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NSFDirectorate for Technology, Innovation, and Partnerships

SBIR Phase I: Liquid Biopsy Diagnostic for Early Detection of Ovarian Cancer Targeting Novel Extracellular Vesicle Biomarkers

Completed
Institution
BEKEN BIO, INC., CA
Principal investigator
Christopher Millan
Funding
$275K
Duration
2024–2025

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Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on advancing a novel ovarian cancer (OC) diagnostic to enhance patient outcomes significantly. This initiative introduces a pioneering method for identifying biomarkers within extracellular vesicles (EVs), early markers specific to cancer circulating in the bloodstream. By targeting cancer EVs in a minimally invasive blood test, the project aims for unprecedented sensitivity and specificity in early detection. Success in this endeavor could pave the way for adapting the approach to develop diagnostics for other cancer types. The global market for OC diagnostics, currently valued at $1.3 billion and growing at an annual rate of 7%, reflects increasing incidence rates in younger populations and a growing emphasis on early detection strategies. A successful outcome from this project would position any resulting products favorably in the broader landscape of cancer detection technologies. This Small Business Innovation Research (SBIR) Phase I project aims to develop a highly sensitive, minimally invasive laboratory test to detect early-stage ovarian cancers (OC) by assaying relevant biomarkers on circulating extracellular vesicles (EVs) in patient plasma. EVs provide significant advantages in sensitivity and patient outcomes over tests using circulating tumor cells or cell-free tumor DNA, as they are secreted by living cancer cells within the primary tumor. This project will utilize a novel technology to identify key proteins in EVs from various OC subtypes, engineering an assay to differentiate between patients with malignant tumors and those without cancer with high specificity. The second objective is to employ a machine learning tool to perform diagnostic classification, providing actionable information to clinicians regarding the presence of a tumor. Successful completion of these objectives will facilitate the development and validation of scalable workflows for the diagnostic assay during Phase II, with the goal of having a laboratory-developed test ready for commercialization by the end of Phase II. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Directorate for Technology, Innovation, and PartnershipsSBIR Phase IBIOMEDICAL ENG AND DIAGNOSTICSovarianovarian cancerextracellular vesiclesearly detectionminimally invasiveevscirculatingvesicles evs

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