Principal Investigator

Marc Elliott
Lenburg
Awardee Organization

Boston University Medical Campus
United States

Fiscal Year
2023
Activity Code
U2C
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

The Boston University - UCLA Lung Cancer Biomarker Characterization Center

Screen and incidentally detected intermediate risk indeterminant pulmonary nodules (IPN) represent a clinical dilemma for which there is little consensus about appropriate follow up due to a lack of sensitive and specific approaches for the detection of lung cancer absent invasive tissue sampling, and concerns about costs and harms from invasive tissue sampling in this large clinical population. Minimally invasive approaches that could accurately reclassify individuals from the intermediate risk group (5-65% risk of malignancy) to either low (< 5%) or high (>65%) risk would reduce uncertainty and transform the diagnostic workup of intermediate risk IPN. Developing, evaluating, standardizing, and validating such minimally invasive biomarkers so that they are ready for clinical use is the goal of the proposed BU-UCLA Lung Cancer Biomarker Characterization Center (BCC). In previous EDRN-funded work we established lung-cancer associated gene expression patterns in nasal epithelium collected with a swab from the inferior turbinate as a lung cancer biomarker. A test based on this innovative approach to lung cancer detection is being launched for clinical use as a CLIA LDT by our long-time collaborator Veracyte, Inc., which is participating in this BCC. We will evaluate the nasal biomarker for lung cancer in the setting of intermediate risk IPN. To further improve the ability to clinically discriminate benign from malignant intermediate risk IPN, the BU-UCLA Lung Cancer Biomarker Discovery Lab embedded within the BCC will develop and test lung cancer detection approaches that incorporate detection of circulating tumor cells (CTC) using a CLIA LDT assay from our collaborator LungLife AI, Inc. as well as blood based immune biomarkers, advanced imaging biomarkers, and refined nasal gene expression biomarkers. We will additionally determine if longitudinal biomarker assessment improves lung cancer detection over cross-sectional measurements. Promising assays will be refined, standardized, and validated by the BU-UCLA Lung Cancer Biomarker Reference Lab embedded within the BCC to advance them toward clinical adoption. These studies are enabled by biospecimens and imaging data that are being prospectively collected from diverse populations of patients undergoing workup for intermediate risk IPN in several large-scale ongoing clinical studies including VA LPOP, DECAMP 1-Plus, and UCLA IDx; lung cancer research programs at UCLA and Lahey; and our EDRN collaborators at Nashville VA and Vanderbilt. The BU-UCLA Lung Cancer BCC Team has the required multidisciplinary expertise in lung cancer, translational and clinical pulmonary medicine, biomarker discovery, clinical assay development, biostatistics, clinical epidemiology, pathology, imaging, artificial intelligence, biological sciences, bioinformatics, genomics, and complex scientific program management to accomplish these goals. An Administrative Core embedded within the BCC will ensure that the BCC delivers on its aim to substantially advance novel lung cancer biomarkers from discovery to clinical application and make significant contributions to the Early Detection Research Network.

Publications

  • Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology. 2023 Oct;309(1):e222904. PMID: 37815447
  • Inoue K, Hsu W. Transportability Analysis-A Tool for Extending Trial Results to a Representative Target Population. JAMA network open. 2024 Jan 2;7(1):e2346302. PMID: 38289608
  • Ding R, Yadav A, Rodriguez E, Araujo Lemos da Silva AC, Hsu W. Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas. Computers in biology and medicine. 2023 Sep 16;166:107484. Epub 2023 Sep 16. PMID: 37741228