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

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 -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients. Personalized prediction of tumor radiosensitivity would facilitate development of precision medicine workflows for cancer treatment. Here, the authors integrate machine learning and genome-scale metabolic modeling approaches to identify multi-omics biomarkers predictive of radiation response.

Automated summary

Resistance to ionizing radiation is a major clinical challenge in cancer treatment. Personalized prediction of tumor radiosensitivity is hindered by insufficient accuracy of existing machine learning classifiers, which do not include metabolomics data. By integrating metabolic features with other multi-omics datasets using machine learning, improved classification accuracy can be achieved, facilitating the development of precision medicine workflows for cancer treatment.