Abstract 1205: Leveraging genome-wide methylation data to understand lung cancer risk in heavy smokers
Authors: Laurie Grieshober, Jincheng Shen, Stefan Graw, Matt J. Barnett, Mark D. Thornquist, Gary E. Goodman, Chu Chen, Devin C. Koestler, Carmen J. Marsit, Jennifer A. Doherty
Journal: Cancer Research (2020)

Abstract Epigenome-wide association studies (EWAS) have been used to identify variation in methylation of cytosine-phosphate-guanine (CpG) sites associated with many phenotypes, including lung cancer. A recent meta-EWAS revealed that associations between CpG methylation and lung cancer risk differ by smoking status. Also, smoking status, intensity, and time since cessation are associated with changes in peripheral blood DNA methylation across the genome. We sought to address whether genome-wide methylation differences are associated with lung cancer risk in 316 matched lung cancer case and control pairs from the Beta Carotene and Retinol Efficacy Trial (CARET) of heavy smokers (≥20 pack years). Case-control pairs were matched on age (±5 years), sex, race/ethnicity, enrollment year (±2 years), smoking status (ever/never), occupational asbestos exposure (yes/no), and follow-up time. Genome-wide DNA methylation was assayed on the Illumina EPIC array in whole blood samples collected on average 4.1 years prior to diagnosis in cases. To improve scalability for testing, we mapped 866,326 normalized and processed EPIC array CpGs to 26,994 gene regions using the EPIC array annotation. We performed region-based variance component tests for associations between each region and lung cancer risk. We modeled associations as random effects such that changes reflecting both hyper- and hypo-methylation across different CpG sites in a gene region cumulatively contribute to the overall variance component test. The test assesses whether the variance associated with the regression coefficient for each CpG in the region is equal to zero. Since this method does not have a conditional adaptation for matched study designs, our region-based models included adjustment for age, sex, race/ethnicity, enrollment year, smoking status, and occupational asbestos exposure, as well as pack years and the first two principal components estimated from the genome-wide methylation data. Our preliminary results include 66 genes (12 in adenocarcinoma, 33 in squamous, 21 in small cell) with permutation-based P&amp;lt;0.001. The top statistically significant associations (all P&amp;lt;0.0001) were TMEM63C in adenocarcinoma, MCC in squamous cell carcinoma, and GRM5-AS1, UBE2QL1, and TNNI1 in small cell lung cancer. GWAS studies have shown associations between single nucleotide polymorphisms (SNPs) in TNNI1 and lung function, and also SNPs from the TMEM and UBE2 gene families with lung cancer risk. Our findings suggest that region-based testing may be applied for association studies of genome-wide methylation where site-specific targeting of methylation markers may be underpowered to individually associate with risk. Citation Format: Laurie Grieshober, Jincheng Shen, Stefan Graw, Matt J. Barnett, Mark D. Thornquist, Gary E. Goodman, Chu Chen, Devin C. Koestler, Carmen J. Marsit, Jennifer A. Doherty. Leveraging genome-wide methylation data to understand lung cancer risk in heavy smokers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1205.