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.

Peroxiredoxins have a long-established cellular function as regulators of redox metabolism by catalyzing the reduction of peroxides (e.g., H₂O₂, lipid peroxides) with high catalytic efficiency. This activity is also critical to the initiation and relay of both phosphorylation and redox signaling in a broad range of pathophysiological contexts. Under normal physiological conditions, peroxiredoxins protect normal cells from oxidative damage that could promote oncogenesis (e.g., environmental stressors). In cancer, higher expression level of peroxiredoxins has been associated with both tumor growth and resistance to radiation therapies. However, this relationship between the expression of peroxiredoxins and the response to radiation is not evident from an analysis of data in The Cancer Genome Atlas (TCGA) or NCI60 panel of cancer cell lines. The focus of this review is to summarize the current experimental knowledge implicating this class of proteins in cancer, and to provide a perspective on the value of targeting peroxiredoxins in the management of cancer. Potential biases in the analysis of the TCGA data with respect to radiation resistance are also highlighted.

Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status. Significance: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma.