The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes

Bernard Pereira(University of Cambridge), Suet‐Feung Chin(University of Cambridge), Oscar M. Rueda(University of Cambridge), Hans-Kristian Moen Vollan(Oslo University Hospital), Elena Provenzano(University of Cambridge), Helen Bardwell(University of Cambridge), Michelle Pugh, Linda Jones(University of Cambridge), Roslin Russell(University of Cambridge), Stephen‐John Sammut(University of Cambridge), Dana W.Y. Tsui(University of Cambridge), Bin Liu(University of Cambridge), Sarah‐Jane Dawson(University of Cambridge), Jean Abraham(University of Cambridge), Helen Northen(University of Chester), John F. Peden(University of Chester), Abhik Mukherjee(Nottingham University Hospitals NHS Trust), Gulisa Turashvili(Queen's University), Andrew R. Green(Nottingham University Hospitals NHS Trust), Steve McKinney(BC Cancer Agency), Arusha Oloumi(BC Cancer Agency), Sohrab P. Shah(BC Cancer Agency), Nitzan Rosenfeld(University of Cambridge), Leigh C. Murphy(Research Institute in Oncology and Hematology), David Bentley(University of Chester), Ian O. Ellis(Nottingham University Hospitals NHS Trust), Arnie Purushotham(King's College London), Sarah E. Pinder(King's College London), Anne‐Lise Børresen‐Dale(University of Oslo), Helena Earl(University of Cambridge), Paul D.P. Pharoah(University of Cambridge), Mark T. Ross(University of Chester), Samuel Aparício(BC Cancer Agency), Carlos Caldas(University of Cambridge)
Nature Communications
May 10, 2016
Cited by 1,793Open Access
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Abstract

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.


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