Fragmentomics features of ovarian cancer.

Journal: International journal of cancer
PMID: 38769763

Abstract

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.

Authors

  • Xiaopei Chao
    Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Beijing, China.
  • Zhentian Kai
    Department of Bioinformatics, Zhejiang Shaoxing Topgen Biomedical Technology CO., LTD, Shanghai, China.
  • Huanwen Wu
    Department of Pathology, Peking Union Medical College Hospital, Beijing, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Xiaojing Chen
    Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
  • Haiqi Su
    Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Beijing, China.
  • Xiao Shang
    Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Beijing, China.
  • Ruijue Lin
    Department of Technology, Zhejiang Topgen Clinical Laboratory Co., LTD., Huzhou, China.
  • Lisha Huang
    Department of Bioinformatics, Zhejiang Shaoxing Topgen Biomedical Technology CO., LTD, Shanghai, China.
  • Hongsheng He
  • Jinghe Lang
    Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Beijing, China.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.

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