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A4697 - Further Interrogation of an Exhaled MicroRNA Panel for Lung Cancer Risk Assessment
Author Block: S. D. Spivack1, M. Shi1, W. Han2, M. Aldabagh1, D. Patel1, J. B. Dobkin3, C. Shah1, D. Hosgood4, K. Pradhan5, K. Ye5; 1Pulmonary Medicine, Albert Einstein College of Med, Bronx, NY, United States, 2Immunotoxicology, Wadsworth Center, NYSDOH, Albany, NY, United States, 3Pulmonary Medicine, Montefiore Medical Center, Bronx, NY, United States, 4DEPH-Epidemiology, Albert Einstein College of Med, Bronx, NY, United States, 5DEPH-Biostatistics, Albert Einstein College of Med, Bronx, NY, United States.
Background: There is a need for non-invasive airway-based biomarkers in lung carcinogenesis for both risk assessment of the ex-smoker, and earlier diagnosis. Exhaled breath condensate (EBC) contains airway lining fluid molecules, including nucleic acids, presumably in part from epithelial cellular origins. MicroRNAs play important regulatory roles in many processes, including carcinogenesis. Here we further develop and begin validation of the detection of microRNAs in EBC from lung cancer patients and controls. Methods: Exhaled breath condensate (EBC) was collected non-invasively, using a handheld device in ambulatory subjects. MicroRNA expression profiling using RNA-specific RT-qPCR was performed in EBC. We generated a 39 miR panel based on literature-derived microRNAs, combined with a lung tissue-based discovery effort we have performed using microRNA-seq on lung tumors and surrounding non-tumor tissue. The qPCR primers were designed using our previously published RNA-specific realtime RT-PCR technique. All samples were run twice with positive and negative controls. Results: We constructed a panel of 35 microRNAs applied to EBC from a small clinical set of 44 early stage (I and II) cases and 45 controls (Group D, 89 total individuals). The qualitative RT-PCR data (individual miR present or absent in a given EBC sample, by one of two replicate call criteria, P1) was analyzed in two different ways, first by logistic regression, and then by random forests (RF). Both analyses revealed a small set of miRs that carry overall case-control discriminant capacity [RF AUC 0.71=>0.83 (P1 criterion); 0.72=>0.81 (P2, both replicates positive criterion)] over the pre-selected clinical factors (age, smoking status, COPD presence) alone. In a second replicate small clinical case-control series of 41 early stage cases and 47 controls, qualitative exhaled microRNA data for Group E, a small set of miRs improved the discriminant AUC of clinical factors (AUC 0.77=>0.81 (P1, criterion); 0.77=>0.82 (P2 criterion). Combining both sub-studies (groups D+E), case-control discrimination improved from clinical factors (age, smoking status, COPD presence) alone versus clinical factors plus exhaled microRNAs (adenocarcinoma only) by 3-6%; similar incremental prediction (3-8%) was found for all NSCLC histologies combined. Quantitative RT-PCR was not sufficiently robust to further analyze. Conclusion: This new exhaled biomarker platform can yield case-control discriminant microRNA sets. Once further distilled and validated, our goal is to apply this non-invasive biomarker approach to prospective cohorts for non-invasive lung cancer risk assessment, in order to better select higher risk individuals to undergo effective CT screening.