# A novel approach to detect exosome-localized proteins and its application in breast cancer detection

> **NIH NIH R21** · AUGUSTA UNIVERSITY · 2020 · $200,970

## Abstract

Project Summary/Abstract
Because exosomes mirror the origin and status of the cells, the analysis of the content encapsulated in
exosomes from biological fluids can reveal information relevant to human health and disease. However, due to
the paucity of highly effective and rigorous exosome isolation methods, identifying exosome content carrying
biological information remains challenging. To advance our understanding of exosomes from breast cancer
cells, we developed an innovative exosome isolation technology. With this new tool, we will investigate the
function of exosomes in early detection and staging of breast cancer. In Aim 1 we will expand our new isolation
technology that reports exosome proteome and validate the functionality extensively. In Aim 2, we will
investigate how the release of exosomes is modulated in breast cancer cells in in vitro cultures and cell-derived
xenografts. Successful data collection and analysis from this proposed study will not only determine the
applicability of this novel technique but will also uncover the association between exosomes and breast cancer
cell states, thus providing molecular signatures that can potentially serve as biomarkers and therapeutic
choices for breast cancer.

## Key facts

- **NIH application ID:** 10005258
- **Project number:** 5R21CA229370-02
- **Recipient organization:** AUGUSTA UNIVERSITY
- **Principal Investigator:** Kenneth Kwon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $200,970
- **Award type:** 5
- **Project period:** 2019-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005258

## Citation

> US National Institutes of Health, RePORTER application 10005258, A novel approach to detect exosome-localized proteins and its application in breast cancer detection (5R21CA229370-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10005258. Licensed CC0.

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