# Click Chemistry-Mediated Surface Protein Assay for Quantifying Subpopulations of Hepatocellular Carcinoma-associated Extracellular Vesicles

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $674,097

## Abstract

PROJECT SUMMARY
Hepatocellular carcinoma (HCC) accounts for 80-85% of primary liver cancers, and mainly occurs in patients
with liver cirrhosis or chronic hepatitis B virus (HBV) infection. Prognosis of HCC is dismal primarily due to
advanced stage of disease at diagnosis. Current clinical practice guidelines recommend HCC surveillance by
biannual liver ultrasound with/without serum alpha-fetoprotein (AFP) for at-risk patients to achieve the goal of
detecting HCC at a curative stage. However, their accuracy remains relevantly low with sensitivity between 60-
70% at a specificity of 90%. As such, novel biomarkers for early detection of HCC are still desperately 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 and those cells within tumor microenvironment. Tumor-associated 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-associated EVs could mirror those of the parental tumor cells and those cells
within tumor microenvironment, exploiting the diagnostic potential of HCC-associated 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.
We propose an HCC EV Surface Protein Assay (SPA) to quantify subpopulations of HCC-associated EVs for
detecting early-stage HCC. The proposed HCC EV SPA couples two powerful technologies: Click Chemistry-
mediated EV Click Beads for isolating different subsets of HCC-associated EVs, and downstream 4-plex real-
time immuno-PCR for quantification of the isolated subsets of HCC EVs. One of the major challenges emerging
in the field of EV utilization for clinical use is the lack of robust and reproducible methods for the isolation of
subpopulations of tumor-associated EVs. Conventional methods for isolating EVs, such as ultracentrifugation,
filtration, and precipitation, are incapable of isolating subpopulation of tumor-associated EVs from total EVs. New
research efforts have been devoted to exploring immunoaffinity-based capture techniques for enriching tumor-
associated EVs from the plasma samples of patients with different solid tumors. However, there are challenges
identified for the single antibody-mediated tumor-derived EV enriching approaches, such as limited
sensitivity/specificity and a need for multiple capture antibodies to overcome the tumor heterogeneity. In order
to address these concerns, our research team developed HCC EV SPA, which combines a click chemistry-
mediated tumor-associated EV isolation, and downstream 4-plex real-time immuno-PCR. HCC EV SPA is
capable of highly sensitive and specific quantification of 32 subpopulations of HCC EVs in patients’ plasma
samples, based on the combined use of 8 different HCC-associated surface protein markers and four EV surface
markers...

## Key facts

- **NIH application ID:** 10737497
- **Project number:** 1R01CA277530-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Vatche Agopian
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $674,097
- **Award type:** 1
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10737497

## Citation

> US National Institutes of Health, RePORTER application 10737497, Click Chemistry-Mediated Surface Protein Assay for Quantifying Subpopulations of Hepatocellular Carcinoma-associated Extracellular Vesicles (1R01CA277530-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10737497. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*