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Accession PRJCA018404
Title DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues
Relevance Medical
Data types Epigenomics
Organisms Homo sapiens
Description 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 new 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).
Sample scope Multiisolate
Release date 2023-07-16
Publication
PubMed ID Article title Journal name DOI Year
37709764 DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues Nature Communications 10.1038/s41467-023-41015-0 2023
Grants
Agency program Grant ID Grant title
National Natural Science Foundation of China (NSFC) 32270691
Zhejiang Province and National Health Commission of China WKJ-ZJ-2331
National Natural Science Foundation of China 82072950
Major Project of Hangzhou Science and Technology Bureau 20180417A01
Accessions in other database
Accession Database name
PRJNA970357 NCBI
Submitter Jiantao    Shi  (jtshi@sibcb.ac.cn)
Organization Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences
Submission date 2023-07-16

Project Data

Resource name Description
BioSample (791)  show -