RESUMEN
Serial CA125 and second line transvaginal ultrasound (TVS) screening in the UKCTOCS indicated a shift towards detection of earlier stage ovarian cancer (OvCa), but did not yield a significant mortality reduction. There remains a need to establish additional biomarkers that can complement CA125 for even earlier and at a larger proportion of new cases. Using a cohort of plasma samples from 219 OvCa cases (59 stage I/II and 160 stage III/IV) and 409 female controls and a novel Sensitivity Maximization At A Given Specificity (SMAGS) method, we developed a blood-based metabolite-based test consisting of 7 metabolites together with CA125 for detection of OvCa. At a 98.5% specificity cutpoint, the metabolite test achieved sensitivity of 86.2% for detection of early-stage OvCa and was able to capture 64% of the cases with low CA125 levels (< 35 units/mL). In an independent test consisting of 65 early-stage OvCa cases and 141 female controls, the metabolite panel achieved sensitivity of 73.8% at a 91.4% specificity and captured 13 (44.8%) out of 29 early-stage cases with CA125 levels < 35 units/mL. The metabolite test has utility for ovarian cancer screening, capable of improving upon CA125 for detection of early-stage disease.
RESUMEN
PURPOSE: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. EXPERIMENTAL DESIGN: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. RESULTS: A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76-0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84-0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. CONCLUSIONS: A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.