Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients
Authors: Tobias Fehlmann, Mustafa Kahraman, Nicole Ludwig, Christina Backes, Valentina Galata, Verena Keller, Lars Geffers, Nathaniel Mercaldo, Daniela Hornung, Tanja Weis, Elham Kayvanpour, Masood Abu-Halima, Christian Deuschle, Claudia Schulte, Ulrike Suenkel, Anna-Katharina von Thaler, Walter Maetzler, Christian Herr, Sebastian Fähndrich, Claus Vogelmeier, Pedro Guimaraes, Anne Hecksteden, Tim Meyer, Florian Metzger, Caroline Diener, Stephanie Deutscher, Hashim Abdul-Khaliq, Ingo Stehle, Sebastian Haeusler, Andreas Meiser, Heinrich V Groesdonk, Thomas Volk, Hans-Peter Lenhof, Hugo Katus, Rudi Balling, Benjamin Meder, Rejko Kruger, Hanno Huwer, Robert Bals, Eckart Meese, Andreas Keller
Journal: JAMA Oncology (2020)

<h3>Importance</h3> 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. <h3>Objective</h3> 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. <h3>Design, Setting, and Participants</h3> 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. <h3>Main Outcomes and Measures</h3> Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer. <h3>Results</h3> 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%). <h3>Conclusions and Relevance</h3> 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.