Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models Journal Article


Authors: Funnell, T.; Zhang, A. W.; Grewal, D.; McKinney, S.; Bashashati, A.; Wang, Y. K.; Shah, S. P.
Article Title: Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models
Abstract: Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies. © 2019 Funnell et al.
Journal Title: PLoS Computational Biology
Volume: 15
Issue: 2
ISSN: 1553-7358
Publisher: Public Library of Science  
Date Published: 2019-02-22
Start Page: e1006799
Language: English
DOI: 10.1371/journal.pcbi.1006799
PUBMED: 30794536
PROVIDER: scopus
PMCID: PMC6402697
DOI/URL:
Notes: Article -- Export Date: 1 April 2019 -- Source: Scopus
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MSK Authors
  1. Sohrab Prakash Shah
    5 Shah
  2. Diljot Singh Grewal
    1 Grewal