122 expression in liver cancer correlates with suppression of the hepatic phenotype and gain of metastatic properties. Oncogene 2009;28(40):3526–3536.
- Mott JL. MicroRNAs involved in tumor suppressor and oncogene pathways: implications for hepatobiliary neoplasia. Hepatology 2009;50(2):630–637.
- Ladeiro Y, Couchy G, Balabaud C, et al. MicroRNA profiling in hepatocellular tumors is associated with clinical features and oncogene/tumor suppressor gene mutations. Hepatology 2008;47(6):1955–1963.
- Toffanin S, Hoshida Y, Lachenmayer A, et al. MicroRNA-based classification of hepatocellular carcinoma and oncogenic role of miR-517a. Gastroenterology 2011;140(5):1618–1628.e16.
- Ji J, Yu L, Yu Z, et al. Development of a miR-26 companion diagnostic test for adjuvant interferon-alpha therapy in hepatocellular carcinoma. Int J Biol Sci 2013;9(3):303–312.
- Marquardt JU, Andersen JB. Liver cancer oncogenomics: opportunities and dilemmas for clinical applications. Hepat Oncol 2015;2(1):79–93.
- Arzumanyan A, Reis HM, Feitelson MA. Pathogenic mechanisms in HBV- and HCV-associated hepatocellular carcinoma. Nat Rev Cancer 2013;13(2):123–135.
- Kaposi-Novak P, Libbrecht L, Woo HG, et al. Central role of c-Myc during malignant conversion in human hepatocarcinogenesis. Cancer Res 2009;69(7):2775–2782.
- Nault JC, Calderaro J, Di Tommaso L, et al. Telomerase reverse transcriptase promoter mutation is an early somatic genetic alteration in the transformation of premalignant nodules in hepatocellular carcinoma on cirrhosis. Hepatology 2014;60(6):1983–1992.
- Marquardt JU, Seo D, Andersen JB, et al. Sequential transcriptome analysis of human liver cancer indicates late stage acquisition of malignant traits. J Hepatol 2014;60(2):346–353.
- Marquardt JU, Andersen JB, Thorgeirsson SS. Functional and genetic deconstruction of the cellular origin in liver cancer. Nat Rev Cancer 2015;15(11):653–667.
- Schulze K, Imbeaud S, Letouze E, et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 2015;47(5):505–511.
- Fujimoto A, Furuta M, Totoki Y, et al. Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer. Nat Genet 2016;48(5):500–509.
- Fujimoto A, Totoki Y, Abe T, et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet 2012;44(7):760–764.
- Guichard C, Amaddeo G, Imbeaud S, et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat Genet 2012;44(6):694–698.
- Li M, Zhao H, Zhang X, et al. Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma. Nat Genet 2011;43(9):828–829.
- Totoki Y, Tatsuno K, Yamamoto S, et al. High-resolution characterization of a hepatocellular carcinoma genome. Nat Genet 2011;43(5):464–469.
- Toffanin S, Cornella H, Harrington A, et al. Next-generation sequencing: path for driver discovery in hepatocellular carcinoma. Gastroenterology 2012;143(5):1391–1393.
- Cleary SP, Jeck WR, Zhao X, et al. Identification of driver genes in hepatocellular carcinoma by exome sequencing. Hepatology 2013;58(5):1693–1702.
- Kan Z, Zheng H, Liu X, et al. Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma. Genome Res 2013;23(9):1422–1433.
- Nault JC, Mallet M, Pilati C, et al. High frequency of telomerase reverse-transcriptase promoter somatic mutations in hepatocellular carcinoma and preneoplastic lesions. Nat Commun 2013;4:2218.
- Wheeler DA, Roberts LR; for The Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 2017;169(7):1327–1341.e23.
- Hernandez-Gea V, Toffanin S, Friedman SL, et al. Role of the microenvironment in the pathogenesis and treatment of hepatocellular carcinoma. Gastroenterology 2013;144(3):512–527.
- Karin M. Nuclear factor-kappaB in cancer development and progression. Nature 2006;441(7092):431–436.
- Luedde T, Schwabe RF. NF-kappaB in the liver—linking injury, fibrosis and hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2011;8(2):108–118.
- He G, Dhar D, Nakagawa H, et al. Identification of liver cancer progenitors whose malignant progression depends on autocrine IL-6 signaling. Cell 2013;155(2):384–396.
- Naugler WE, Sakurai T, Kim S, et al. Gender disparity in liver cancer due to sex differences in MyD88-dependent IL-6 production. Science 2007;317(5834):121–124.
- Park EJ, Lee JH, Yu GY, et al. Dietary and genetic obesity promote liver inflammation and tumorigenesis by enhancing IL-6 and TNF expression. Cell 2010;140(2):197–208.
- Guo Y, Xu F, Lu T, et al. Interleukin-6 signaling pathway in targeted therapy for cancer. Cancer Treat Rev 2012;38(7):904–910.
- Makarova-Rusher OV, Medina-Echeverz J, Duffy AG, et al. The yin and yang of evasion and immune activation in HCC. J Hepatol 2015;62(6):1420–1429.
- Wan S, Zhao E, Kryczek I, et al. Tumor-associated macrophages produce interleukin 6 and signal via STAT3 to promote expansion of human hepatocellular carcinoma stem cells. Gastroenterology 2014;147(6):1393–1404.
- Hoshida Y, Villanueva A, Kobayashi M, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008;359(19):1995–2004.
- Feng GS. Conflicting roles of molecules in hepatocarcinogenesis: paradigm or paradox. Cancer Cell 2012;21(2):150–154.
- Li SC, Tachiki LM, Kabeer MH, et al. Cancer genomic research at the crossroads: realizing the changing genetic landscape as intratumoral spatial and temporal heterogeneity becomes a confounding factor. Cancer Cell Int 2014;14(1):115.
- Greten TF, Wang XW, Korangy F. Current concepts of immune based treatments for patients with HCC: from basic science to novel treatment approaches. Gut 2015;64(5):842–848.
- Thorgeirsson SS. Genomic decoding of hepatocellular carcinoma. Gastroenterology 2006;131(4):1344–1346.
- Schirmacher P, Calvisi DF. Molecular diagnostic algorithms in hepatocellular carcinoma: dead-end street or light at the end of the tunnel? Gastroenterology 2013;145(1):49–53.
- Farazi PA, DePinho RA. Hepatocellular carcinoma pathogenesis: from genes to environment. Nat Rev Cancer 2006;6(9):674–687.
- Villanueva A, Hoshida Y, Battiston C, et al. Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology 2011;140(5):1501–1512.e2.
- Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366(10):883–892.
- Teufel A, Marquardt JU, Galle PR. Novel insights in the genetics of HCC recurrence and advances in transcriptomic data integration. J Hepatol 2012;56(1):279–281.
- Xue R, Li R, Guo H, et al. Variable intra-tumor genomic heterogeneity of multiple lesions in patients with hepatocellular carcinoma. Gastroenterology 2016;150(4):998–1008.
- Calderaro J, Couchy G, Imbeaud S, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol 2017;67(4):727–738.
- Andersen JB, Thorgeirsson SS. Genetic profiling of intrahepatic cholangiocarcinoma. Curr Opin Gastroenterol 2012;28(3):266–272.
- Andersen JB, Spee B, Blechacz BR, et al. Genomic and genetic characterization of cholangiocarcinoma identifies therapeutic targets for tyrosine kinase inhibitors. Gastroenterology 2012;142(4):1021–1031.e15.
- Sia D, Hoshida Y, Villanueva A, et al. Integrative molecular analysis of intrahepatic cholangiocarcinoma reveals 2 classes that have different outcomes. Gastroenterology 2013;144(4):829–840.
- Woo HG, Lee JH, Yoon JH, et al. Identification of a cholangiocarcinoma-like gene expression trait in hepatocellular carcinoma. Cancer Res 2010;70(8):3034–3041.
- Moeini A, Sia D, Zhang Z, et al. Mixed hepatocellular cholangiocarcinoma tumors: cholangiolocellular carcinoma is a distinct molecular entity. J Hepatol 2017;66(5):952–961.
- Chaisaingmongkol J, Budhu A, Dang H, et al. Common molecular subtypes among Asian hepatocellular carcinoma and cholangiocarcinoma. Cancer Cell 2017;32(1):57–70.e3.
- Farshidfar F, Zheng S, Gingras MC, et al. Integrative genomic analysis of cholangiocarcinoma identifies distinct IDH-mutant molecular profiles. Cell Rep 2017;18(11):2780–2794.
- Jusakul A, Cutcutache I, Yong CH, et al. Whole-genome and epigenomic landscapes of etiologically distinct subtypes of cholangiocarcinoma. Cancer Discov 2017;7(10):1116–1135.
- Nakamura H, Arai Y, Totoki Y, et al. Genomic spectra of biliary tract cancer. Nature Genet 2015;47(9):1003– 1010.
- Marquardt JU, Thorgeirsson SS. Stem cells in hepatocarcinogenesis: evidence from genomic data. Semin Liver Dis 2010;30(1):26–34.
- Marquardt JU, Factor VM, Thorgeirsson SS. Epigenetic regulation of cancer stem cells in liver cancer: current concepts and clinical implications. J Hepatol 2010;53(3):568–577.