# Developing and Automating an Extracellular Vesicle-Based Test for Early Detection of Hepatocellular Carcinoma > **NIH NIH R44** · EXIMIUS DIAGNOSTICS CORP · 2023 · $661,609 ## Abstract PROJECT SUMMARY Hepatocellular carcinoma (HCC) comprises 80-85% of primary liver cancers and frequently develops in patients with liver cirrhosis or chronic hepatitis B virus infection. HCC's poor prognosis is primarily due to advanced-stage diagnosis. Current clinical practice guidelines recommend biannual liver ultrasounds, with or without serum alpha-fetoprotein (AFP) testing, for at-risk patients to detect HCC at a curable stage. However, their accuracy is limited, with sensitivity between 60-70% and specificity of 90%. Consequently, novel biomarkers for early detection of HCC are urgently needed. Extracellular vesicles (EVs) are a heterogeneous group of lipid nanoparticles that are released by all types of cells, and even more so by tumor cells. Tumor-derived EVs are present in circulation at relatively early stages of disease and are readily accessible across all disease stages. Since the surface proteins of tumor EVs mirror those of the parental tumor cells and those cells within tumor microenvironment, exploiting the diagnostic potential of HCC EVs’ surface protein signatures as a novel biomarker for early detection of HCC holds great promise to significantly augment the ability of current diagnostic modalities. Over the last five years, our joint team comprised of Eximius Dx, UCLA, and Cedars Sinai Medical Center (CSMC) has demonstrated of HCC EV Surface Protein (SP) Test, capable of dissecting and quantifying subpopulations of HCC EVs in plasma samples. In our 2022 Hepatology paper, we summarized a phase-2 biomarker study which successfully validated the feasibility of HCC EV SP Test for early HCC detection. The long-term goal of this Direct-to-Phase-II proposal is to advance the development, optimization, and automation of the HCC EV SP Test, with the ultimate goal of establishing a more sensitive in vitro diagnostic (IVD) test based on HCC EVs. The innovation of the proposed HCC EV SP Test lies in the integration of two platform technologies: (i) EV Click MagBeads for click chemistry-mediated capture of subpopulations of HCC EVs, and (ii) real-time immuno-PCR for quantifying the captured HCC EVs. In parallel, an algorithm will be established to process the resulting HCC EV signatures into HCC EV SP score for distinguishing early-stage HCC from at-risk cirrhosis. This new IVD test will use less then 1-mL plasma and have a sample-to-answer workflow of no more than 3 hours. By adopting an in-house developed robotic system, the automated workflow allows for a throughput > 480 samples per round. Once optimized and automated the HCC EV SP Test will be validated by clinically annotated plasma samples to assess its diagnostic performance for distinguishing early-stage HCC from at-risk liver cirrhotic patients, covering etiologies including alcohol-associated liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and viral hepatitis (B/C). The successful development of the proposed HCC EV SP Test is rapidly translatable, enabling a se... ## Key facts - **NIH application ID:** 10823687 - **Project number:** 1R44CA288163-01 - **Recipient organization:** EXIMIUS DIAGNOSTICS CORP - **Principal Investigator:** Han-Yu Chuang - **Activity code:** R44 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $661,609 - **Award type:** 1 - **Project period:** 2023-09-14 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10823687 ## Citation > US National Institutes of Health, RePORTER application 10823687, Developing and Automating an Extracellular Vesicle-Based Test for Early Detection of Hepatocellular Carcinoma (1R44CA288163-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10823687. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*