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

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, Shenglin Ma (), Jiantao Shi () and Hongcang Gu ()
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Shirong Zhang: Hangzhou First People’s Hospital
Shutao He: Chinese Academy of Sciences
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
Jiantao Shi: Chinese Academy of Sciences
Hongcang Gu: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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).

Date: 2023
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DOI: 10.1038/s41467-023-41015-0

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