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Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

ABSTRACT: We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

SUBMITTER: Nik-Zainal S

PROVIDER: S-EPMC4910866 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

Nik-Zainal Serena S Davies Helen H Staaf Johan J Ramakrishna Manasa M Glodzik Dominik D Zou Xueqing X Martincorena Inigo I Alexandrov Ludmil B LB Martin Sancha S Wedge David C DC Van Loo Peter P Ju Young Seok YS Smid Marcel M Brinkman Arie B AB Morganella Sandro S Aure Miriam R MR Lingjærde Ole Christian OC Langerød Anita A Ringnér Markus M Ahn Sung-Min SM Boyault Sandrine S Brock Jane E JE Broeks Annegien A Butler Adam A Desmedt Christine C Dirix Luc L Dronov Serge S Fatima Aquila A Foekens John A JA Gerstung Moritz M Hooijer Gerrit K J GK Jang Se Jin SJ Jones David R DR Kim Hyung-Yong HY King Tari A TA Krishnamurthy Savitri S Lee Hee Jin HJ Lee Jeong-Yeon JY Li Yilong Y McLaren Stuart S Menzies Andrew A Mustonen Ville V O'Meara Sarah S Pauporté Iris I Pivot Xavier X Purdie Colin A CA Raine Keiran K Ramakrishnan Kamna K Rodríguez-González F Germán FG Romieu Gilles G Sieuwerts Anieta M AM Simpson Peter T PT Shepherd Rebecca R Stebbings Lucy L Stefansson Olafur A OA Teague Jon J Tommasi Stefania S Treilleux Isabelle I Van den Eynden Gert G GG Vermeulen Peter P Vincent-Salomon Anne A Yates Lucy L Caldas Carlos C van't Veer Laura L Tutt Andrew A Knappskog Stian S Tan Benita Kiat Tee BK Jonkers Jos J Borg Åke Å Ueno Naoto T NT Sotiriou Christos C Viari Alain A Futreal P Andrew PA Campbell Peter J PJ Span Paul N PN Van Laere Steven S Lakhani Sunil R SR Eyfjord Jorunn E JE Thompson Alastair M AM Birney Ewan E Stunnenberg Hendrik G HG van de Vijver Marc J MJ Martens John W M JW Børresen-Dale Anne-Lise AL Richardson Andrea L AL Kong Gu G Thomas Gilles G Stratton Michael R MR

Nature 20160502 7605

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended ...[more]

PMID: 27135926

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Project description:BackgroundThe advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types.ResultsIn this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways.ConclusionsThis new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer .

Dataset Information

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

Publications

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

Similar Datasets

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