Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening : a correlative MILD trial study

Purpose: Recent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients. However, high false-positive rates, costs, and potential harms highlight the need for complementary biomarkers. The diagnostic performance of a noninvasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicenter Italian Lung Detection (MILD) trial. Patients and Methods: Plasma samples from 939 participants, including 69 patients with lung cancer and 870 disease-free individuals (n = 652, LDCT arm; n = 287, observation arm) were analyzed by using a quantitative reverse transcriptase polymerase chain reaction-based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study that used prespecified risk groups. Results: The diagnostic performance of MSC for lung cancer detection was 87% for sensitivity and 81% for specificity across both arms, and 88% and 80%, respectively, in the LDCT arm. For all patients, MSC had a negative predictive value of 99% and 99.86% for detection and death as a result of disease, respectively. LDCT had sensitivity of 79% and specificity of 81% with a false-positive rate of 19.4%. Diagnostic performance of MSC was confirmed by time dependency analysis. Combination of both MSC and LDCT resulted in a five-fold reduction of LDCT false-positive rate to 3.7%. MSC risk groups were significantly associated with survival (\u3c712 = 49.53; P < .001). Conclusion: This large validation study indicates that MSC has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT, thus improving the efficacy of lung cancer screening