Dataset Information

Comprehensive genomic profiles of small cell lung cancer.

ABSTRACT: We have sequenced the genomes of 110 small cell lung cancers (SCLC), one of the deadliest human cancers. In nearly all the tumours analysed we found bi-allelic inactivation of TP53 and RB1, sometimes by complex genomic rearrangements. Two tumours with wild-type RB1 had evidence of chromothripsis leading to overexpression of cyclin D1 (encoded by the CCND1 gene), revealing an alternative mechanism of Rb1 deregulation. Thus, loss of the tumour suppressors TP53 and RB1 is obligatory in SCLC. We discovered somatic genomic rearrangements of TP73 that create an oncogenic version of this gene, TP73?ex2/3. In rare cases, SCLC tumours exhibited kinase gene mutations, providing a possible therapeutic opportunity for individual patients. Finally, we observed inactivating mutations in NOTCH family genes in 25% of human SCLC. Accordingly, activation of Notch signalling in a pre-clinical SCLC mouse model strikingly reduced the number of tumours and extended the survival of the mutant mice. Furthermore, neuroendocrine gene expression was abrogated by Notch activity in SCLC cells. This first comprehensive study of somatic genome alterations in SCLC uncovers several key biological processes and identifies candidate therapeutic targets in this highly lethal form of cancer.

SUBMITTER: George J

PROVIDER: S-EPMC4861069 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive genomic profiles of small cell lung cancer.

George Julie J Lim Jing Shan JS Jang Se Jin SJ Cun Yupeng Y Ozretić Luka L Kong Gu G Leenders Frauke F Lu Xin X Fernández-Cuesta Lynnette L Bosco Graziella G Müller Christian C Dahmen Ilona I Jahchan Nadine S NS Park Kwon-Sik KS Yang Dian D Karnezis Anthony N AN Vaka Dedeepya D Torres Angela A Wang Maia Segura MS Korbel Jan O JO Menon Roopika R Chun Sung-Min SM Kim Deokhoon D Wilkerson Matt M Hayes Neil N Engelmann David D Pützer Brigitte B Bos Marc M Michels Sebastian S Vlasic Ignacija I Seidel Danila D Pinther Berit B Schaub Philipp P Becker Christian C Altmüller Janine J Yokota Jun J Kohno Takashi T Iwakawa Reika R Tsuta Koji K Noguchi Masayuki M Muley Thomas T Hoffmann Hans H Schnabel Philipp A PA Petersen Iver I Chen Yuan Y Soltermann Alex A Tischler Verena V Choi Chang-min CM Kim Yong-Hee YH Massion Pierre P PP Zou Yong Y Jovanovic Dragana D Kontic Milica M Wright Gavin M GM Russell Prudence A PA Solomon Benjamin B Koch Ina I Lindner Michael M Muscarella Lucia A LA la Torre Annamaria A Field John K JK Jakopovic Marko M Knezevic Jelena J Castaños-Vélez Esmeralda E Roz Luca L Pastorino Ugo U Brustugun Odd-Terje OT Lund-Iversen Marius M Thunnissen Erik E Köhler Jens J Schuler Martin M Botling Johan J Sandelin Martin M Sanchez-Cespedes Montserrat M Salvesen Helga B HB Achter Viktor V Lang Ulrich U Bogus Magdalena M Schneider Peter M PM Zander Thomas T Ansén Sascha S Hallek Michael M Wolf Jürgen J Vingron Martin M Yatabe Yasushi Y Travis William D WD Nürnberg Peter P Reinhardt Christian C Perner Sven S Heukamp Lukas L Büttner Reinhard R Haas Stefan A SA Brambilla Elisabeth E Peifer Martin M Sage Julien J Thomas Roman K RK

Nature 20150713 7563

We have sequenced the genomes of 110 small cell lung cancers (SCLC), one of the deadliest human cancers. In nearly all the tumours analysed we found bi-allelic inactivation of TP53 and RB1, sometimes by complex genomic rearrangements. Two tumours with wild-type RB1 had evidence of chromothripsis leading to overexpression of cyclin D1 (encoded by the CCND1 gene), revealing an alternative mechanism of Rb1 deregulation. Thus, loss of the tumour suppressors TP53 and RB1 is obligatory in SCLC. We dis ...[more]

PMID: 26168399

Similar Datasets

Project description:BackgroundThe aim of this study was to carry out a descriptive analysis of the somatic genetic profile and co-occurring mutations of non-small cell lung cancer (NSCLC) samples from patients tested with comprehensive genomic profiling (CGP).MethodsThis was a retrospective cross-sectional study of patients diagnosed with NSCLC from 2013 to 2018 in Brazil and whose samples were submitted to CGP (FoundationOne or FoundationACT) using either tumor or circulating tumor DNA (ctDNA) from plasma.ResultsWe recovered 513 CGP results from patients, 457 (89.1%) of which were from tumors and 56 (10.9%) from plasma. The median age of patients was 64 years old, of which 51.6% were males. TP53 mutations were identified in 53.6% of tumor samples, KRAS mutations in 24.2%, EGFR activating mutations were detected in 22.5%, STK11 mutations in 11.6%, PIK3CA mutations in 8.8%, ALK rearrangements in 5.4%, BRAF mutations in 5.2%, and ERBB2 alterations in 4.9%. The most commonly comutated gene was TP53. TP53 p.R337H was observed in 4.3% of samples and was associated with somatic mutations in EGFR and ERBB2 (P < 0.00001). Tumor mutational burden (TMB) analysis was available for 80.5% of samples tested, and 5.5% of samples had high TMB (≥ 20 mutations/Mb). In conclusion, this retrospective analysis of genomic data from NSCLC patients obtained by CGP showed that common abnormalities such as EGFR mutations and ALK rearrangements had similar frequency to those previously described by other groups using others strategies. Additionally, our data confirm an association between TP53 p.R337H, supposedly germline in nature, and somatic mutations in genes of the HER family.Key pointsSIGNIFICANT FINDINGS OF THE STUDY: This is the first report of the prevalence of driver mutations in Brazilian NSCLC patients using comprehensive genomic profiling (CGP). The frequency of the most common driver mutations in this population was similar to that previously described in Brazil.What this study addsTP53 was the most commonly comutated gene across samples. TP53 p.R337H was associated with somatic mutations in EGFR and ERBB2. Most samples had low TMB; only 5.5% of samples had high TMB.

Dataset Information

Comprehensive genomic profiles of small cell lung cancer.

Publications

Comprehensive genomic profiles of small cell lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets