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

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      Nature genetics

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          Genomic analyses promise to improve tumor characterization in order to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors revealed mutational signatures associated with specific risk factors, mainly combined alcohol/tobacco consumption, and aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrent pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (HBV), and AXIN1. Analyses according to tumor stage progression revealed TERT promoter mutation as an early event whereas FGF3, FGF4, FGF19/CCND1 amplification, TP53 and CDKN2A alterations, appeared at more advanced stages in aggressive tumors. In 28% of the tumors we identified genetic alterations potentially targetable by 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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          A method and server for predicting damaging missense mutations

          To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (, which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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            Signatures of mutational processes in human cancer

            All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.
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              The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

              The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .

                Author and article information

                Nat Genet
                Nat. Genet.
                Nature genetics
                1 April 2015
                30 March 2015
                May 2015
                01 November 2015
                : 47
                : 5
                : 505-511
                [1 ]Inserm, UMR-1162, Génomique fonctionnelle des Tumeurs solides, Equipe Labellisée Ligue Contre le Cancer, Institut Universitaire d’Hematologie, Paris, F-75010 France
                [2 ]Université Paris Descartes, Labex Immuno-Oncology, Sorbonne Paris Cité, Faculté de Médecine, Paris, France
                [3 ]Université Paris 13, Sorbonne Paris Cité, Unité de Formation et de Recherche Santé, Medecine, Biologie humaine, F-93000 Bobigny, France
                [4 ]Université Paris Diderot, F-75013 Paris
                [5 ]Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK
                [6 ]Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [7 ]Assistance Publique-Hôpitaux de Paris, Department of Pathology, Centre hospitalier universitaire Henri Mondor, F-94000 Créteil, France
                [8 ]Hepatocellular Carcinoma Translational Research Laboratory, Barcelona-Clínic Liver Cancer Group, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Liver Unit. Centro de Investigación Biomédica en Red: Enfermedades Hépaticas y Digestivas; Hospital Clínic, Barcelona, Catalonia, Spain
                [9 ]Inserm, UMR-1053; Université de Bordeaux, Bordeaux, F-33076, France
                [10 ]Assistance Publique-Hôpitaux de Paris, Department of Digestive and Hepatobiliary Surgery, Centre hospitalier universitaire Henri Mondor, F-94000 Créteil, France
                [11 ]Inserm, UMR-955, F-94000 Créteil, France
                [12 ]Centre hospitalier universitaire de Bordeaux, Department of Hepatology, Hôpital Saint-André, Bordeaux, F-33076, France
                [13 ]Department of Liver Surgery and Transplant, Fondazione Istituto Tumori, via Venezian 1, 20133 Milan, Italy
                [14 ]Assistance Publique Hôpitaux de Paris, Hôpitaux Universitaires Paris – Seine Saint-Denis, Site Jean Verdier, Pôle d’Activité Cancérologique Spécialisée, Service d’Hépatologie, F-93143 Bondy, France
                [15 ]Centre hospitalier universitaire de Bordeaux, Pellegrin Hospital, Department of Pathology, Bordeaux, F-33076, France
                [16 ]Mount Sinai Liver Cancer Program (Division of Liver Diseases), Mount Sinai School of Medicine, New York, USA
                [17 ]Institució Catalana de Recerca i Estudis Avançats, Barcelona. Catalonia, Spain
                [18 ]Assistance Publique-Hôpitaux de Paris, Hopital Europeen Georges Pompidou, F-75015 Paris, France
                Author notes
                Correspondence should be addressed to: Jessica Zucman-Rossi; MD, PhD, INSERM U 1162, Génomique fonctionnelle des tumeurs solides, 27 Rue Juliette Dodu, 75010 Paris, France, TEL: +33 1 53 72 51 66, FAX: +33 1 53 72 51 92, jessica.zucman-rossi@

                Authors Contributions:

                Study concept and design: KS, SI, EL, LBA, MRS, JML, JZR

                Acquisition of data: JC, SR, GC,CM, FS, ALC, RP, LP, CB, AL, JFB, VM, AV, JCN, PBS

                Analysis and interpretation of data: KS, SI, EL, LBA, JC, SR, GC, CM, JS, FS, ALC, RP LP, AV, JCN, JZR

                Drafting the manuscript: KS, SI, EL, SR, JZR

                Critical revision of the manuscript: KS, SI, EL, LBA, JC, SR, RP, CB, JFB, JCN, PBS, JML, JZR

                Statistical analysis: KS, SI, EL

                Obtained funding: FC, JML, JZR




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