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Cancer stratification from mutation profiles

Banff International Research Station for Mathematical Innovation and Discovery

Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: MINES ParisTech

2017-09-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63102

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/