PROJECT SUMMARY Lung cancer remains the number one cancer killer in the United States and clinically useful biomarkers are needed to improve early detection and diagnosis. The objectives of this proposal for our continuing Clinical Validation Center are to push early lung cancer detection biomarkers into clinical practice while continuing to serve as a core resource to the EDRN, as well as to our academic and industry partners. Our overall objective is to demonstrate that biospecimen and imaging biomarkers will provide clinical utility to diagnose lung cancer by reducing the number of invasive procedures performed for benign disease and the time to diagnosis for cancer. Aim 1 will seek to demonstrate clinical utility of a combined biomarker and radiomic approach for providing Indeterminate Pulmonary Nodule (IPN) diagnoses. We will expand the existing lung specimen and imaging biorepository available to the scientific community, demonstrate the clinical utility of combination biospecimen and radiomic biomarkers, and validate additional candidate lung cancer risk biomarkers. We will diversify the population and enhance statistical power by recruiting from existing partnerships funded by prior EDRN funding: Meharry Medical College and Washington University in St. Louis. We seek to accomplish three objectives in this aim: 1) to validate the combined approach of hsCYFRA 21-1 cancer biomarker, radiomic (HealthMyne) biomarker and a Histoplasmosis benign biomarker (MiraVista) in the EDRN Lung Team Project 2 and National Lung Screening Trial reference cohorts, 2) to determine the clinical utility of the Histoplasmosis test followed by a Combined Biomarker Model (hsCYFRA21-1, radiomics, and Mayo Model) in a Phase 4 randomized clinical trial and 3) to validate new candidate blood and epithelial biomarkers in Phase 2 and 3 prospective- specimen-collection and retrospective-blinded-evaluation (PRoBE) design studies for the early diagnosis of lung cancer. In Aim 2 we will validate radiomic risk assessment platforms in IPNs and conduct a pilot clinical implementation trial in screening discovered IPNs. We will leverage the robust bioinformatics infrastructure at Vanderbilt University Medical Center to capture and deidentify 800 thoracic CT scans in patients with IPNs. A Lung Cancer Prediction Convolutional Neural Network (LCP-CNN) and the HealthMyne radiomic model will be compared to each other and against the Lung-RADS categories. We will perform a prospective pilot evaluation of the best performing model in Lung-RADS category 3 and 4 IPNs. To accomplish Aim 2 we will: 1) compare the accuracy of LCP-CNN and HealthMyne radiomics 2) determine the LCP-CCN's ability to reclassify nodules in screening patients in a prospective clinical implementation pilot study. At the completion of this proposal, we will have 1) evaluated clinical utility of combining lung cancer biospecimen and imaging biomarkers, 2) developed a platform within current practice to present an imaging biomarker approach to improve IPN risk assessment, and 3) enhanced the biorepository resource for the EDRN and collaborative use.

PROJECT NARRATIVE Doctors continue to struggle to diagnose lung cancer early when it is most curable. Lung cancer screening along with breakthroughs in blood tests and sophisticated computerized imaging may help solve this problem. We propose to enroll a diverse population with suspicious lung lesions in a trial to test these new technologies and also validate new ones for clinical use.