Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients

Abstract

Importance: The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures.

Objective: To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants.

Design, setting, and participants: This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non-small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019.

Main outcomes and measures: Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer.

Results: A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%).

Conclusions and relevance: The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests.

Conflict of interest statement

Conflict of Interest Disclosures: Mr Kahraman reported receiving personal fees from Hummingbird Diagnostics (HBDx) during the conduct of the study outside the submitted work. Dr Backes reported receiving personal fees from HBDx during the conduct of the study. Dr V. Keller reported receiving personal fees from HBDx during the conduct of the study. Dr Maetzler reported receiving grants from Neuroallianz during the conduct of the study; grants from the European Union, Janssen, and the Michael J Fox Foundation; grants and personal fees from Lundbeck; and personal fees from Abbvie outside the submitted work. Dr Fähndrich reported receiving nonfinancial support and fees for lectures from Grifols, CSL Behring, CSL Behring, AstraZeneca, Novartis, and BerlinChemie during the conduct of the study. Dr Vogelmeier reported receiving grants from the German Ministry of Research (BMBF), AstraZeneca, GlaxoSmithKline, Grifols, Novartis, Bayer-Schering, Merck Sharp & Dohme, and Pfizer during the conduct of the study; and personal fees from AstraZeneca, CSL Behring, Chiesi, GlaxoSmithKline, Grifols, Menarini, Mundipharma, and Novartis outside the submitted work. Dr Stehle reported receiving personal fees from Institut für medizinische Dokumentation, Gutachtenerstellung, Gesundheitsförderung und Qualitätssicherung, Roche, Novartis, MSD, and Boehringer Ingelheim outside the submitted work. Dr Meiser reported receiving personal fees from Pall Medical, Dahlhausen, Medtronics, and Sedana Medical outside the submitted work. Dr Katus reported receiving personal fees from Daiichi, AstraZeneca, and Bayer Vital outside the submitted work. Dr Balling reported being cofounder and holding shares in MEGENO, SARL, Information Technology for Translational Medicine SARL, and Theracule, SARL outside the submitted work. Dr Meder reported receiving grants from University of Heidelberg, the BMBF, German Centre for Cardiovascular Research, and Else Kröner Fresenius Stiftung (Excellence Fellowship) during the conduct of the study. Dr Kruger reported receiving grants from Fonds National de Recherche during the conduct of the study. Dr Bals reported receiving personal fees from AstraZeneca; grants from Boehringer Ingelheim, BMBF, Competence Network Asthma and COPD (ASCONET), and Schwiete Stiftung; and grants and personal fees from GlaxoSmithKline, Novartis, and CSL Behring outside the submitted work. Dr Meese reported receiving grants from German Cancer Aid during the conduct of the study. Dr A. Keller reported receiving grants and personal fees from HBDx during the conduct of the study and had patent US18445209P issued and licensed. No other disclosures were reported.

Figures

Figure.. Quantile Normalized Expression Intensity Distribution of 4 MicroRNAs in the 4 Test Groups

The boxes span the first to the third quartile with the line inside the box representing the median value. The whiskers show the minimum and maximum values or values up to 1.5 times the interquartile range below or above the first or third quartile if outliers are present (shown as separate dots). Con indicates control participants; LCa, lung cancer; NTLD, nontumor lung diseases; OD, other diseases.