Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. PURPOSE: The Normal Risk Ovarian Screening Study (NROSS) tested a two-stage screening strategy in postmenopausal women at conventional hereditary risk where significantly rising cancer antigen (CA)-125 prompted transvaginal sonography (TVS) and abnormal TVS prompted surgery to detect ovarian cancer. METHODS: A total of 7,856 healthy postmenopausal women were screened annually for a total of 50,596 woman-years in a single-arm study (ClinicalTrials.gov identifier: NCT00539162 ). Serum CA125 was analyzed with the Risk of Ovarian Cancer Algorithm (ROCA) each year. If risk was unchanged and <1:2,000, women returned in a year. If risk increased above 1:500, TVS was undertaken immediately, and if risk was intermediate, CA125 was repeated in 3 months with a further increase in risk above 1:500 prompting referral for TVS. An average of 2% of participants were referred to TVS annually. RESULTS: Thirty-four patients were referred for operations detecting 15 ovarian cancers and two borderline tumors with 12 in early stage (I-II). In addition, seven endometrial cancers were detected with six in stage I. As four ovarian cancers and two borderline tumors were diagnosed with a normal ROCA, the sensitivity for detecting ovarian and borderline cancer was 74% (17 of 23), and 70% of ROCA-detected cases (12 of 17) were in stage I-II. NROSS screening reduced late-stage (III-IV) disease by 34% compared with UKCTOCS controls and by 30% compared with US SEER values. The positive predictive value (PPV) was 50% (17 of 34) for detecting ovarian cancer and 74% (25 of 34) for any cancer, far exceeding the minimum acceptable study end point of 10% PPV. CONCLUSION: While the NROSS trial was not powered to detect reduced mortality, the high specificity, PPV, and marked stage shift support further development of this strategy.

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.

In epithelial ovarian cancer (EOC), carboplatin/cisplatin-induced chemoresistance is a major hurdle to successful treatment. Aerobic glycolysis is a common characteristic of cancer. However, the role of glycolytic metabolism in chemoresistance and its impact on clinical outcomes in EOC are not clear. Here, we show a functional interaction between the key glycolytic enzyme hexokinase II (HKII) and activated P-p53 (Ser15) in the regulation of bioenergetics and chemosensitivity. Using translational approaches with proximity ligation assessment in cancer cells and human EOC tumor sections, we showed that nuclear HKII–P-p53 (Ser15) interaction is increased after chemotherapy, and functions as a determinant of chemoresponsiveness as a prognostic biomarker. We also demonstrated that p53 is required for the intracellular nuclear HKII trafficking in the control of glycolysis in EOC, associated with chemosensitivity. Mechanistically, cisplatin-induced P-p53 (Ser15) recruits HKII and apoptosis-inducing factor (AIF) in chemosensitive EOC cells, enabling their translocation from the mitochondria to the nucleus, eliciting AIF-induced apoptosis. Conversely, in p53-defective chemoresistant EOC cells, HKII and AIF are strongly bound in the mitochondria and, therefore, apoptosis is suppressed. Collectively, our findings implicate nuclear HKII–P-p53(Ser15) interaction in chemosensitivity and could provide an effective clinical strategy as a promising biomarker during platinum-based therapy.

Supplementary Figure from A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Supplementary Table from A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses