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Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer.

ABSTRACT: Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype with poor prognosis. We sequenced 29 SCLC exomes, 2 genomes and 15 transcriptomes and found an extremely high mutation rate of 7.4±1 protein-changing mutations per million base pairs. Therefore, we conducted integrated analyses of the various data sets to identify pathogenetically relevant mutated genes. In all cases, we found evidence for inactivation of TP53 and RB1 and identified recurrent mutations in the CREBBP, EP300 and MLL genes that encode histone modifiers. Furthermore, we observed mutations in PTEN, SLIT2 and EPHA7, as well as focal amplifications of the FGFR1 tyrosine kinase gene. Finally, we detected many of the alterations found in humans in SCLC tumors from Tp53 and Rb1 double knockout mice. Our study implicates histone modification as a major feature of SCLC, reveals potentially therapeutically tractable genomic alterations and provides a generalizable framework for the identification of biologically relevant genes in the context of high mutational background.

SUBMITTER: Peifer M

PROVIDER: S-EPMC4915822 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer.

Peifer Martin M Fernández-Cuesta Lynnette L Sos Martin L ML George Julie J Seidel Danila D Kasper Lawryn H LH Plenker Dennis D Leenders Frauke F Sun Ruping R Zander Thomas T Menon Roopika R Koker Mirjam M Dahmen Ilona I Müller Christian C Di Cerbo Vincenzo V Schildhaus Hans-Ulrich HU Altmüller Janine J Baessmann Ingelore I Becker Christian C de Wilde Bram B Vandesompele Jo J Böhm Diana D Ansén Sascha S Gabler Franziska F Wilkening Ines I Heynck Stefanie S Heuckmann Johannes M JM Lu Xin X Carter Scott L SL Cibulskis Kristian K Banerji Shantanu S Getz Gad G Park Kwon-Sik KS Rauh Daniel D Grütter Christian C Fischer Matthias M Pasqualucci Laura L Wright Gavin G Wainer Zoe Z Russell Prudence P Petersen Iver I Chen Yuan Y Stoelben Erich E Ludwig Corinna C Schnabel Philipp P Hoffmann Hans H Muley Thomas T Brockmann Michael M Engel-Riedel Walburga W Muscarella Lucia A LA Fazio Vito M VM Groen Harry H Timens Wim W Sietsma Hannie H Thunnissen Erik E Smit Egbert E Heideman Daniëlle A M DA Snijders Peter J F PJ Cappuzzo Federico F Ligorio Claudia C Damiani Stefania S Field John J Solberg Steinar S Brustugun Odd Terje OT Lund-Iversen Marius M Sänger Jörg J Clement Joachim H JH Soltermann Alex A Moch Holger H Weder Walter W Solomon Benjamin B Soria Jean-Charles JC Validire Pierre P Besse Benjamin B Brambilla Elisabeth E Brambilla Christian C Lantuejoul Sylvie S Lorimier Philippe P Schneider Peter M PM Hallek Michael M Pao William W Meyerson Matthew M Sage Julien J Shendure Jay J Schneider Robert R Büttner Reinhard R Wolf Jürgen J Nürnberg Peter P Perner Sven S Heukamp Lukas C LC Brindle Paul K PK Haas Stefan S Thomas Roman K RK

Nature genetics 20120902 10

Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype with poor prognosis. We sequenced 29 SCLC exomes, 2 genomes and 15 transcriptomes and found an extremely high mutation rate of 7.4±1 protein-changing mutations per million base pairs. Therefore, we conducted integrated analyses of the various data sets to identify pathogenetically relevant mutated genes. In all cases, we found evidence for inactivation of TP53 and RB1 and identified recurrent mutations in the CREBBP, EP300 and MLL ...[more]

PMID: 22941188

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Project description:MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:IntroductionTargeted therapies for lung adenocarcinoma (LUAD) have improved patient outcomes; however, drug resistance remains a major problem. One strategy to achieve durable response is to develop combination-based therapies that target both mutated oncogenes and key modifiers of oncogene-driven tumorigenesis. This is based on the premise that mutated oncogenes, although necessary, are not sufficient for malignant transformation. We aimed to uncover genetic alterations that cooperate with mutant EGFR during LUAD development.MethodsWe performed integrative genomic analyses, combining copy number, gene expression and mutational information for over 500 LUAD tumors. Co-immunoprecipitation and Western blot analysis were performed in LUAD cell lines to confirm candidate interactions while RNA interference and gene overexpression were used for in vitro and in vivo functional assessment.ResultsWe identified frequent amplifications/deletions of chromosomal regions affecting the activity of genes specifically in the context of EGFR mutation, including amplification of the mutant EGFR allele and deletion of dual specificity phosphatase 4 (DUSP4), which have both previously been reported. In addition, we identified the novel amplification of a segment of chromosome arm 16p in mutant-EGFR tumors corresponding to increased expression of Golgi Associated, Gamma Adaptin Ear Containing, ARF Binding Protein 2 (GGA2), which functions in protein trafficking and sorting. We found that GGA2 interacts with EGFR, increases EGFR protein levels and modifies EGFR degradation after ligand stimulation. Furthermore, we show that overexpression of GGA2 enhances EGFR mediated transformation while GGA2 knockdown reduces the colony and tumor forming ability of EGFR mutant LUAD.ConclusionsThese data suggest that overexpression of GGA2 in LUAD tumors results in the accumulation of EGFR protein and increased EGFR signaling, which helps drive tumor progression. Thus, GGA2 plays a cooperative role with EGFR during LUAD development and is a potential therapeutic target for combination-based strategies in LUAD.

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Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer.

Publications

Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer.

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