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OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study

R. Kandimalla, W. Wang, F. Yu, N. Zhou, F. Gao, M. Spillman, L. Moukova, O. Slaby, B. Salhia, S. Zhou, X. Wang, A. Goel

. 2021 ; 27 (15) : 4277-4286. [pub] 20210525

Language English Country United States

Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Validation Study

Persistent link   https://www.medvik.cz/link/bmc22019686

PURPOSE: Due to the lack of effective screening approaches and early detection biomarkers, ovarian cancer has the highest mortality rates among gynecologic cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRNA (miRNA) biomarkers for the early detection of ovarian cancer. EXPERIMENTAL DESIGN: During the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (n = 31), which was subsequently validated in multiple, independent data sets (TCGA, n = 543; GSE65819, n = 87). Subsequently, we performed multivariate logistic regression-based training in a serum data set (GSE106817, n = 640), followed by its independent validation in three retrospective data sets (GSE31568, n = 85; GSE113486, n = 140; Czech Republic cohort, n = 192) and one prospective serum cohort (n = 95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in several other cancers (GSE31568 cohort, n = 369). RESULTS: The OCaMIR demonstrated a robust diagnostic accuracy in the stage I high-grade serous ovarian cancer patients in the discovery cohort (AUC = 0.99), which was consistently reproducible in both stage I (AUC = 0.96) and all stage patients (AUC = 0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC = 0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared with CA125 levels, even in stage I patients, and was more cost-effective, highlighting its potential role for screening and early detection of ovarian cancer. CONCLUSIONS: Small RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.

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$a PURPOSE: Due to the lack of effective screening approaches and early detection biomarkers, ovarian cancer has the highest mortality rates among gynecologic cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRNA (miRNA) biomarkers for the early detection of ovarian cancer. EXPERIMENTAL DESIGN: During the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (n = 31), which was subsequently validated in multiple, independent data sets (TCGA, n = 543; GSE65819, n = 87). Subsequently, we performed multivariate logistic regression-based training in a serum data set (GSE106817, n = 640), followed by its independent validation in three retrospective data sets (GSE31568, n = 85; GSE113486, n = 140; Czech Republic cohort, n = 192) and one prospective serum cohort (n = 95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in several other cancers (GSE31568 cohort, n = 369). RESULTS: The OCaMIR demonstrated a robust diagnostic accuracy in the stage I high-grade serous ovarian cancer patients in the discovery cohort (AUC = 0.99), which was consistently reproducible in both stage I (AUC = 0.96) and all stage patients (AUC = 0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC = 0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared with CA125 levels, even in stage I patients, and was more cost-effective, highlighting its potential role for screening and early detection of ovarian cancer. CONCLUSIONS: Small RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.
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$a Wang, Wei $u Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, P.R. China $u Department of Gynecology, Huzhou Maternity & Child Health Care Hospital, Huzhou, P.R. China
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$a Yu, Fan $u Department of Laboratory Medicine, West China Second Hospital, Sichuan University, Chengdu, P.R. China
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$a Zhou, Nianxin $u Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE & State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P.R. China
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$a Gao, Feng $u The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, P.R. China $1 https://orcid.org/0000000205005527
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$a Spillman, Monique $u Department of Obstetrics and Gynecology, Baylor University Medical Center, Dallas, Texas
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$a Moukova, Lucie $u Masaryk Memorial Cancer Institute, Brno, Czech Republic
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$a Slaby, Ondrej $u Central European Institute of Technology, Masaryk University, Brno, Czech Republic $u Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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$a Wang, Xin $u Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, P.R. China. ajgoel@coh.org Xin.Wang@cityu.edu.hk taotaovip2005@163.com $u Shenzhen Research Institute, City University of Hong Kong, Shenzhen, P.R. China
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