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Blood-based DNA methylation profiling for the detection of ovarian cancer

  • Author Footnotes
    1 The first two authors contributed equally to the work and should be regarded as co-first authors.
    Ning Li
    Footnotes
    1 The first two authors contributed equally to the work and should be regarded as co-first authors.
    Affiliations
    Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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  • Author Footnotes
    1 The first two authors contributed equally to the work and should be regarded as co-first authors.
    Xin Zhu
    Footnotes
    1 The first two authors contributed equally to the work and should be regarded as co-first authors.
    Affiliations
    Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China

    Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China
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  • Weiqi Nian
    Affiliations
    Chongqing University Cancer Hospital, Chongqing 400030, China
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  • Yifan Li
    Affiliations
    Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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  • Yangchun Sun
    Affiliations
    Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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  • Guangwen Yuan
    Affiliations
    Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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  • Zhenjing Zhang
    Affiliations
    Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China
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  • Wenqing Yang
    Affiliations
    Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China

    Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China
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  • Jiayue Xu
    Affiliations
    Burning Rock Biotech, Guangzhou 510300, China
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  • Analyn Lizaso
    Affiliations
    Burning Rock Biotech, Guangzhou 510300, China
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  • Bingsi Li
    Affiliations
    Burning Rock Biotech, Guangzhou 510300, China
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  • Zhihong Zhang
    Affiliations
    Burning Rock Biotech, Guangzhou 510300, China
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  • Lingying Wu
    Correspondence
    Correspondence to: L. Wu, Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
    Affiliations
    Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
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  • Yu Zhang
    Correspondence
    Correspondence to: Y. Zhang, Department of Gynecology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan Province 410008, China.
    Affiliations
    Department of Gynecology, Xiangya Hospital, Central South University, Changsha 410008, China

    Gynecological Oncology Research and Engineering Center of Hunan Province, Changsha 410008, China

    National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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  • Author Footnotes
    1 The first two authors contributed equally to the work and should be regarded as co-first authors.
Published:September 09, 2022DOI:https://doi.org/10.1016/j.ygyno.2022.07.008

      Highlights

      • Blood-based DNA methylation profiling is feasible in identifying patients with malignant or non-malignant ovarian tumor
      • Malignant ovarian tumor samples had distinct DNA methylation profile than normal/benign ovarian tissues
      • Machine learning classifier had a positive predictive value of 93.7% and negative predictive value of 86.8%

      Abstract

      Objectives

      Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer.

      Methods

      Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184).

      Results

      Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%.

      Conclusions

      Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.

      Keywords

      Abbreviations:

      AUC (area under the curve), CA125 (cancer antigen 125), cfDNA (cell-free DNA), DMR (differentially methylated regions), ROC (receiver operating characteristics)
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