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

Sep 24, 2026

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Identifying the primary site of metastatic cancer is essential for directing subsequent therapy, yet 3–9% of patients are diagnosed with cancer of unknown primary sites (CUP) after comprehensive workups. A universally accepted molecular test remains unavailable. The study reports FFPE-RRBS, using formalin-fixed paraffin-embedded tissues to build reduced representation bisulphite sequencing libraries, then evaluates 28 DNA methylation–based classifiers.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Clinical importance of identifying primary sites

Challenges: cancer of unknown primary sites (CUP)

Proposed method and evaluation

FFPE-RRBS library construction

Classifier design and performance assessment

| | | | | | --- | --- | --- | --- | | Article [https://doi.org/10.1038/s41467-023-41015-0](https://doi.org/10.1038/s41467-023-41015-0) | | | | | DNA methylation proﬁling to determine the primary sites of metastatic cancers using formalin-ﬁxed parafﬁn-embedded tissues | | | | | Received: 28 November 2022
Accepted: 18 August 2023 Check for updates | Shirong Zhang1,2,15, Shutao He3,4,15, Xin Zhu5,15, Yunfei Wang 6, Qionghuan Xie6, Xianrang Song7, Chunwei Xu8, Wenxian Wang5, Ligang Xing 7, Chengqing Xia6, Qian Wang9, Wenfeng Li10, Xiaochen Zhang11, Jinming Yu7, Shenglin Ma1,12 , Jiantao Shi 3 & Hongcang Gu 13,14
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, parafﬁnembedded 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 in81-93%ofCUP patients(n = 68). | | | | Cancer causes approximately 10 million deaths worldwide and more than 3 million in China alone1,2, of which 90% are due to metastasis3. For most patients with metastatic cancer, the tissue of origin can be determined by a comprehensive diagnostic workup, either in the | | early or metastatic stages4. However, the remaining 3–9% of cases have to be assigned as cancer of unknown primary sites, making this heterogeneous group of cancers the seventh or eighth most frequent malignancy and the fourth most common cause of cancer death5–8. | | 1Translational Medicine Research Center, Hangzhou First People’s Hospital, 310006 Hangzhou, Zhejiang Province, China. 2Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou First People’s Hospital, 310006 Hangzhou, Zhejiang Province, China. 3State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, 200031 Shanghai, China. 4Institute of Biotechnology and Health, Beijing Academy of Science and Technology, 100089 Beijing, China. 5Key Laboratory of Head&Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer Hospital,310022Hangzhou, Zhejiang Province, China. 6Zhejiang ShengTing Biotech Co. Ltd, 310018 Hangzhou, Zhejiang Province, China. 7Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117 Jinan, Shandong Province, China. 8Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, 210002 Nanjing, Jiangshu Province, China. 9Department of Respiratory Medicine, Afﬁliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029 Nanjing, Jiangshu Province, China. 10Department of Medical Oncology, The First Afﬁliated Hospital of Wenzhou Medical University, 325000 Wenzhou, Zhejiang Province, China. 11Department of Medical Oncology, The First Afﬁliated Hospital, Zhejiang University School of Medicine, 310006 Hangzhou, Zhejiang Province, China. 12Department of Oncology, Hangzhou Cancer Hospital, 310006 Hangzhou, Zhejiang Province, China. 13Anhui Province Key Laboratory of Medical Physics and Technology, Institute of

FAQs

Why is identifying the primary site of metastatic cancer important?

It provides a starting point for cancer diagnosis and is critical for guiding subsequent treatment decisions for metastatic cancer.

What problem motivates the study regarding CUP?

Even after comprehensive diagnostic workups, about 3–9% of metastatic patients remain with cancer of unknown primary sites, and a widely accepted molecular test is still lacking.

What approach does the study propose to predict the primary site?

It constructs FFPE-RRBS libraries from formalin-fixed paraffin-embedded tissues and evaluates multiple molecular classifiers based on DNA methylation scoring and machine learning.

Sep 24, 2026

Uploaded by Terk