Transcriptomic characterization of fibrolamellar hepatocellular carcinoma

Abstract

Fibrolamellar hepatocellular carcinoma (FLHCC) tumors all carry a deletion of ∼ 400 kb in chromosome 19, resulting in a fusion of the genes for the heat shock protein, DNAJ (Hsp40) homolog, subfamily B, member 1, DNAJB1, and the catalytic subunit of protein kinase A, PRKACA. The resulting chimeric transcript produces a fusion protein that retains kinase activity. No other recurrent genomic alterations have been identified. Here we characterize the molecular pathogenesis of FLHCC with transcriptome sequencing (RNA sequencing). Differential expression (tumor vs. adjacent normal tissue) was detected for more than 3,500 genes (log2 fold change ≥ 1, false discovery rate ≤ 0.01), many of which were distinct from those found in hepatocellular carcinoma. Expression of several known oncogenes, such as ErbB2 and Aurora Kinase A, was increased in tumor samples. These and other dysregulated genes may serve as potential targets for therapeutic intervention.

Keywords: fusion gene; genomics; liver cancer; pediatric cancer; protein kinase.

Fig. 1.

Tumor gene expression patterns in FLHCC are distinct from adjacent normal tissue. (A) Principal component analysis of variance stabilized transformed RNA-seq read counts for whole transcriptomes. Ellipses indicate 95% confidence interval of group membership. Axis percentages indicate variance contribution. (B) Heat map depicting hierarchical clustering of sample-to-sample distance. Variance-stabilized transformed RNA-seq read counts for whole transcriptomes were used to calculate sample-to-sample Euclidean distances (color scale) for hierarchical clustering, complete method. R in patient labels indicates recurrence tumor sample.

Fig. 2.

Gene set analysis. Significant enrichment of canonical pathway gene sets (MSigDB version 4.0, c2.cp collection) was assessed by GSVA. (A) GSVA enrichment scores for the top 30 gene sets significantly enriched (ranked by adjusted P value) in normal liver tissue vs. FLHCC. Row side bar colors indicate liver functional category (manually classified): Detoxification (red), metabolism (orange), synthesis (light blue), bile processes (green), not applicable (white). Gene sets labeled by source, Reactome (R), KEGG (K), Biocarta (B). (B) GSVA enrichment scores for select cell cycle/proliferation pathway gene sets significantly enriched (FDR 0.05) in FLHCC vs. normal liver tissue.

Fig. 3.

Gene expression differences in FLHCC vs. normal liver tissue for select gene collections of functional interest. For genes in each collection, log₂ fold-change (FLHCC vs. adjacent normal liver) expression is plotted with heat-map color scale. Genes significantly differentially expressed (FDR 0.01, log₂ fold change >1) are indicated in bold. Genes that did not pass read count filters (DESeq2) were removed from collection for plotting purposes. (A) Cytochrome P450 (Reactome, GSVA adjusted P value = 6.86E-08). (B) Glycolysis and gluconeogenesis (KEGG, GSVA adjusted P value = 0.014). (C) Estrogen metabolic process (GO:0008210, GSVA enrichment not assessed). (D) Collagen (genes selected by “collagen” gene description, Ensembl BIOMART annotation, differentially expressed FLHCC vs. normal liver, FDR 0.05). (E) GRB2 events in ERBB2 signaling (Reactome, GSVA adjusted P value = 0.0004).

Fig. 4.

Distance clustering of FLHCC and HCC. Protein-coding gene expression log₂ fold change was calculated for each tumor–normal pair (10 FLHCC, 42 HCC after filtering by read depth ratios). Hierarchical clustering (complete method) was performed on spearman rank correlation matrix for all protein coding genes passing DESeq2 expression filter (ranked by moderated t statistic ranking). Side bar colors indicate tumor type on diagnosis: FLHCC (purple) and HCC (orange).

Fig. 5.

Expression (variance stabilized read counts) of select genes implicated in neuroendrocrine malignancies. Heat map displays variance-stabilized transformed RNA-seq read count values. Genes significantly differentially expressed (FDR 0.01, log₂ fold change >1) are indicated in bold. Asterisk indicates CHGA did not have sufficient reads to clear expression filters (DESeq2) but is included here for comprehensiveness.

Fig. 6.

(A) The transcriptional changes as assayed by RNA-seq were correlated with transcriptional changes as assayed by qPCR. Column plot of individual transcripts showing the expression as assayed by RNA-seq (n = 10) and qPCR (n = 5–8; error bars are SD). (B) Transcriptional changes as assayed by RNA-seq (n = 10) were correlated with changes in the proteome as assayed by mass spectrometry (n = 4). (C) Protein expression in FLHCC (T) and adjacent normal (N) liver was assayed by Western blot for transcripts that were increased in FLHCC as assayed by RNA-seq.