Tobias Fehlmann1, Mustafa Kahraman1, Nicole Ludwig2, Christina Backes1, Valentina Galata1, Verena Keller3, Lars Geffers4, Nathaniel Mercaldo5, Daniela Hornung6, Tanja Weis7, Elham Kayvanpour7, Masood Abu-Halima8, Christian Deuschle9,10, Claudia Schulte9,10, Ulrike Suenkel9,10, Anna-Katharina von Thaler9,10, Walter Maetzler11, Christian Herr12, Sebastian Fähndrich12, Claus Vogelmeier13, Pedro Guimaraes1, Anne Hecksteden14, Tim Meyer14, Florian Metzger15,16, Caroline Diener8, Stephanie Deutscher8, Hashim Abdul-Khaliq17, Ingo Stehle18, Sebastian Haeusler19, Andreas Meiser20, Heinrich V Groesdonk20, Thomas Volk20, Hans-Peter Lenhof21, Hugo Katus7, Rudi Balling4, Benjamin Meder7, Rejko Kruger4,22, Hanno Huwer23, Robert Bals12, Eckart Meese8, Andreas Keller1,21,24. 1. Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany. 2. Junior Research Group of Human Genetics, Saarland University, Homburg, Germany. 3. Department of Medicine II, Saarland University Medical Center, Homburg, Germany. 4. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. 5. Institute for Technology Assessment, Massachusetts General Hospital, Boston. 6. Endometriosis Center, ViDia Clinics, Karlsruhe, Germany. 7. Department of Internal Medicine, Heidelberg University, Heidelberg, Germany. 8. Institute of Human Genetics, Saarland University, Homburg, Germany. 9. Hertie Institute for Clinical Brain Research, Center of Neurology, Department of Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany. 10. German Center for Neurodegenerative Diseases, Tübingen, Germany. 11. Department of Neurology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany. 12. Department of Internal Medicine V: Pulmonology, Allergology, Intensive Care Medicine, Saarland University Medical Center, Saarland University, Homburg, Germany. 13. Department of Medicine, Pulmonary and Critical Care Medicine, Philipps-University of Marberg, Member of the German Centre for Lung Research (DZL), Marburg, Germany. 14. Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany. 15. Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany. 16. Center for Geriatric Medicine, University Hospital Tübingen, Tübingen, Germany. 17. Department of Pediatric Cardiology, Saarland University, Saarbrücken, Germany. 18. Schwerpunktpraxis Hämatologie und Onkologie, Kaiserslautern, Germany. 19. Department of Gynecology, University Hospital Würzburg, Würzburg, Germany. 20. Department of Anaesthesiology, Intensive Care and Pain Therapy, Saarland University Medical Center and Faculty of Medicine, Saarland University, Homburg, Germany. 21. Center for Bioinformatics, Saarland University, Saarbrücken, Germany. 22. Parkinson's Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg. 23. Department of Cardiothoracic Surgery, Völklingen Heart Centre, Völklingen, Germany. 24. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California.
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.
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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