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Mutational interactions define novel cancer subgroups.

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Kuipers J,Thurnherr T,Moffa G,Suter P,Behr J,Goosen R,Christofori G,Beerenwinkel N

doi

10.1038/s41467-018-06867-x

subject

Has Abstract

pub_date

2018-10-19 00:00:00

pages

4353

issue

1

issn

2041-1723

pii

10.1038/s41467-018-06867-x

journal_volume

9

pub_type

杂志文章

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