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Activity Number: 388 - Statistical and Computational Advances in Cancer Genomics with Application to Precision Medicine
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #326737 Presentation
Title: Unsupervised Learning for Deciphering Mutational Signatures in Human Cancer
Author(s): Ludmil B Alexandrov* and Velimir V Vesselinov and Boian S Alexandrov
Companies: University of California, San Diego and Los Alamos National Lab and Los Alamos National Lab
Keywords: mutational signatures; unsupervised machine learning; matrix factorization; tensor factorization; carcinogenesis; mutagenesis

The genome of a cancer carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Each process causing mutations leaves a characteristic imprint on the genome of a cancer cell, termed, mutational signature.

In this talk, I will demonstrate that modeling mutational signatures as a blind source separation problem allows developing an effecting computational methodology for deciphering mutational signatures from cancer genomes. By applying unsupervised machine learning matrix factorization approaches to 12,023 cancers, we are able to reveal more than 40 distinct mutational signatures. Further application of tensor factorization approaches reveals the dynamics of mutational signatures across cancer subclones. Many of the signatures extracted in an unsupervised manner match mutational patterns generated by known carcinogens.

The results reveal the diversity of mutational processes underlying the development of cancer as well as the ability of unsupervised machine learning approaches to extract meaningful previously unknown features from complex datasets.

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

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