Predicting clinical outcomes using cancer progression associated signatures.

ABSTRACT: Somatic mutation signatures are an informative facet of cancer aetiology, however they are rarely useful for predicting patient outcome. The aim of this study is to evaluate the utility of a panel of 142 mutation-signature-associated metrics (P142) for predicting cancer progression in patients from a 'TCGA PanCancer Atlas' cohort. The P142 metrics are comprised of AID/APOBEC and ADAR deaminase associated SNVs analyzed for codon context, strand bias, and transitions/transversions. TCGA tumor-normal mutation data was obtained for 10,437 patients, representing 31 of the most prevalent forms of cancer. Stratified random sampling was used to split patients into training, tuning and validation cohorts for each cancer type. Cancer specific machine learning (XGBoost) models were built using the ou

SUBMITTER: Mamrot J 

PROVIDER: S-EPMC8057277 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature