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Activity Number: 173 - Bayesian Methods Applied to Biometric Problems
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #305193
Title: From Mutation Signatures to Patient Subgroups: An Application of Latent Dirichlet Allocation Relating Mutational Signatures to Patient Characteristics
Author(s): LiJin Joo* and Seyoung Park and Hongyu Zhao
Companies: Yale University/Takeda Pharmaceutical and Sungkyunkwan University and Yale
Keywords: Cancer Genomics; Cancer Somatic Mutations; Variational Bayes; Topic Modeling; Personalized Medicine

We propose a stochastic labeling method for testing functional or regional enrichment pattern by mutational signatures in cancer. Mutation signatures in cancer are common mutation patterns in the 96 tri-nucleotide sequence contexts (i.e. six possible mutations for the middle nucleotide in the context of 16 possible combinations of two flanking nucleotides) of population samples learned by non-negative matrix factorization (NMF) on cancer samples. The learned mutation patterns contain information on mutagenic sources such as external carcinogens, endogenous mutagens, or genomic defects. However, mixed scores by NMF do not provide a link which mutation is attributable to which signature, thus are limited to quantify the burden of each signature attributable to risk factors.

We extended Latent Dirichlet Allocation (LDA) incorporating co-occurrence patterns of mutations and correlation among signatures. In this proof-of-concept study, we showed the utility of stochastic labeling methods for generating informative annotations of mutational signatures compared to existing deterministic labeling methods.

Authors who are presenting talks have a * after their name.

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