Abstract P030: Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer

Abstract

Abstract Background: Ovarian cancer has poor prognosis due to >60% of the patients being diagnosed at late-stage. However, there is currently no ovarian cancer biomarker appropriate for screening. This may be, in part, due to many prior studies using clinical samples obtained at the time of diagnosis for biomarker discovery. Here, we sought to discover novel plasma metabolomic biomarkers for ovarian cancer early detection using prospectively collected blood samples. Methods: We examined 250 plasma metabolites in 93 high-grade serous ovarian cancer (HGSOC) cases with blood drawn up to five years prior to diagnosis and 93 matched controls in the prospective Nurses’ Health Studies and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We also measured metabolites in 40 early-stage HGSOC cases with blood collected at diagnosis and 40 matched controls in a hospital-based study. We used conditional logistic regression to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of the association between one standard deviation increase in metabolite levels and ovarian cancer risk, and used metabolite set enrichment analysis (MSEA) to identify metabolite group associations. False discovery rate (FDR) was used to correct for multiple testing. For the prospectively collected blood samples, we conducted analyses by time between blood draw and diagnosis. Results: Plasma metabolites associated with ovarian cancer in blood samples collected prospectively were different than in blood samples collected at diagnosis of early-stage disease compared to controls. There were 45 individual metabolites associated with early-stage disease (FDR<0.05), and none of these metabolites were associated with ovarian cancer in the prospectively collected blood samples (FDR<0.05). There were 10 metabolites associated with ovarian cancer diagnosed within 3 years of blood collection (p<0.05), including 7 lipid metabolites (2 lysophosphatidylcholines, 2 diglycerides, 1 phosphatidylcholine, 1 cholesteryl esters, 1 phosphatidylethanolamines) that were inversely associated with ORs ranging from 0.61-0.66. Interestingly, the direction of association remained similar when restricting to cases with blood collected up to 2 years and 1 year prior to diagnosis, and the magnitude of association generally became stronger for cases closer to diagnosis. Similarly, distinct profiles were observed in MSEA, with diglycerides being inversely associated with ovarian cancer diagnosis in prospectively collected blood samples (FDR ranging from <0.05 to <0.20) and positively associated in blood collected at early-stage disease diagnosis (FDR<0.05).Conclusion: Our results revealed plasma metabolomic profiles differ between prospectively collected blood samples up to 3 years prior to diagnosis and blood samples collected at time of diagnosis, even for early-stage disease, suggesting the use of prospectively collected blood samples may discover promising novel early detection biomarkers for ovarian cancer. Citation Format: Naoko Sasamoto, Oana A. Zeleznik, Allison F. Vitonis, Daniel W. Cramer, Julian Avila-Pacheco, Clary B. Clish, Britton Trabert, Shelley S. Tworoger, Kathryn L. Terry. Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P030.