Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

ABSTRACT: Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfully revealed many known and novel signatures from whole exome data, but many still remain undiscovered. Here, we developed a deep learning approach, DeepMS, to decompose mutational signatures using 52,671,908 somatic mutations from 2780 highly curated cancer genomes with whole genome sequencing (WGS) in 37 cancer types/subtypes. With rigorous model training and comparison, we characterized 54 signatures for single base substitutions (SBSs), 11 for doublet base substitutions (DBSs) and 16 for small insertions and deletions (Indels). Compared to

SUBMITTER: Pei G 

PROVIDER: S-EPMC7334101 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature