Mutational interactions define novel cancer subgroups

Jack Kuipers (), Thomas Thurnherr, Giusi Moffa, Polina Suter, Jonas Behr, Ryan Goosen, Gerhard Christofori and Niko Beerenwinkel ()
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Jack Kuipers: ETH Zurich
Thomas Thurnherr: ETH Zurich
Giusi Moffa: University College London
Polina Suter: ETH Zurich
Jonas Behr: ETH Zurich
Ryan Goosen: University of Basel
Gerhard Christofori: University of Basel
Niko Beerenwinkel: ETH Zurich

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.

Date: 2018
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DOI: 10.1038/s41467-018-06867-x

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