亚型
仿形(计算机编程)
生物
癌症研究
计算生物学
计算机科学
操作系统
程序设计语言
作者
Francesca Chemi,Simon P. Pearce,Alexandra Clipson,Steven M. Hill,Alicia‐Marie Conway,Sophie Richardson,Katarzyna Kamieniecka,Rebecca Caeser,Daniel J. White,Sumitra Mohan,Victoria Foy,Kathryn Simpson,Melanie Galvin,Kristopher K. Frese,Lynsey Priest,Jacklynn V. Egger,Alastair Kerr,Pierre P. Massion,John T. Poirier,Gerard Brady
出处
期刊:Nature cancer
[Nature Portfolio]
日期:2022-08-08
卷期号:3 (10): 1260-1270
被引量:89
标识
DOI:10.1038/s43018-022-00415-9
摘要
Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.
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