MicroRNA Test Reduces False-Positive CT Screening Rate for Lung Cancer
Although recent data indicate that low-dose computed tomography (CT) reduces lung cancer mortality in high-risk patients, high false-positive rates, costs, and the potential for harm point out the need for biomarkers that can improve risk assessment. In a study reported in the Journal of Clinical Oncology, Sozzi et al evaluated a plasma microRNA signature classifier in detection of lung cancer in smokers. They found that use of low-dose CT and the classifier together resulted in a fivefold reduction in CT false-positives.
In the study, plasma samples from 939 participants in the randomized Multicenter Italian Lung Detection (MILD) trial, including 69 patients with lung cancer and 870 disease-free subjects, were analyzed using a quantitative reverse transcriptase polymerase chain reaction–based assay for a microRNA signature classifier. Of the 939 subjects, 652 were in the CT group and 287 in the observation group of the MILD trial. Diagnostic performance of the microRNA signature classifier was evaluated in blinded fashion using three prespecified risk groups based on expression ratio signatures of 24 microRNAs.
Subjects with lung cancer were older (mean, 61 vs 56 years; 19% vs 43% < 55 years, 26% vs 10% ≥ 65 years, P < .001 for trend), more likely to be male (81% vs 63%, P = .003), and had longer smoking history (40–49 years in 59% vs 38%, ≥ 50 years in 13% vs 5%, P < .001 for trend). There were no significant differences between groups in smoking status (80% and 79% current smokers) or number of cigarettes per day (eg, 20–29 in 45% and 51%, ≥ 40 in 19% vs 11%).
Cancer Detection
The microRNA intermediate- and high-risk classification had 87% sensitivity, 81% specificity, 27% positive predictive value, and 99% negative predictive value for lung cancer across both the CT and observation groups, including values of 88%, 80%, 31%, and 99% in the CT group and 82%, 83%, 16%, and 99% in the observation group. Low-dose CT had 79% sensitivity, 81% specificity, and a false-positive rate of 19.4% in lung cancer detection. The combination of the microRNA test and CT resulted in a fivefold reduction of the CT false-positive rate to 3.7% with a decrease in sensitivity to 69%. With positive outcome on at least one test, sensitivity was 98% and specificity was 65%, and the microRNA test detected 82% of lung cancers in the observation group. The microRNA test risk groups were not significantly associated with tumor stage (P = .40), histologic subtypes (P = .4485), or adenocarcinoma vs squamous cell carcinoma (P = .759).
Association With Survival
MicroRNA signature classifier risk groups were significantly associated with survival (χ21 = 49.53, P < .001); 2- and 3-year survival rates were 100%, 98%, and 87%, and 100%, 97%, and 77% for high, intermediate, and low microRNA test risk. The test had 95% sensitivity, 81% specificity, 10% positive predictive value, and 100% negative predictive value for lung cancer death.
The investigators concluded, “This large validation study indicates that [microRNA signature classifier] has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of [low-dose] CT, thus improving the efficacy of lung cancer screening.”
Ugo Pastorino, MD, of Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, is the corresponding author for the Journal of Clinical Oncology article.
The study was supported by grants from the Italian Association for Cancer Research, Italian Ministry of Health, and National Cancer Institute and by Gensignia. Gabriella Sozzi, PhD, Mattia Boeri, PhD, and Dr. Pastorino are coinventors for two patent applications regarding the microRNA signature disclosed in this article.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
