OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer: A multi-cohort retrospective and prospective study

Raju Kandimalla; Wei Wang; Fan Yu; Nianxin Zhou; Feng Gao; Monique Spillman; Lucie Moukova; Ondrej Slaby; Bodour Salhia; Shengtao Zhou; Xin Wang; Ajay Goel

doi:10.1158/1078-0432.CCR-21-0267

OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer: A multi-cohort retrospective and prospective study

Raju Kandimalla, Wei Wang, Fan Yu, Nianxin Zhou, Feng Gao, Monique Spillman, Lucie Moukova, Ondrej Slaby, Bodour Salhia, Shengtao Zhou^*, Xin Wang^*, Ajay Goel^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

20 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	4277-4286
Journal	Clinical Cancer Research
Volume	27
Issue number	15
Online published	25 May 2021
DOIs	https://doi.org/10.1158/1078-0432.CCR-21-0267
Publication status	Published - Aug 2021

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@article{9486233e86ac489b8e56f90e351c1eb1,

title = "OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer: A multi-cohort retrospective and prospective study",

abstract = " 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.",

author = "Raju Kandimalla and Wei Wang and Fan Yu and Nianxin Zhou and Feng Gao and Monique Spillman and Lucie Moukova and Ondrej Slaby and Bodour Salhia and Shengtao Zhou and Xin Wang and Ajay Goel",

year = "2021",

month = aug,

doi = "10.1158/1078-0432.CCR-21-0267",

language = "English",

volume = "27",

pages = "4277--4286",

journal = "Clinical Cancer Research",

issn = "1078-0432",

publisher = "American Association for Cancer Research",

number = "15",

}

TY - JOUR

T1 - OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer

T2 - A multi-cohort retrospective and prospective study

AU - Kandimalla, Raju

AU - Wang, Wei

AU - Yu, Fan

AU - Zhou, Nianxin

AU - Gao, Feng

AU - Spillman, Monique

AU - Moukova, Lucie

AU - Slaby, Ondrej

AU - Salhia, Bodour

AU - Zhou, Shengtao

AU - Wang, Xin

AU - Goel, Ajay

PY - 2021/8

Y1 - 2021/8

N2 - 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.

AB - 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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DO - 10.1158/1078-0432.CCR-21-0267

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C2 - 34035068

SN - 1078-0432

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JO - Clinical Cancer Research

JF - Clinical Cancer Research

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ER -

OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer: A multi-cohort retrospective and prospective study

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GRF: Dissecting Age-dependent Molecular Heterogeneity of Colorectal Cancer and Establishing a Probabilistic Model for Robust Prediction of High-risk Young-onset Colorectal Cancer Patients

RIF-ExtU-Lead: Patient-Derived Preclinical Models for Translational Cancer Research: a Hong Kong-based Biotechnology Centre for Genomic Medicine

GRF: Multi-omic Molecular Subtyping of Ovarian High-grade Serous Carcinoma and Multidimensional Network Inference for Dissecting the Mesenchymal Subtype Specific Regulatory Mechanisms

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OCaMIR—A noninvasive, diagnostic signature for early-stage ovarian cancer: A multi-cohort retrospective and prospective study

Abstract

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GRF: Dissecting Age-dependent Molecular Heterogeneity of Colorectal Cancer and Establishing a Probabilistic Model for Robust Prediction of High-risk Young-onset Colorectal Cancer Patients

RIF-ExtU-Lead: Patient-Derived Preclinical Models for Translational Cancer Research: a Hong Kong-based Biotechnology Centre for Genomic Medicine

GRF: Multi-omic Molecular Subtyping of Ovarian High-grade Serous Carcinoma and Multidimensional Network Inference for Dissecting the Mesenchymal Subtype Specific Regulatory Mechanisms

Cite this