Initial development and testing of an exhaled microRNA detection strategy for lung cancer case-control discrimination.

For detecting field carcinogenesis non-invasively, early technical development and case-control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically "fingerprinted", using paired EBC, upper and lower airway donor sample sets. A clinic-based case-control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case-control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1-2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.