Gene methylation biomarkers in sputum as a classifier for lung cancer risk
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Shuguang Leng1, Guodong Wu1, Donna M. Klinge1, Cynthia L. Thomas1, Elia Casas1, Maria A. Picchi1, Christine A. Stidley2, Sandra J. Lee3, Seena Aisner4, Jill M. Siegfried5, Suresh Ramalingam6, Fadlo R. Khuri6, Daniel D. Karp7 and Steven A. Belinsky1
1Lung Cancer Program, Lovelace Respiratory Research Institute, Albuquerque, NM, USA
2Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
3Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
4Rutgers New Jersey Medical School, Newark, NJ, USA
5Department of Pharmacology, University of Minnesota, Minneapolis, MN, USA
6Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, USA
7MD Anderson Cancer Center, Houston, TX, USA
Steven A. Belinsky, email: [email protected]
Keywords: gene methylation, lung cancer risk, biomarker, CT screening
Received: March 14, 2017 Accepted: June 05, 2017 Published: July 15, 2017
CT screening for lung cancer reduces mortality, but will cost Medicare ~2 billion dollars due in part to high false positive rates. Molecular biomarkers could augment current risk stratification used to select smokers for screening. Gene methylation in sputum reflects lung field cancerization that remains in lung cancer patients post-resection. This population was used in conjunction with cancer-free smokers to evaluate classification accuracy of a validated eight-gene methylation panel in sputum for cancer risk. Sputum from resected lung cancer patients (n=487) and smokers from Lovelace (n=1380) and PLuSS (n=718) cohorts was studied for methylation of an 8-gene panel. Area under a receiver operating characteristic curve was calculated to assess the prediction performance in logistic regressions with different sets of variables. The prevalence for methylation of all genes was significantly increased in the ECOG-ACRIN patients compared to cancer-free smokers as evident by elevated odds ratios that ranged from 1.6 to 8.9. The gene methylation panel showed lung cancer prediction accuracy of 82–86% and with addition of clinical variables improved to 87–90%. With sensitivity at 95%, specificity increased from 25% to 54% comparing clinical variables alone to their inclusion with methylation. The addition of methylation biomarkers to clinical variables would reduce false positive screens by ruling out one-third of smokers eligible for CT screening and could increase cancer detection rates through expanding risk assessment criteria.
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