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 output from the P142 panel to predict patient Progression Free Survival (PFS) status as either “High PFS” or “Low PFS”. Predictive performance of each model was evaluated using the validation cohort. Models accurately predicted PFS status for several cancer types, including adrenocortical carcinoma, glioma, mesothelioma, and sarcoma. In conclusion, the P142 panel of metrics successfully predicted cancer progression status in patients with some, but not all cancer types analyzed. These results pave the way for future studies on cancer progression associated signatures.
Highlights
Cancer is a leading cause of human mortality worldwide and the incidence of cancer is expected to rise as our average life expectancy increases [1,2,3]
The aim of this study is to evaluate the efficacy of a panel of 142 deaminase-associated metrics for predicting the rate of cancer progression in patients selected from The Cancer Genome Atlas (TCGA) PanCancer Atlas cohort
The panel of 142 mutation-signature–associated metrics (P142) panel of metrics was applied to every patient eligible for inclusion in the study and the results were collated into patient profiles
Summary
Cancer is a leading cause of human mortality worldwide and the incidence of cancer is expected to rise as our average life expectancy increases [1,2,3]. Despite many recent advances in treatment, the immense socio-economic burden of cancer persists [1,2,3]. A key strategy for reducing the burden of cancer is to personalize treatment regimes to optimize patient outcomes [4]. Less than 25% of patients benefit from personalized care [5, 6] and efforts to increase adoption and utility are ongoing, for example by incorporating novel biomarkers into existing treatment methods. Effective personalized cancer treatment requires a detailed understanding of the aetiology, physiology and molecular biology of the cancerous cells. Many of the mechanisms driving cancer progression are still not fully understood, such as the causes, effects, and patterns of DNA mutation in oncogenesis
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