Abstract 7084: Multi-omics information from tumor-derived extracellular vesicles enables biomarker discovery and early detection in non-small cell lung cancer

Abstract Liquid biopsy plays an increasingly influential role in clinical decision making by supporting earlier cancer detection, diagnosis, patient stratification, and treatment monitoring. The most common approaches utilize cell-free circulating tumor DNA or circulating tumor cells in the peripheral blood of cancer patients. However, these methods are impeded by miniscule analyte abundance leading to unfavorable signal to noise ratios. As a promising alternative, extracellular vesicles (EVs) have emerged as a complementary liquid biopsy analyte. EVs are cell-derived lipid membrane encapsulated particles carrying diverse protein, nucleic acid, and metabolite cargos. Blood plasma of cancer patients contains EVs from healthy cells and tissues throughout the body, but also contains a population of EVs derived directly from tumor tissue. These tumor-derived EVs (TDEVs) serve as rich sources of insight into tumor biology and disease pathology. To detect TDEVs amongst background noise, we have developed a clinically applicable, multi-omic EV subpopulation interrogation pipeline that robustly profiles TDEVs in blood plasma. We have demonstrated the ability of the pipeline to enrich cancer markers and detect activation of pathways driving oncogenesis across several different cancers, including non-small cell lung cancer (NSCLC). There is a particularly compelling need for liquid biopsy-based solutions in early detection and screening applications, when patient adherence to standard of care screening regimens could be improved by simplifying the procedure to a routine blood draw. Here, we have applied our EV-omic pipeline in combination with machine learning in a cohort of NSCLC patients to robustly identify Stage I and II disease using blood-based EV proteomic and transcriptomic signatures. Biomarker profiles found to be most important for patient stratification combine markers known to be relevant in tumor tissue along with novel features. This work highlights our EV subpopulation technology and its ability to address the major challenge of early cancer detection in liquid biopsy. Citation Format: Kelley Van Vaerenberghe, Margie Kinnersley, Claire Seibold, Tre Blohm, Riley Kemp, Sean Lodmell, Amanda Mast, Tia Seibold, Rachel Short Miller, Katie Havranek. Multi-omics information from tumor-derived extracellular vesicles enables biomarker discovery and early detection in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7084.