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Original Investigation
March 5, 2020

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

Author Affiliations
  • 1Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
  • 2Junior Research Group of Human Genetics, Saarland University, Homburg, Germany
  • 3Department of Medicine II, Saarland University Medical Center, Homburg, Germany
  • 4Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
  • 5Institute for Technology Assessment, Massachusetts General Hospital, Boston
  • 6Endometriosis Center, ViDia Clinics, Karlsruhe, Germany
  • 7Department of Internal Medicine, Heidelberg University, Heidelberg, Germany
  • 8Institute of Human Genetics, Saarland University, Homburg, Germany
  • 9Hertie Institute for Clinical Brain Research, Center of Neurology, Department of Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany
  • 10German Center for Neurodegenerative Diseases, Tübingen, Germany
  • 11Department of Neurology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
  • 12Department of Internal Medicine V: Pulmonology, Allergology, Intensive Care Medicine, Saarland University Medical Center, Saarland University, Homburg, Germany
  • 13Department of Medicine, Pulmonary and Critical Care Medicine, Philipps-University of Marberg, Member of the German Centre for Lung Research (DZL), Marburg, Germany
  • 14Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany
  • 15Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany
  • 16Center for Geriatric Medicine, University Hospital Tübingen, Tübingen, Germany
  • 17Department of Pediatric Cardiology, Saarland University, Saarbrücken, Germany
  • 18Schwerpunktpraxis Hämatologie und Onkologie, Kaiserslautern, Germany
  • 19Department of Gynecology, University Hospital Würzburg, Würzburg, Germany
  • 20Department of Anaesthesiology, Intensive Care and Pain Therapy, Saarland University Medical Center and Faculty of Medicine, Saarland University, Homburg, Germany
  • 21Center for Bioinformatics, Saarland University, Saarbrücken, Germany
  • 22Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg
  • 23Department of Cardiothoracic Surgery, Völklingen Heart Centre, Völklingen, Germany
  • 24Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California
JAMA Oncol. 2020;6(5):714-723. doi:10.1001/jamaoncol.2020.0001
Key Points

Question  Can the detection of lung cancer in symptomatic patients be improved by using circulating microRNAs as biomarkers?

Findings  This cohort study used genome-wide microRNA profiles from the blood samples of 3046 individuals to identify patients with lung cancer with 91.4% accuracy, 82.8% sensitivity, and 93.5% specificity.

Meaning  The findings of this study suggest that the identified patterns of circulating microRNAs may enable them to be used as biomarkers in a liquid biopsy to complement imaging tests, sputum cytology, and biopsies.

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.

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