Abstract 3261: Gene methylation biomarkers in sputum as a classifier for lung cancer risk
Authors: Maria A. Picchi, Shuguang Leng, Guodong Wu, Donna M. Klinge, Cynthia L. Thomas, Elia Casas, Christine A. Stidley, Sandra J. Lee, Seena Aisner, Jill M. Siegfried, Suresh Ramalingam, Fadlo R. Khuri, Daniel D. Karp, Steven A. Belinsky
Journal: Cancer Research (2017)

Abstract Lung cancer is the leading cause of cancer death primarily because it is often diagnosed at an advanced stage. Results from the National Lung Screening Trial (NLST) showed that CT screening can reduce lung cancer mortality by 20%, but the high number of false positives and need for additional testing suggest that better risk classification of smokers prior to screening could save more lives with less cost and improved efficiency. Furthermore, eligibility criteria for NLST were restricted to smokers ages of 55-74 with &amp;gt;30 pack-years of smoking and &amp;lt; 15y since smoking cessation, which captures only ~40% of incident lung cancer cases. Molecular biomarkers could augment current risk stratification used to select smokers for screening. We have shown that gene specific promoter hypermethylation detected in sputum provides an assessment of field cancerization within the lungs of smokers that in turn predicts lung cancer. The current study addressed whether our validated 8-gene methylation panel could be extended to improve the existing risk prediction model used to recommend people for a CT screen by evaluating methylation in 487 resected Stage I lung cancer patients from the ECOG-ACRIN5597 trial (field of injury remains), 1378 current and former smokers from the Lovelace Smokers cohort (LSC) and 718 current and former smokers from the PLuSS cohort. Our initial analysis was restricted to individuals from each cohort who met NLST criteria for CT screening (n=371 ECOG-ACRIN, n=466 LSC and n=597 PLuSS). The methylation prevalence of all 8 genes was significantly increased in resected lung cancer patients compared to cancer-free smokers (odds ratios 1.6 to 8.9). The area under the receiver operator characteristic curve (AUC) was used to evaluate classification accuracy of different logistic regression models. Classification accuracy for clinical risk factors alone was 74-76%, methylation alone, 82-86%; and clinical risk factors plus methylation, 87-90% (p&amp;lt;10-9 - 10-16). Setting the sensitivity at 95% improved specificity from 25% to 54% with both methylation and clinical factors in the model. Assessment of the performance of the gene methylation panel in all of the cancer patients and smokers did not reduce classification accuracy or increase specificity. Implementation of gene methylation biomarkers for screening could be a paradigm shift for lung cancer management by providing a much improved risk assessment model that will allow for expanding the number of smokers considered for screening, decreasing by one-third the actual number referred, and reducing mortality through increasing the CT positive predictive value. (Supported largely by R01 CA095568 and in part P30 CA118100) Citation Format: Maria A. Picchi, Shuguang Leng, Guodong Wu, Donna M. Klinge, Cynthia L. Thomas, Elia Casas, Christine A. Stidley, Sandra J. Lee, Seena Aisner, Jill M. Siegfried, Suresh Ramalingam, Fadlo R. Khuri, Daniel D. Karp, Steven A. Belinsky. Gene methylation biomarkers in sputum as a classifier for lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3261. doi:10.1158/1538-7445.AM2017-3261