Unique genomic profile of fibrolamellar hepatocellular carcinoma

Abstract

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 be treated only with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors.

Methods: By 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 a 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 showed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mammalian target of rapamycin signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine enriched production; and the unannotated class (23% of samples) had a gene expression signature that was not associated previously with liver tumors. Expression of genes that regulate neuroendocrine function, as well as 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. By using this information, we identified a gene signature that is associated with patient survival time.

Keywords: Genomic Profiling; Molecular Classification; Outcome; Targeted Therapies.

Figure 1. Molecular classes of FLC and their distinct genomic profile

(A) NMF-based algorithm identified 3 robust classes: Proliferation (green), Inflammation (purple) and Unannotated (turquoise). Heat-map shows unsupervised clustering of 35 FLCs based on whole-genome expression showing the top differentially expressed genes for each class. Bottom part of the heat-map shows the overlap of the results from the NTP ICC-Proliferation signature, the IHC results of p-RPS6 and EGFR (panels C and E), and the expression values of IL-10 and 18. (B, D) GSEA plots demonstrated the enrichment in HCC Boyaoult-G2 (B) and ICC-Inflammation (D) gene signatures in FLC-Proliferation and FLC-Inflammation classes, respectively. (C, E) Immunohistochemical pattern of p-RPS6 (C) and EGFR (E) staining in FLC (upper panels) and non-tumoral tissues (lower panels).

Figure 2. Immunohistochemical characterization of FLC

(A) FLC pathological characterization by hematoxylin and eosin staining (upper panels), and HepPar1, K7, K19 and EpCAM immunostaining (median and lower panels). (B) Distribution of the IHC results within the molecular classes.

Figure 3. Significant broad and focal chromosomal alterations in FLC

GISTIC algorithm identified significant CNVs in 32 FLC samples. Chromosomes are displayed in descending order along the vertical axis. GISTIC q-values (x-axis) for amplifications (left, red) and deletions (right, blue) corresponding to the FDR q-value obtained from GISTIC are plotted across the genome (y-axis). Vertical green line stands for the significance threshold of q < 0.25. A–B) represent graphically the significance of broad arm level CNVs, and C–D) the significance of focal CNVs.

Figure 4. Gene expression-based prognostic signature and its validation

(A, B & C) Kaplan-Meier plots estimating overall survival in training (n=29, A, left panel) and validation-French (n=22, C) cohort, and overall recurrence in those patients from the training set for whom data on recurrence was available (n=26, B). Heat-map shows expression values of the 8 genes that constitute the prognostic signature (A, right panel). Patients in the Poor-prognosis class (red) showed shorter survival and earlier recurrence.

Figure 5. Integrative genomic analysis

FLC gene expression classification and its overlap with the previously published consensus ICC classification and the FLC results of the copy number alterations, immunohistochemical stainings and gene expression of interleukines and neuroendocrine markers, together with the presence of the fusion transcript and the enrichment results of the prognostic signature. The integrative analysis revealed an indolent 20 profile together with a paucity of progenitor markers for the Unannotated class, a highly enrichment of progenitor cells traits for the Proliferation class, and an enrichment of focal deletions and interleukines at the Inflammation class. Moreover, these analysis demonstrate the highly prevalence of the DNAJB1-PRKACA fusion transcript.