DNA甲基化
腺癌
德纳姆
生物
甲基化
克拉斯
肺癌
癌症研究
免疫系统
癌症
免疫学
DNA
肿瘤科
基因
遗传学
医学
结直肠癌
基因表达
作者
Kayla Guidry,Varshini Vasudevaraja,Kristen E. Labbe,Hussein M.H. Mohamed,Jonathan Serrano,Brett W Guidry,Michael Delorenzo,Hua Zhang,Jiehui Deng,Soumyadip Sahu,Christina Almonte,Andre L. Moreira,Aristotelis Tsirigos,Thales Papagiannakopoulos,Harvey I. Pass,Matija Snuderl,Kwok-Kin Wong
标识
DOI:10.1158/1078-0432.ccr-22-0391
摘要
Abstract Purpose: Lung adenocarcinoma (LUAD) is a clinically heterogeneous disease, which is highlighted by the unpredictable recurrence in low-stage tumors and highly variable responses observed in patients treated with immunotherapies, which cannot be explained by mutational profiles. DNA methylation–based classification and understanding of microenviromental heterogeneity may allow stratification into clinically relevant molecular subtypes of LUADs. Experimental Design: We characterize the genome-wide DNA methylation landscape of 88 resected LUAD tumors. Exome sequencing focusing on a panel of cancer-related genes was used to genotype these adenocarcinoma samples. Bioinformatic and statistical tools, the immune cell composition, DNA methylation age (DNAm age), and DNA methylation clustering were used to identify clinically relevant subgroups. Results: Deconvolution of DNA methylation data identified immunologically hot and cold subsets of LUADs. In addition, concurrent factors were analyzed that could affect the immune microenvironment, such as smoking history, ethnicity, or presence of KRAS or TP53 mutations. When the DNAm age was calculated, a lower DNAm age was correlated with the presence of a set of oncogenic drivers, poor overall survival, and specific immune cell populations. Unsupervised DNA methylation clustering identified six molecular subgroups of LUAD tumors with distinct clinical and microenvironmental characteristics. Conclusions: Our results demonstrate that DNA methylation signatures can stratify LUAD into clinically relevant subtypes, and thus such classification of LUAD at the time of resection may lead to better methods in predicting tumor recurrence and therapy responses.
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