A panel sequencing dataset of peripheral blood gene variations in pan-cancer
Yanxia Liu, Jie Liu, Shouwei Zhang, Jinyue Wang, Zhihong Sun, Huaibo Sun, Ying Yang, Guangmin Zheng, Yu Huang, Meng Li, Zhaojun Zhang, Jingfa Xiao, Changqing Zeng, Chengming Sun, Hongzhu Qu, Xiangdong Fang

TL;DR
This study creates a comprehensive database of gene variations in blood from over 16,000 cancer patients across 27 cancer types to support cancer research and personalized treatment.
Contribution
The first pan-cancer cfDNA database integrating somatic and germline variants with comparative analyses across cancer types.
Findings
Panel sequencing identified 496 germline variants and 11,232 somatic mutations across 1,115 cancer-related genes.
The CPGV database provides a valuable resource for analyzing and visualizing pan-cancer cfDNA mutations.
The dataset spans 27 cancer types, enabling comparative mutation analysis for diverse cancers.
Abstract
Circulating cell-free DNA (cfDNA) in the peripheral blood is a promising biomarker for cancer diagnosis and prognosis. Somatic mutations identified in cancers have been used to detect therapeutic targets for clinical transformation and individualize drug selection, while germline variants can predict a patient’s risk of developing cancer and drug sensitivity. However, no platform has been developed to analyze, calculate, integrate, and friendly visualize these pan-cancer cfDNA mutations deeply. In this work, we performed panel sequencing encompassing 1,115 cancer-related genes across 16,659 cancer patients, spanning 27 cancer types. We detected 496 germline variants in leukocytes and 11,232 somatic mutations in the cfDNA of all patients. CPGV (Cancer Peripheral blood Gene Variations), a database constructed from this dataset, is the first pan-cancer cfDNA database that encompasses…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsCancer Genomics and Diagnostics · Single-cell and spatial transcriptomics · Epigenetics and DNA Methylation
