BACKGROUND & AIMS Cholangiocarcinoma, the second most common liver cancer, can be classified as intra-hepatic cholangiocarcinoma (ICC) or extrahepatic cholangiocarcinoma. We performed an integrative genomic analysis of ICC samples from a large series of patients. METHODS We performed a gene expression profile, high-density single-nucleotide polymorphism array, and mutation analyses using formalin-fixed ICC samples from 149 patients. Associations with clinicopathologic traits and patient outcomes were examined for 119 cases. Class discovery was based on a non-negative matrix factorization algorithm and significant copy number variations were identified by GISTIC analysis. Gene set enrichment analysis was used to identify signaling pathways activated in specific molecular classes of tumors, and to analyze their genomic overlap with hepatocellular carcinoma (HCC). RESULTS We identified 2 main biological classes of ICC. The inflammation class (38% of ICCs) is characterized by activation of inflammatory signaling pathways, overexpression of cytokines, and STAT3 activation. The proliferation class (62%) is characterized by activation of oncogenic signaling pathways (including RAS, mitogen-activated protein kinase, and MET), DNA amplifications at 11q13.2, deletions at 14q22.1, mutations in KRAS and BRAF, and gene expression signatures previously associated with poor outcomes for patients with HCC. Copy number variation– based clustering was able to refine these molecular groups further. We identified high-level amplifications in 5 regions, including 1p13 (9%) and 11q13.2 (4%), and several focal deletions, such as 9p21.3 (18%) and 14q22.1 (12% in coding regions for the SAV1 tumor suppressor). In a complementary approach, we identified a gene expression signature that was associated with reduced survival times of patients with ICC; this signature was enriched in the proliferation class (P < .001). CONCLUSIONS We used an integrative genomic analysis to identify 2 classes of ICC. The proliferation class has specific copy number alterations, many features of the poor-prognosis signatures for HCC, and is associated with worse outcome. Different classes of ICC, based on molecular features, therefore might require different treatment approaches.

Epigenetic deregulation has emerged as a driver in human malignancies. There is no clear understanding of the epigenetic alterations in hepatocellular carcinoma (HCC) and of the potential role of DNA methylation markers as prognostic biomarkers. Analysis of tumor tissue from 304 patients with HCC treated with surgical resection allowed us to generate a methylation‐based prognostic signature using a training‐validation scheme. Methylome profiling was done with the Illumina HumanMethylation450 array (Illumina, Inc., San Diego, CA), which covers 96% of known cytosine‐phosphate‐guanine (CpG) islands and 485,000 CpG, and transcriptome profiling was performed with Affymetrix Human Genome U219 Plate (Affymetrix, Inc., Santa Clara, CA) and miRNA Chip 2.0. Random survival forests enabled us to generate a methylation signature based on 36 methylation probes. We computed a risk score of mortality for each individual that accurately discriminated patient survival both in the training (221 patients; 47% hepatitis C–related HCC) and validation sets (n = 83; 47% alcohol‐related HCC). This signature correlated with known predictors of poor outcome and retained independent prognostic capacity of survival along with multinodularity and platelet count. The subset of patients identified by this signature was enriched in the molecular subclass of proliferation with progenitor cell features. The study confirmed a high prevalence of genes known to be 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) and describes potential candidate epidrivers (e.g., septin 9 and ephrin B2). Conclusions: A validated signature of 36 DNA methylation markers accurately predicts poor survival in patients with HCC. Patients with this methylation profile harbor messenger RNA–based signatures indicating tumors with progenitor cell features. (Hepatology 2015;61:1945–1956)

Background & Aims In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early-stage (BCLC 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n=287) and adjacent non-tumor, cirrhotic tissue (n=226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from non-tumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature “G3-proliferation” (hazard ratio [HR]=1.75, P=0.003) and an adjacent “poor-survival” signature (HR=1.74, P=0.004) were independent predictors of HCC recurrence, along with satellites (HR=1.66, P=0.04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.

Mutations in Isocitrate dehydrogenase 1 (IDH1) and IDH2 are among the most common genetic alterations in intrahepatic cholangiocarcinoma (IHCC), a deadly liver cancer1–5. Mutant IDH proteins in IHCC and other malignancies acquire an abnormal enzymatic activity allowing them to convert alpha-ketoglutarate (αKG) to 2-hydroxyglutarate (2HG), which inhibits the activity of multiple αKG-dependent dioxygenases, and results in alterations in cell differentiation, survival, and extracellular matrix maturation6–10. However, the molecular pathways by which IDH mutations lead to tumour formation remain unclear. Here we show that mutant IDH blocks liver progenitor cells from undergoing hepatocyte differentiation through the production of 2HG and suppression of HNF4α, a master regulator of hepatocyte identity and quiescence. Correspondingly, genetically engineered mouse models (GEMMs) expressing mutant IDH in the adult liver show aberrant response to hepatic injury, characterized by HNF4α silencing, impaired hepatocyte differentiation and markedly elevated levels of cell proliferation. Moreover, mutant IDH and activated Kras, genetic alterations that co-exist in a subset of human IHCCs4,5, cooperate to drive the expansion of liver progenitor cells, development of premalignant biliary lesions, and progression to metastatic IHCC. These studies provide a functional link between IDH mutations, hepatic cell fate, and IHCC pathogenesis, and present a novel GEMM of IDH-driven malignancy.

Intrahepatic cholangiocarcinoma (iCCA) is a fatal bile duct cancer with dismal prognosis and limited therapeutic options. By performing RNA-and exome-sequencing analyses, we report a novel fusion event, FGFR2-PPHLN1 (16%), and damaging mutations in the ARAF oncogene (11%). Here we demonstrate that the chromosomal translocation t(10;12)(q26;q12) leading to FGFR2-PPHLN1 fusion possesses transforming and oncogenic activity, which is successfully inhibited by a selective FGFR2 inhibitor in vitro. Among the ARAF mutations, N217I and G322S lead to activation of the pathway and N217I shows oncogenic potential in vitro. Screening of a cohort of 107 iCCA patients reveals that FGFR2 fusions represent the most recurrent targetable alteration (45%, 17/107), while they are rarely present in other primary liver tumours (0/100 of hepatocellular carcinoma (HCC); 1/21 of mixed iCCA-HCC). Taken together, around 70% of iCCA patients harbour at least one actionable molecular alteration (FGFR2 fusions, IDH1/2, ARAF, KRAS, BRAF and FGF19) that is amenable for therapeutic targeting.

Objective Sorafenib is effective in hepatocellular carcinoma (HCC), but patients ultimately present disease progression. Molecular mechanisms underlying acquired resistance are still unknown. Herein, we characterise the role of tumour-initiating cells (T-ICs) and signalling pathways involved in sorafenib resistance. Design HCC xenograft mice treated with sorafenib (n=22) were explored for responsiveness (n=5) and acquired resistance (n=17). Mechanism of acquired resistance were assessed by: (1) role of T-ICs by in vitro sphere formation and in vivo tumourigenesis assays using NOD/SCID mice, (2) activation of alternative signalling pathways and (3) efficacy of anti-FGF and anti-IGF drugs in experimental models. Gene expression (microarray, quantitative real-time PCR (qRT-PCR)) and protein analyses (immunohistochemistry, western blot) were conducted. A novel gene signature of sorafenib resistance was generated and tested in two independent cohorts. Results Sorafenib-acquired resistant tumours showed significant enrichment of T-ICs (164 cells needed to create a tumour) versus sorafenib-sensitive tumours (13 400 cells) and non-treated tumours (1292 cells), p<0.001. Tumours with sorafenib-acquired resistance were enriched with insulin-like growth factor (IGF) and fibroblast growth factor (FGF) signalling cascades (false discovery rate (FDR)<0.05). In vitro, cells derived from sorafenib-acquired resistant tumours and two sorafenibresistant HCC cell lines were responsive to IGF or FGF inhibition. In vivo, FGF blockade delayed tumour growth and improved survival in sorafenib-resistant tumours. A sorafenib-resistance 175 gene signature was characterised by enrichment of progenitor cell features, aggressive tumorous traits and predicted poor survival in two cohorts (n=442 patients with HCC). Conclusions Acquired resistance to sorafenib is driven by T-ICs with enrichment of progenitor markers and activation of IGF and FGF signalling. Inhibition of these pathways would benefit a subset of patients after sorafenib progression.

Signaling pathways have become a major source of targets for novel therapies in hepatocellular carcinoma (HCC). Survival benefits achieved with sorafenib, a multikinase inhibitor, are unprecedented and underscore the importance of improving our understanding of how signaling networks interact in transformed cells. Numerous signaling modules are de-regulated in HCC, including some related to growth factor signaling (e.g., IGF, EGF, PDGF, FGF, HGF), cell differentiation (WNT, Hedgehog, Notch), and angiogenesis (VEGF). Intracellular mediators such as RAS and AKT/MTOR may also play a role in HCC development and progression. Different molecular mechanisms have been shown to induce aberrant pathway activation. These include point mutations, chromosomal aberrations, and epigenetically driven down-regulation. The use of novel molecular technologies such as next-generation sequencing in HCC research has enabled the identification of novel pathways previously underexplored in the HCC field, such as chromatin remodeling and autophagy. Considering recent failures of molecular therapies in advanced clinical trials (e.g., sunitinib, brivanib), survey of these and other new pathways may provide alternative therapeutic targets.

Background & Aims Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can only be treated with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors. Methods Using 78 clinically annotated FLC samples, we performed whole-transcriptome (n=58), single-nucleotide polymorphism array (n=41), and next-generation sequencing (n=48) analyses; we also assessed the prevalence of the DNAJB1–PRKACA fusion transcript associated with this cancer (n=73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort. Results Unsupervised gene expression clustering revealed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mTOR signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine production; and the unannotated class (23% of samples) had a gene expression signature not previously associated with liver tumors. Expression of genes that regulate neuroendocrine function, as well has histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples) and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1–PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC. Conclusions In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1–PRKACA fusion transcript. Using this information, we identified a gene signature that is associated with patient survival time.