Data from Metabolomic Prediction of Breast Cancer Treatment–Induced Neurologic and Metabolic Toxicities

<div>AbstractPurpose:<p>Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities.</p>Experimental Design:<p>Untargeted high-resolution metabolomic profiles of 992 patients with estrogen receptor (ER)⁺/HER2⁻ breast cancer from the prospective CANTO cohort were acquired (<i>n</i> = 1935 metabolites). A residual-based modeling strategy with discovery and validation cohorts was used to benchmark machine learning algorithms, taking into account confounding variables.</p>Results:<p>Adaptive Least Absolute Shrinkage and Selection (adaptive LASSO) has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and nonannotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurologic and metabolic toxicity profiles.</p>Conclusions:<p>Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.</p></div>