H. Zhou, J.-L. Chaubard, B. Benigno, J. Skolnick
Early detection of ovarian cancer remains a clinical challenge because available blood biomarkers lack the sensitivity and specificity required for population screening. We tested whether early-stage ovarian cancer is associated with a distributed physiological state in the circulating metabolome. We analyzed untargeted metabolomic profiles from two independent retrospective cohorts: 91 serum samples (59 ovarian cancer, 32 healthy controls) and 83 plasma samples (63 ovarian cancer, 20 healthy controls). Assay-specific boosted decision trees were trained and evaluated independently within each cohort using five-fold cross-validation repeated over 50 randomized rounds. At selected operating points, mean cross-validated sensitivity and specificity were 99.0% and 99.8% in serum and 97.7% and 99.7% in plasma. Restricting inputs to strongly dysregulated features did not improve the overall sensitivity false positive rate trade off, and smaller panels reduced sensitivity. The cohorts shared 239 concordantly altered annotated features spanning lipid, amino-acid, steroid, central-carbon, and redox metabolism. These findings are consistent with a distributed metabolic response involving tumor and host, although tissue contributions were not measured. We propose that the classifier recognizes a metabolomic Systemotype, an integrated physiological state reflected in circulating metabolites. The results support further investigation of this framework.