Abstract 2209: Lung cancer risk prediction using DNA methylation markers

Abstract

Abstract There is an urgent need to improve lung cancer risk assessment as current screening criteria miss a large proportion of cases and result in a high rate of false positives on low-dose CT screening. A fixed effect meta-analyses of 4 epigenome-wide association studies of lung cancer revealed differential DNA methylation at 16 CpG sites (FDR<0.05). The current study aimed to evaluate the extent to which such methylation markers can improve upon smoking-based risk-discrimination among ever smokers. We used data on 662 ever smoking lung cancer case-control pairs from 4 individual prospective cohorts that measured DNA methylation using the Illumina Infinium HumanMethylation450 BeadChip in peripheral blood samples before diagnosis: The Italian part of the European Prospective Investigation into Cancer (EPIC) cohort, the Melbourne Collaborative Cohort (MCCS), the Norwegian Women and Cancer cohort (NOWAC) and the Northern Sweden Health and Disease Study (NSHDS). We adopted a training-testing design where the training was performed on MCCS and NSHDS (N=511 case-control pairs), and the testing on EPIC-Italy and NOWAC (N=151 case-control pairs). Logistic regressions with lasso penalties were performed in the training set to select the best set of CpGs jointly predicting lung cancer. A methylation score was trained by fitting a logistic regression model including the selected CpGs in the training set. A baseline score based on self-reported smoking information (duration, cigarettes/day for current smokers and time since smoking cessation for former smokers) was also developed. The discriminative performances of both scores, as well as integrated model where both smoking and methylation info was incorporated, were assessed by the AUC under the ROC curves in the validation set. The methylation score based on the 9 selected CpG sites yielded an AUC of 0.78 [0.73-0.84] compared to 0.73 [0.68-0.79] for the baseline-smoking score. The model integrating both scores yielded an AUC of 0.79 [0.73-0.84], a notable 0.06-increase in AUC from using the smoking score alone (P=0.008 for difference in AUC). In conclusion, specific methylation biomarkers have a strong potential to improve lung cancer risk assessment and current USPSTF criteria for CT-screening. During the conference, we will present absolute risk estimates based on the integrated risk prediction model. Citation Format: Florence Guida, Therese H. Nøst, Caroline Relton, Paul Brennan, Torkjel M. Sandanger, Marc Chadeau-Hyam, Mattias Johansson. Lung cancer risk prediction using DNA methylation markers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2209.