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. 2015 May;47(5):505-511.
doi: 10.1038/ng.3252. Epub 2015 Mar 30.

Exome Sequencing of Hepatocellular Carcinomas Identifies New Mutational Signatures and Potential Therapeutic Targets

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Free PMC article

Exome Sequencing of Hepatocellular Carcinomas Identifies New Mutational Signatures and Potential Therapeutic Targets

Kornelius Schulze et al. Nat Genet. .
Free PMC article

Abstract

Genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. Analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereas FGF3, FGF4, FGF19 or CCND1 amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)-approved drugs. In conclusion, we identified risk factor-specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.

Figures

Figure 1
Figure 1
Consensus signatures of mutational processes in hepatocellular carcinoma. (a) Patterns of the signatures of the mutational processes operative in 243 liver exomes. Signatures 23 and 24 were identified using de novo WTSI mutational signatures framework, while the presence of Signatures 1A, 1B, 4, 5, 6, and 16 were identified via re-introduction of consensus mutational signatures previously identified in liver cancer by a pan-cancer analysis. Each signature is displayed according to the 96 substitution classification defined by the substitution class and sequence context immediately 3′ and 5′ to the mutated base. Signatures 1A, 1B, 4, 5, 6 and 16 match signatures previously described in a pan-cancer study and the plotted patterns correspond to the updated consensus signatures. Signatures 23 and 24 are new.(b) Unsupervised hierarchical clustering of 243 liver tumors based on the intensity of signatures operative in each sample. Tumors were classified into 6 mutational signature (MSig) groups and 4 singletons. The number of mutations attributed to each signature in each tumor is represented by color bars below the dendogram. *Tumors harboring more than 500 mutations. (c) Principal component analysis of mutational signature intensities in 243 liver tumors. Tumor samples are plotted in three dimensions using their projections onto the first three principal components (PC). MSig group membership is represented by a color code and labels. (d) Clinical and molecular features associated with each MSig group. Associations were assessed using Chi-square tests for categorical variables and ANOVA for quantitative features.
Figure 2
Figure 2
Integration of mutations, focal amplifications and homozygous deletions identifies putative driver genes in hepatocellular carcinoma. (a) The 161 putative driver genes identified by integrating mutations and focal CNAs are represented, with the log-transformed mutation significance on the x-axis and the net frequency of gains and deletions on the y-axis. The size and color of each sample represent the alteration frequency and MutSig q-values, respectively. Significantly mutated genes (q < 0.05) are indicated. (b) Frequency of CNAs along the genome. The top axis indicates the frequency of low-amplitude changes (gains and losses); the bottom axis indicates the frequency of high-amplitude changes (focal amplifications and homozygous deletions). Target genes of recurrent amplifications and homozygous deletions are indicated. (c) Bar plot indicating the number and type of events for the most frequently altered genes (≥4%).
Figure 3
Figure 3
The landscape of altered genes and pathways in HCC. Eleven pathways altered in ≥5% of HCC. Genes belonging to each pathway are represented with their alteration frequencies (green font: inactivation; red font: activation; white font: unknown) and activating or inhibiting interactions between pathways are highlighted. Major genes lacking alterations are highlighted in white. Focal amplifications (FA) and significant associations with etiologies are indicated. Potential druggable genes are assigned (yellow: FDA-approved drugs; lighting: drugs screened in phase I-III clinical trials).
Figure 4
Figure 4
Major clusters of associated alterations. Three major groups of associated alterations: CTNNB1-cluster, AXIN1-cluster, and TP53-cluster. Significant associations and exclusions between genes are represented, with line widths proportional to significance, as indicated in the legend. Surrounding colors of each gene indicate its participation to a particular pathway.
Figure 5
Figure 5
Sensitivity of liver cancer cell lines to HSP90 inhibitors is associated with NQO1 expression. (a) Sensitivity of 29 liver cancer cell lines to 17-AAG and 17-DMAG was assessed by a cell viability assay. The heatmap represents the GI50 of the two drugs in each cell line, and mRNA expression levels of NQO1 are represented atop the heatmap. Three cell lines harboring KEAP1 mutations are indicated. (b) Correlation between NQO1 protein expression and sensitivity to 17-AAG. A color code indicates cell lines with KEAP1 mutation, and the MHCC97H cell line that harbors a KEAP1 mutation but is homozygous for the NQO1*2 variant causing NQO1 enzymatic deficiency. (c) Cell viability curves for cell lines JHH-4 (KEAP1-wild-type), JHH-5 (KEAP1-mutated) and MHCC97H (KEAP1-mutated and NQO1*2 homozygous) in the presence of the HSP90 inhibitor 17-AAG. Results are shown as mean + sem (standard error of the mean). Each experiment was repeated two to three times for each cell line.
Figure 6
Figure 6
Molecular features of HCC progression in cirrhotic and non-cirrhotic liver. (a) Box-and-whisker plots show the distribution of mutation numbers (top) and chromosome instability scores (bottom) relative to tumor stage in cirrhotic (left) and non-cirrhotic liver (right). FAA, fraction of aberrant arms; DMN, dysplastic macronodule; HCA, hepatocellular adenoma; HCC, hepatocellular carcinoma. Middle bar, median; box, interquartile range; bars extend to 1.5 times the interquartile range. *Most HCA and HCC in HCA samples are from a previous study. (b) Genetic alterations with significantly different frequencies across tumor stages in cirrhotic (top) and non-cirrhotic liver (bottom). **, P < 0.01, ***, P < 0.001, Chi-square test for trends in proportion. (c) Kaplan-Meier curves for overall survival in the presence or absence of CDKN2A alteration (left) or FGF3, FGF4, FGF19/CCND1 amplification (right), two features found significantly associated with survival in a multivariate Cox model. P-values were obtained using Wald tests.

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