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Neutral tumor evolution

Published on Dec 1, 2018in Nature Genetics25.455
· DOI :10.1038/s41588-018-0258-x
Maxime Tarabichi5
Estimated H-index: 5
(Francis Crick Institute),
Inigo Martincorena30
Estimated H-index: 30
(Wellcome Trust Sanger Institute)
+ 7 AuthorsPeter Van Loo48
Estimated H-index: 48
(Katholieke Universiteit Leuven)
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Abstract
  • References (22)
  • Citations (10)
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Summary Cancer develops as a result of somatic mutation and clonal selection, but quantitative measures of selection in cancer evolution are lacking. We adapted methods from molecular evolution and applied them to 7,664 tumors across 29 cancer types. Unlike species evolution, positive selection outweighs negative selection during cancer development. On average, 10/tumor in endometrial and colorectal cancers. Half of driver substitutions occur in yet-to-be-discovered cancer genes. With increasing...
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Recent studies suggest that the evolutionary history of a cancer is important in forecasting clinical outlook. To gain insight into the clonal dynamics of multiple myeloma (MM) and its possible influence on patient outcome we analysed whole exome sequencing tumor data for 333 patients from Myeloma XI, a UK phase III trial and 434 patients from the CoMMpass study, all of which had received immunomodulatory therapy (IMiD). By analysing mutant allele frequency distributions in tumors we found that ...
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In this issue of Blood , Erdmann et al examine the importance of phosphoinositide 3-kinase (PI3K) in diffuse large B-cell lymphoma (DLBCL) and rationalize dual inhibition of the p110 α and δ isoforms. 1
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COSMIC, the Catalogue of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk) is a high-resolution resource for exploring targets and trends in the genetics of human cancer. Currently the broadest database of mutations in cancer, the information in COSMIC is curated by expert scientists, primarily by scrutinizing large numbers of scientific publications. Over 4 million coding mutations are described in v78 (September 2016), combining genome-wide sequencing results from 28 366 tumours with co...
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© 2016 John Wiley & Sons, Inc. CaVEMan is an expectation maximization-based somatic substitutiondetection algorithm that is written in C. The algorithm analyzes sequence data from a test sample, such as a tumor relative to a reference normal sample from the same patient and the reference genome. It performs a comparative analysis of the tumor and normal sample to derive a probabilistic estimate for putative somatic substitutions.When combined with a set of validated post-hoc filters, CaVEMan gen...
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The authors analyze the extent of intratumor heterogeneity across 12 tumor types to reveal that increased heterogeneity is a general phenomenon and has a biphasic contribution to tumor progression.
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