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DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

Author

Listed:
  • Shirong Zhang

    (Hangzhou First People’s Hospital
    Hangzhou First People’s Hospital)

  • Shutao He

    (Chinese Academy of Sciences
    Beijing Academy of Science and Technology)

  • Xin Zhu

    (Zhejiang Cancer Hospital)

  • Yunfei Wang

    (Zhejiang ShengTing Biotech Co. Ltd)

  • Qionghuan Xie

    (Zhejiang ShengTing Biotech Co. Ltd)

  • Xianrang Song

    (Shandong First Medical University and Shandong Academy of Medical Sciences)

  • Chunwei Xu

    (Nanjing University School of Medicine)

  • Wenxian Wang

    (Zhejiang Cancer Hospital)

  • Ligang Xing

    (Shandong First Medical University and Shandong Academy of Medical Sciences)

  • Chengqing Xia

    (Zhejiang ShengTing Biotech Co. Ltd)

  • Qian Wang

    (Jiangsu Province Hospital of Chinese Medicine)

  • Wenfeng Li

    (The First Affiliated Hospital of Wenzhou Medical University)

  • Xiaochen Zhang

    (The First Affiliated Hospital, Zhejiang University School of Medicine)

  • Jinming Yu

    (Shandong First Medical University and Shandong Academy of Medical Sciences)

  • Shenglin Ma

    (Hangzhou First People’s Hospital
    Hangzhou Cancer Hospital)

  • Jiantao Shi

    (Chinese Academy of Sciences)

  • Hongcang Gu

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3–9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

Suggested Citation

  • Shirong Zhang & Shutao He & Xin Zhu & Yunfei Wang & Qionghuan Xie & Xianrang Song & Chunwei Xu & Wenxian Wang & Ligang Xing & Chengqing Xia & Qian Wang & Wenfeng Li & Xiaochen Zhang & Jinming Yu & She, 2023. "DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41015-0
    DOI: 10.1038/s41467-023-41015-0
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    References listed on IDEAS

    as
    1. Ming Y. Lu & Tiffany Y. Chen & Drew F. K. Williamson & Melissa Zhao & Maha Shady & Jana Lipkova & Faisal Mahmood, 2021. "AI-based pathology predicts origins for cancers of unknown primary," Nature, Nature, vol. 594(7861), pages 106-110, June.
    2. Jianfeng Xu & Jiejun Shi & Xiaodong Cui & Ya Cui & Jingyi Jessica Li & Ajay Goel & Xi Chen & Jean-Pierre Issa & Jianzhong Su & Wei Li, 2021. "Cellular Heterogeneity–Adjusted cLonal Methylation (CHALM) improves prediction of gene expression," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Wei Jiao & Gurnit Atwal & Paz Polak & Rosa Karlic & Edwin Cuppen & Alexandra Danyi & Jeroen Ridder & Carla Herpen & Martijn P. Lolkema & Neeltje Steeghs & Gad Getz & Quaid D. Morris & Lincoln D. Stein, 2020. "A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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