24O - Clinical utility of a plasma-based microRNA signature classifier within computed tomography lung cancer screening

Date 28 March 2014
Event ELCC 2014
Session Proffered papers 3 - Screening, staging and epidemiology
Topics Cancer Etiology, Epidemiology, Prevention
Lung and other Thoracic Tumours
Translational Research
Presenter Ugo Pastorino
Citation Journal of Thoracic Oncology (2014) 9 (Supplement 9): S7-S52. 10.1097/JTO.0000000000000131
Authors U. Pastorino1, M. Boeri2, G. Sozzi2
  • 1Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 - Milan/IT
  • 2Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 - Milan/IT




Recent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung-cancer mortality in high risk subjects. However, high false positive rates, costs and potential harms highlight the need for complementary biomarkers. MicroRNAs (miRNAs) are tissue and disease specific molecules, actively released by cells in the circulatory system, which are associated with protein complexes and/or packaged in exosomes and microvesicles. Circulating miRNAs are rather stable and easily detectable in body fluids suggesting the possibility of using miRNAs as a new promising class of biomarkers. We previously reported that miRNA profiling in plasma samples of disease-free smokers enrolled in two independent spiral-CT screening trials, resulted in the generation of miRNA signatures with strong predictive, diagnostic, and prognostic potential (Boeri et al. PNAS 2011).


The diagnostic performance of a non-invasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicentre Italian Lung Detection (MILD) trial. Plasma samples from 939 subjects including 69 lung cancer patients and 870 disease-free individuals (652 LDCT arm; 287 observation arm) were analyzed using a qRT-PCR based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study using pre-specified risk groups.


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 subjects, MSC had a negative predictive value of 99% and 99.86% for detection and death-by-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 5-fold reduction of LDCT false positive rate to 3.7%. MSC risk groups were significantly associated with survival (=49.53, p<0.0001).


This large validation study indicates that MSC has predictive, diagnostic and prognostic value and could reduce the false positive rate of LDCT improving the efficacy of lung cancer screening.


All authors have declared no conflicts of interest.