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Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets

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.

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Figure 1: Consensus signatures of mutational processes in HCC.
Figure 2: Integration of mutations, focal amplifications and homozygous deletions identifies putative driver genes in HCC.
Figure 3: The landscape of altered genes and pathways in HCC.
Figure 4: Major clusters of associated alterations.
Figure 5: Sensitivity of liver cancer cell lines to HSP90 inhibitors is associated with NQO1 expression.
Figure 6: Molecular features of HCC progression in cirrhotic and non-cirrhotic liver.

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Acknowledgements

We warmly thank A. Boulais, C. Guichard, I. Ben Maad and C. Pilati for helpful participation in this work. We thank L. de Koning, C. Baldeyron, A. Barbet and C. Lecerf from the Institut Curie for the reverse-phase protein array experiments. We also thank J. Saric, C. Laurent, L. Chiche, B. Le Bail and C. Castain (Centre Hospitalier Universitaire Bordeaux) and D. Cherqui and J. Tran Van Nhieu (Centre Hospitalier Universitaire Henri Mondor, Créteil) for contributing to the tissue collection. This work was supported by Institut National du Cancer (INCa) with the ICGC project, the PAIR-CHC project NoFLIC (funded by INCa and Association pour la Recherche contre le Cancer, ARC), HEPTROMIC (Framework Programme 7), Cancéropole Ile de France, Centres de Ressources Biologiques (CRB) Liver Tumors, Tumorotheque Centre Hospitalier Universitaire Bordeaux and Centre Hospitalier Universitaire Henri Mondor, BioIntelligence (OSEO) and INSERM. J.-C.N. was supported by a fellowship from INCa. K.S. is supported by the Deutsche Forschungsgemeinschaft (DFG grant SCHU 2893/2-1). Research performed at Los Alamos National Laboratory was carried out under the auspices of the National Nuclear Security Administration of the US Department of Energy. V.M. is supported by a grant from AIRC (Italian Association for Cancer Research). J.M.L. is supported by grants from the European Comission Framework Programme 7 (HEPTROMIC, proposal 259744), The Samuel Waxman Cancer Research Foundation, the Spanish National Health Institute (SAF-2010-16055 and SAF-2013-41027) and the Asociación Española Contra el Cáncer (AECC).

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Authors and Affiliations

Authors

Contributions

Study concept and design: K.S., S.I., E.L., L.B.A., M.R.S., J.M.L. and J.Z.-R. Acquisition of data: J.C., S.R., G.C., C.M., F.S., A.-L.C., R.P., L.P., C.B., A.L., J.-F.B., V.M., A.V., J.-C.N. and P.B.-S. Analysis and interpretation of data: K.S., S.I., E.L., L.B.A., J.C., S.R., G.C., C.M., J.S., F.S., A.-L.C., R.P., L.P., A.V., J.-C.N. and J.Z.-R. Drafting the manuscript: K.S., S.I., E.L., S.R. and J.Z.-R. Critical revision of the manuscript: K.S., S.I., E.L., L.B.A., J.C., S.R., R.P., C.B., J.-F.B., J.-C.N., P.B.-S., J.M.L. and J.Z.-R. Statistical analysis: K.S., S.I. and E.L. Obtained funding: F.C., J.M.L. and J.Z.-R.

Corresponding author

Correspondence to Jessica Zucman-Rossi.

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Integrated supplementary information

Supplementary Figure 1 Statistics of mapping of sequencing reads.

(a) Summary statistics for whole-exome sequence reads of 243 HCCs with their non-tumor liver tissues. (b) Mean depth (with 95% IC) of reads on each chromosome, (c) Cumulative fraction of coding bases covered in captured regions. We considered 1-fold, 4-fold, 10-fold and 25-fold coverage (mean with 95% IC) per exome. Exomes are ordered by bait length (46–75 Mb).

Supplementary Figure 2 Reproducibility plot for the de novo analysis.

Signature stability and Frobenius reconstruction errors obtained for K = 1 to 11 signatures in our de novo mutational signature analysis using the WTSI framework. We chose to keep the decomposition in four signatures, which has good stability and a low Frobenius error.

Supplementary Figure 3 De novo mutational signature analysis and comparison with previously identified signatures.

(a) Mutation patterns of the four signatures identified de novo in our series. Mutation patterns are characterized by six substitution types (first two letters on the x axis) and further decomposed by the 5′ and 3′ bases surrounding the mutated base (indicated by a dot on the x axis). (b) Hierarchical clustering of the four de novo signatures with signatures previously identified in a pan-cancer study14. The cosine similarity between each pair of signatures is represented by a color code. The presence of a transcriptional strand bias and the abundance of indels were also taken into account to evaluate the similarity between de novo signatures and existing ones.

Supplementary Figure 4 Hypermutated tumor sample CHC892T.

Hypermutated tumor sample CHC892T, resected from a female patient who developed an HCC in a non-fibrotic liver presenting black anthracosic pigment deposition (arrow) in the non-tumor liver compartment, predominantly in macrophages and vessels. The brown pigment corresponded to lipofuscine deposition in hepatocytes (pericanalicular location).

Supplementary Figure 5 Comparison of mutation spectrums in our series with TCGA and ICGC-Japan data.

(a) The 96-mutation patterns observed in the 3 series. (b) Principal-component analysis of tumors belonging to the three series. As previously described25, Japanese cases and Asians from the TCGA cohort tend to cluster separately. We also identified two samples in the Japanese cohort with patterns very similar to our samples BCM723T (signature 6) and CHC892T (signature 23). (c) The 96-mutation patterns of the Japanese and INSERM cases corresponding to signature 6 (DNA MMR deficiency). (d) The 96-mutation patterns of the Japanese and INSERM cases corresponding to signature 23 (new signature associated with a hypermutator phenotype).

Supplementary Figure 6 Aflatoxin B1–related group in datasets of INSERM and TCGA.

(a) Principal-component analysis of INSERM and TCGA cases reveals a cluster of samples associated with classic aflatoxin B1–related features (African or Asian origin and TP53 R249S mutation). (b) Hierarchical clustering identifies a cluster of 11 cases comprising our 5 MSig2 tumors (signature 24) and 6 additional cases from the TCGA series. (b) Clinical characteristics of the 11 cases belonging to the aflatoxin B1 cluster.

Supplementary Figure 7 Mutational spectrum of WNT/β-catenin pathway, p53/cell cycle, chromatin remodeling and epigenetic regulation.

Supplementary Figure 8 Mutational spectrum of the PI3K/AKT/mTOR pathway, hepatic differentiation and the IL-6/JAK-STAT pathway.

Supplementary Figure 9 Mutational spectrum of the MAP kinases pathway, oxidative stress and the TGFβ pathway.

Supplementary Figure 10 Heat map of mutated groups of genes in 201 samples.

Supplementary Figure 11 Distribution and co-occurrence of targetable genes per sample.

Supplementary Figure 12 RSK2 silencing in liver cancer cell lines promotes ERK1/2 phosphorylation.

(a) The HepG2, Huh6, Huh7 and PLC/PRF5 cell lines were transfected with 2 nM of three different RPS6KA3 siRNAs (R1, R2, R3) or with a control siRNA (C) or transfection reagent alone (0). For each cell line, EGF stimulation (50 ng/μl for 10 min in serum-free medium) was performed as a positive control for ERK1/2 phosphorylation. Expression levels of RSK2, phospho-ERK1/2 (Thr202/Thyr204) and total ERK1/2 were analyzed by protein blotting in the different experimental conditions. β-actin was used as a loading control. (b) Schematic representation of the hypothesized role of RSK2 inactivation in activation of the ERK1/2 pathway. RSK2 was previously shown to exert feedback inhibition on the ERK1/2 pathway by phosphorylating and inhibiting son of sevenless (SOS). We hypothesized that RSK2 inactivation may lead to constitutive activation of the ERK1/2 pathway through the loss of negative feedback on the upstream regulator SOS.

Supplementary Figure 13 Number of targets as a function of histological stage.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1, 2 and 4–15. (PDF 2832 kb)

Supplementary Table 3

List of mutations identified by exome sequencing (hypermutated CHC892T excluded). (XLSX 2338 kb)

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Schulze, K., Imbeaud, S., Letouzé, E. et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 47, 505–511 (2015). https://doi.org/10.1038/ng.3252

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