INTRODUCTION: The recent findings of the National Lung Screening Trial showed 24.2% of individuals at high risk for lung cancer having one or more indeterminate nodules detected by low-dose computed tomography-based screening, 96.4% of which were eventually confirmed as false positives. These positive scans necessitate additional diagnostic procedures to establish a definitive diagnosis that adds cost and risk to the paradigm. A plasma test able to assign benign versus malignant pathology in high-risk patients would be an invaluable tool to complement low-dose computed tomography-based screening and promote its rapid implementation. METHODS: We evaluated 17 biomarkers, previously shown to have value in detecting lung cancer, against a discovery cohort, comprising benign (n = 67) cases and lung cancer (n = 69) cases. A Random Forest method based analysis was used to identify the optimal biomarker panel for assigning disease status, which was then validated against a cohort from the Mayo Clinic, comprising patients with benign (n = 61) or malignant (n = 20) indeterminate lung nodules. RESULTS: Our discovery efforts produced a seven-analyte plasma biomarker panel consisting of interleukin 6 (IL-6), IL-10, IL-1ra, sIL-2Rα, stromal cell-derived factor-1α+β, tumor necrosis factor α, and macrophage inflammatory protein 1 α. The sensitivity and specificity of our panel in our validation cohort is 95.0% and 23.3%, respectively. The validated negative predictive value of our panel was 93.8%. CONCLUSION: We developed a seven-analyte plasma biomarker panel able to identify benign nodules, otherwise deemed indeterminate, with a high degree of accuracy. This panel may have clinical utility in risk-stratifying screen-detected lung nodules, decrease unnecessary follow-up imaging or invasive procedures, and potentially avoid unnecessary morbidity, mortality, and health care costs.
INTRODUCTION: The recent findings of the National Lung Screening Trial showed 24.2% of individuals at high risk for lung cancer having one or more indeterminate nodules detected by low-dose computed tomography-based screening, 96.4% of which were eventually confirmed as false positives. These positive scans necessitate additional diagnostic procedures to establish a definitive diagnosis that adds cost and risk to the paradigm. A plasma test able to assign benign versus malignant pathology in high-risk patients would be an invaluable tool to complement low-dose computed tomography-based screening and promote its rapid implementation. METHODS: We evaluated 17 biomarkers, previously shown to have value in detecting lung cancer, against a discovery cohort, comprising benign (n = 67) cases and lung cancer (n = 69) cases. A Random Forest method based analysis was used to identify the optimal biomarker panel for assigning disease status, which was then validated against a cohort from the Mayo Clinic, comprising patients with benign (n = 61) or malignant (n = 20) indeterminate lung nodules. RESULTS: Our discovery efforts produced a seven-analyte plasma biomarker panel consisting of interleukin 6 (IL-6), IL-10, IL-1ra, sIL-2Rα, stromal cell-derived factor-1α+β, tumor necrosis factor α, and macrophage inflammatory protein 1 α. The sensitivity and specificity of our panel in our validation cohort is 95.0% and 23.3%, respectively. The validated negative predictive value of our panel was 93.8%. CONCLUSION: We developed a seven-analyte plasma biomarker panel able to identify benign nodules, otherwise deemed indeterminate, with a high degree of accuracy. This panel may have clinical utility in risk-stratifying screen-detected lung nodules, decrease unnecessary follow-up imaging or invasive procedures, and potentially avoid unnecessary morbidity, mortality, and health care costs.
Authors: Brian M Nolen; Aleksey Lomakin; Adele Marrangoni; Liudmila Velikokhatnaya; Denise Prosser; Anna E Lokshin Journal: Cancer Prev Res (Phila) Date: 2014-11-21
Authors: Charles E Birse; Robert J Lagier; William FitzHugh; Harvey I Pass; William N Rom; Eric S Edell; Aaron O Bungum; Fabien Maldonado; James R Jett; Mehdi Mesri; Erin Sult; Elizabeth Joseloff; Aiqun Li; Jenny Heidbrink; Gulshan Dhariwal; Chad Danis; Jennifer L Tomic; Robert J Bruce; Paul A Moore; Tao He; Marcia E Lewis; Steve M Ruben Journal: Clin Proteomics Date: 2015-07-16 Impact factor: 3.988
Authors: Michael R Mehan; Stephen A Williams; Jill M Siegfried; William L Bigbee; Joel L Weissfeld; David O Wilson; Harvey I Pass; William N Rom; Thomas Muley; Michael Meister; Wilbur Franklin; York E Miller; Edward N Brody; Rachel M Ostroff Journal: Clin Proteomics Date: 2014-08-01 Impact factor: 3.988
Authors: Anil Vachani; Harvey I Pass; William N Rom; David E Midthun; Eric S Edell; Michel Laviolette; Xiao-Jun Li; Pui-Yee Fong; Stephen W Hunsucker; Clive Hayward; Peter J Mazzone; David K Madtes; York E Miller; Michael G Walker; Jing Shi; Paul Kearney; Kenneth C Fang; Pierre P Massion Journal: J Thorac Oncol Date: 2015-04 Impact factor: 15.609
Authors: E L Crawford; A Levin; F Safi; M Lu; A Baugh; X Zhang; J Yeo; S A Khuder; A M Boulos; P Nana-Sinkam; P P Massion; D A Arenberg; D Midthun; P J Mazzone; S D Nathan; R Wainz; G Silvestri; J Tita; J C Willey Journal: BMC Pulm Med Date: 2016-01-22 Impact factor: 3.317