Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates

Philip Webster, Joanna C. Dawes, Hamlata Dewchand, Katalin Takacs, Barbara Iadarola, Bruce J. Bolt, Juan J. Caceres, Jakub Kaczor, Gopuraja Dharmalingam, Marian Dore, Laurence Game, Thomas Adejumo, James Elliott, Kikkeri Naresh, Mohammad Karimi, Katerina Rekopoulou, Ge Tan, Alberto Paccanaro and Anthony G. Uren ()
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Philip Webster: MRC London Institute of Medical Sciences (LMS)
Joanna C. Dawes: MRC London Institute of Medical Sciences (LMS)
Hamlata Dewchand: MRC London Institute of Medical Sciences (LMS)
Katalin Takacs: MRC London Institute of Medical Sciences (LMS)
Barbara Iadarola: MRC London Institute of Medical Sciences (LMS)
Bruce J. Bolt: MRC London Institute of Medical Sciences (LMS)
Juan J. Caceres: University of London
Jakub Kaczor: MRC London Institute of Medical Sciences (LMS)
Gopuraja Dharmalingam: MRC London Institute of Medical Sciences (LMS)
Marian Dore: MRC London Institute of Medical Sciences (LMS)
Laurence Game: MRC London Institute of Medical Sciences (LMS)
Thomas Adejumo: MRC London Institute of Medical Sciences (LMS)
James Elliott: MRC London Institute of Medical Sciences (LMS)
Kikkeri Naresh: Imperial College Healthcare NHS Trust
Mohammad Karimi: MRC London Institute of Medical Sciences (LMS)
Katerina Rekopoulou: MRC London Institute of Medical Sciences (LMS)
Ge Tan: MRC London Institute of Medical Sciences (LMS)
Alberto Paccanaro: University of London
Anthony G. Uren: MRC London Institute of Medical Sciences (LMS)

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: Abstract Determining whether recurrent but rare cancer mutations are bona fide driver mutations remains a bottleneck in cancer research. Here we present the most comprehensive analysis of murine leukemia virus-driven lymphomagenesis produced to date, sequencing 700,000 mutations from >500 malignancies collected at time points throughout tumor development. This scale of data allows novel statistical approaches for identifying selected mutations and yields a high-resolution, genome-wide map of the selective forces surrounding cancer gene loci. We also demonstrate negative selection of mutations that may be deleterious to tumor development indicating novel avenues for therapy. Screening of two BCL2 transgenic models confirmed known drivers of human non-Hodgkin lymphoma, and implicates novel candidates including modifiers of immunosurveillance and MHC loci. Correlating mutations with genotypic and phenotypic features independently of local variance in mutation density also provides support for weakly evidenced cancer genes. An online resource http://mulvdb.org allows customized queries of the entire dataset.

Date: 2018
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DOI: 10.1038/s41467-018-05069-9

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