Integrative molecular analysis of intrahepatic cholangiocarcinoma reveals 2 classes that have different outcomes

Abstract

Background & aims: Cholangiocarcinoma, the second most common liver cancer, can be classified as intrahepatic 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 Genomic Identification of Significant Targets in Cancer (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, activation of oncogenic pathways, and is associated with worse outcome. Different classes of ICC, based on molecular features, therefore might require different treatment approaches.

Conflict of interest statement

Conflicts of interest

The authors disclose no conflicts.

Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

Molecular classes of ICC with distinct clinical outcome. Non-negative matrix factorization–based algorithm identified 2 robust classes: proliferation (orange) and inflammation (green) classes. (A) The heatmap shows unsupervised clustering of 149 ICCs based on the whole-genome expression. High and low expression levels are represented in red and blue, respectively. The top 100 differentially expressed genes are presented in the heat-map. (B) Kaplan–Meier estimates of overall survival (n = 119); (C) Kaplan–Meier plot of overall recurrence (n = 113). Patients in the proliferation class (red) showed shorter survival and earlier recurrence.

Figure 2

Dysregulated gene networks and validation of HLA at 11q13.2 in ICC molecular classes. Ingenuity Pathway Analysis results for (A) proliferation and (B) inflammation classes are presented. Network analysis of deregulated genes in the proliferation class shows the presence of 3 central nodes (ie, PI3K, RALA, and RAS homolog). In the inflammation class one top-scored network related to inflammation and including several cytokines (eg, IL-10, IL-19) was identified. A node represents a gene or gene product, and an edge indicates the relationship between nodes. Solid lines symbolize direct interaction and dotted lines stand for indirect interaction. (C) Immunohistochemical STAT3-Tyr705 phosphorylation staining in inflammation (right panel) and proliferation (left panel) class representative samples. Images were captured with 20× (upper panel) and 40× (lower panel) objectives. Nuclear positive staining was enriched in tumors of the inflammation class (77% positive patients in the inflammation vs 58% in the proliferation subclass). (D) Annotated genes mapped in the minimal overlapping region at 11q13.2 and the genomic location of the fluorescence in situ hybridization probe (green bar) used for confirmation of 11q13.2 HLA in 5 patients from the proliferation class. (E) HLA at 11q13.2 was validated by FISH. Red: BAC probe centered on the region of interest; green: BAC probe CEP-11 for the centromere of chromosome 11. Representative images are displayed: 1 patient without HLA (left panel) or with HLA (right panel). (F) ORAOV1 gene expression is increased in the 5 patients harboring 11q13.2 HLA. Each dot represents the corresponding copy number and expression level for a single tumor. Horizontal axis, log2 copy number for SNP probes in the minimal region at 11q13.2. Vertical axis, normalized gene expression level for each tumor.

Figure 3

CNV-based identification of further subgroups within the ICC classes. Non-negative matrix factorization clustering of copy number data identified the subgroups P1–3 and I1–3 within the proliferation and inflammation classes, respectively. Patients with copy number data are represented (n =127). Survival and recurrence signatures were enriched significantly in the proliferation class (patients in red, P < .001). Mutated patients are shown by black bars; KRAS mutations were enriched in the P1 subgroup (P = .03). Heatmap represents IL-6, EGFR, IL-17A, IL-3, IL-10, and I-L4 expression, highest in red and lowest in green; IL-6 and EGFR were found overexpressed in I3 and P3 (P <.001) whereas IL-17A and IL-3 were found overexpressed in I1 (P = .003) and I2 (P < .001) subgroups, respectively. CNV affecting broad arms are shown, gains are in red and losses are in blue. Only significant changes are represented. The presence of focal deletion of 14q22.1 and positive STAT3 staining is shown by black bars.

Figure 4

Summary of characteristics of ICC classes. Specific molecular and clinical characteristics differ between ICC classes. Molecular characteristics such as signatures of poor prognosis (ie, cluster A, CC-like, G3, S1, S2, and stem-cell like ICC), oncogenic pathways (ie, IGF1R, MET, EGFR), gene expression (ie, EGFR, ILs), copy number variations, and oncogenes mutations (KRAS and EGFR) are differentially enriched in the proliferation and inflammation classes. Clinical characteristics such as moderate/poorly differentiated tumors and intraneural invasion are more frequent in the proliferation class. Differences in survival and recurrence were observed.