Metabolomics of Prostate Cancer and Clinical Profiles Following Radiotherapy: Need for a Precision Phylometabolomics Approach

<b>Introduction</b>: Metabolomics-based phylogenetic profiling of prostate cancer (PCa) patients before and after stereotactic body radiation therapy (SBRT) can provide insight into the way in which treatment outcomes relate to the underlying physiology and physiological responses of individual patients. It also offers the potential for helping identify precision biomarkers. <b>Methods</b>: In this study, we used integrated mass spectrometry to obtain untargeted serum metabolomics data from PCa patients (<i>n</i> = 55), which we then analyzed using a parsimony phylogenetic systems biology approach before correlating the results with the patients' clinical parameters before and after treatment. <b>Results</b>: Radiotherapy (RT) generated five phylogenetic subgroups with distinct metabolomic profiles that did not correspond to hormonal treatment, risk assessment, metastasis, or PSA levels. PSA was neither a factor influencing clade membership nor an indicator of risk assessment or metastasis. Moreover, the hormone-treated patients did not form their own clade but were rather spread among the five clades. The same absence of correlation applied to risk assessment and metastasis. The 88 significantly altered pre-RT and 29 post-RT features showed aberrations in the metabolic pathways of purines, porphyrin, glycerophospholipids, and 2-methylglutaric acid, among others. <b>Discussion</b>: Significantly altered metabolites in a majority of patients who developed metastasis included D-tryptophan, carbamate, 5'-Benzoylphosphoadenosine, Phosphatidylcholine (PC), bilirubin, and hypoxanthine. In general, the cladogram offers a new perspective on evaluating the clinical variables that represent significant indicators of PCa progression, metastasis, and treatment response in individuals. <b>Conclusions</b>: Metabolic profiles and associated clinical phenotypes provided by this precision phylometabolomics approach may offer a deeper understanding of the metabolic factors and pathways implicated in cancer progression and metastasis and should contribute to the development of targeted treatments and more precise monitoring of cancer and cancer therapies.