# Clinical and pre-clinical investigation of extracellular vesicles as a mechanism of toxicity in the bladder of prostate cancer patients treated with radiotherapy

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2024 · $449,295

## Abstract

We propose to study extracellular vesicles (EVs) as novel mediators of the pathogenesis of radiation cystitis
(RC), which affects approximately 15% of prostate cancer (PCa) patients treated with radiotherapy (RT).
Bladder radiotoxicity impacts the lives of PCa survivors, most notably older patients, and there are no
predictive markers associated with its incidence, nor are there durable molecular therapies capable of
preventing RC. There is an urgent need for the development of effective medical countermeasures against RC
and the identification of molecular diagnostics to aid in mitigating this debilitating treatment-related
complication. EVs are lipid-bound nanoparticles that mediate intercellular communication by delivering cargo
molecules to neighboring or distant cells. Studies have shown that RT induces EV release and alters the
composition of these EVs. However, most research to date has been conducted in cell lines, with limited
preclinical studies and no human research. Using preserved samples collected from a genome-wide
association study (GWAS), we reported for the first time a link between urinary EV (uEV) levels and the future
onset of hematuria, providing a promising biomarker that may enable more timely treatment of at-risk patients
to mitigate the development of late bladder toxicities. The prognostic utility of EVs in serum was less robust.
This is further supported by an animal study showing a correlation of uEV particle counts with RT-induced
bladder toxicity. Critically, post-RT uEVs derived from PCa patients who developed late hematuria induced
substantial oxidative stress in normal bladder recipient cells, further supporting a functional link between EVs
and radiotoxicity. Proteomic analyses of 12 uEV samples revealed that those EV cargo proteins were
profoundly altered by RT. Notably, we identified 60 RT-toxicity signature uEV proteins including many with
functions associated with innate immunity and neutrophil activity, highlighting potential mechanism(s) of action.
Based on these studies, we hypothesize that RT induces the release of EVs and alters their composition, and
the resultant RT-EVs carry immunologically active biomolecules, thereby inducing additional cellular damage.
The kinetics of RT-EV release and RT-EV cargo molecules can serve as predictive biomarkers for RT-induced
toxicity that will allow for early intervention to mitigate RT toxicity. We propose three Specific Aims. Aim 1: To
define the roles of radiation-induced extracellular vesicles in mediating RT-induced bladder damage in vitro
and in vivo. Aim 2: Characterization of RT-EV cargo molecules and their roles in mediating radiotoxicity. Aim
3: To establish body fluid-derived EV-based predictive biomarkers for RT-induced toxicity. Accomplishing the
proposed studies will define the functional roles of RT-EVs as mediators of RT toxicity. RT-induced EV release
kinetics and alterations in EV cargos will represent sensitive, noninvasive urine/blood-based EV bi...

## Key facts

- **NIH application ID:** 10800092
- **Project number:** 1R01CA280115-01A1
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** YI-FEN LEE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $449,295
- **Award type:** 1
- **Project period:** 2024-04-05 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10800092

## Citation

> US National Institutes of Health, RePORTER application 10800092, Clinical and pre-clinical investigation of extracellular vesicles as a mechanism of toxicity in the bladder of prostate cancer patients treated with radiotherapy (1R01CA280115-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10800092. Licensed CC0.

---

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