# Development of Novel Ovarian Cancer Biomarkers for Early Detection Algorithms > **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $777,101 ## Abstract ABSTRACT Ovarian cancer (OC) is a deadly but often silent disease, showing no specific signs until it reaches advanced stages. The 5-year survival rate for advanced OC is only 50%, as most tumors ultimately become resistant to treatment.1,2 Advances in cytoreductive surgery and combination chemotherapy have improved 5-year survival in patients with epithelial OC, but the rate of cure has not improved over the last two decades. Computer models suggest that detection of OC in early stages (I-II) could substantially improve cure rates, but the low prevalence of OC in the general postmenopausal population restricts early detection efforts. Definitive diagnosis requires operative intervention, but a consensus is that no more than 10 operations should be performed to diagnose a single OC (>10% positive predictive value, PPV). According to current requirements, a first-line biomarker-based screening test must achieve a sensitivity (SN) of at least 75% and a specificity (SP) of 98%, which can then be further increased to 99.6% by adding a second-line screening modality such as transvaginal sonography (TVS). 1,3-6 Because available screening tests remain inadequate to merit wide implementation, based on our strong preliminary findings the proposed project aims to develop a novel, widely translatable, and economically feasible test that can reduce OC mortality rates. Currently, the only promising strategy developed in the United Kingdom Collaborative Trial for OC screening (UKCTOCS), is sequential analysis of the marker CA125 in serum over time (Risk of OC Algorithm, ROCA), followed by TVS. UKCTOCS yielded only a modest 20% decrease in mortality, insufficient to prompt the US Preventive Services Task Force to change its recommendation against population-based OC screening. 1 The most likely reason for such modest mortality reduction by CA125 measures is their insufficient lead-time (estimated interval for detection prior to symptoms-based diagnosis). Bio- mathematical modeling suggests that OC progresses to late stages more than 1 year before symptoms onset, a time range when CA125 levels offer only limited diagnostic power. Therefore, to improve current clinical practice, novel screening algorithms allowing substantially longer lead-times are needed. Based on our strong preliminary findings, we aim to develop and validate a 2-pronged approach, whereby a first-line multi-biomarker test recognizes OC with high SN (>80%) and modest SP (>80%), followed by a second-line biomarker velocity-based test in women who tested positive in the first test, that then yields a combined SP of 98%. Supporting this approach, we have generated a preliminary classification algorithm (threshold-based algorithm, TBA) based on one-time measurement of multiple biomarker concentrations, that identifies with 80%SN-70%SP women who will develop OC 1-7 years later. We further identified several biomarkers that display robust temporal dynamics (velocity) associated with OC development i... ## Key facts - **NIH application ID:** 10226017 - **Project number:** 5R01CA247220-02 - **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH - **Principal Investigator:** ROBERT C BAST - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $777,101 - **Award type:** 5 - **Project period:** 2020-09-01 → 2025-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10226017 ## Citation > US National Institutes of Health, RePORTER application 10226017, Development of Novel Ovarian Cancer Biomarkers for Early Detection Algorithms (5R01CA247220-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10226017. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*