Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

ABSTRACT: Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resi

SUBMITTER: Lewis JE 

PROVIDER: S-EPMC8113601 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature