Abstract Rationale In 2022, lung cancer was the most frequently diagnosed cancer and the leading cause of cancer-related morbidity and mortality. Lung cancer screening (LCS) using low-dose computed tomography (LDCT) has increased early detection rates of lung cancer and reduced lung cancer mortality rates, but it is an imperfect screening tool with high false-positive rates. This leads to unnecessary tests and procedures, overtreatment, and avoidable patient distress. Extracellular vesicle-associated microRNAs (EV-miRs) have emerged as a promising, non-invasive method to detect early-stage non-small cell lung cancer (NSCLC). Several small-scale studies and early meta-analyses have shown that EV-miRs can help accurately identify early-stage NSCLC, and plasma-based microRNA signatures have been reported in LCS. This NCI Early Detection Research Network project (U01CA214195) aims to identify differentially expressed EV-miRs in early-stage NSCLC patients and controls who satisfy present LCS eligibility criteria. Methods Plasma samples were analyzed from 300 patients. The cancer samples were collected from 150 patients with histologically confirmed Stage 1 NSCLC. The control samples were collected from 150 patients who satisfied present LCS criteria but did not develop lung cancer, confirmed by benign histology or lack of nodule progression on LDCT for at least two years. Patients were matched based on age, smoking history, race, and gender. EV-RNA was isolated and differentially expressed microRNAs were identified using the Nanostring Human v3 microRNA assay with analysis via Nanostring nCounter. Results From this cohort, we identified 429 EV-miRs that were significantly different between cancer and control samples (FDR 0.05). Using DIANA-miRPath, we identified thirteen EV-miRs which correlated to pathways associated with cancer, cellular processes, metabolism and signaling pathways, including miR-16-5p, miR-93-5p, and miR-24-3p. Using an elastic-net-regularized logistic regression model, the ROC curve based on the top 50 features achieved an AUC of 0.91 (95% CI 0.88-0.94). The AUC increased to 0.95 (0.92-0.97) when clinical variables were included in the modeling. Conclusions In this study, we identify EV-miRs with high diagnostic yield in differentiating early-stage NSCLC from screen-detected controls. When utilized in conjunction with LDCT, these EV-miRs may reduce the false positive rates associated with LDCT alone, avoid the risks of unnecessary procedures and overtreatment, and provide a less stressful experience for patients being screened for lung cancer. There is an ongoing validation study with two separate cohorts to confirm the findings of this study. This abstract is funded by: NIH
Cloud et al. (Fri,) studied this question.