亚型
DNA甲基化
表观遗传学
肺癌
癌症研究
甲基化
生物
恶性肿瘤
肿瘤科
医学
基因
遗传学
基因表达
程序设计语言
计算机科学
作者
Simon Heeke,Carl M. Gay,Marcos R. Estecio,Hai T. Tran,Benjamin B. Morris,Bingnan Zhang,Ximing Tang,Maria Gabriela Raso,Pedro Rocha,Siqi Lai,Edurne Arriola,Paul Hofman,Véronique Hofman,Prasad Kopparapu,Christine M. Lovly,Kyle Concannon,Luana Guimarães de Sousa,Whitney E. Lewis,Kimie Kondo,Xin Hu
出处
期刊:Cancer Cell
[Cell Press]
日期:2024-01-25
卷期号:42 (2): 225-237.e5
被引量:35
标识
DOI:10.1016/j.ccell.2024.01.001
摘要
Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.
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