# BCCMA: Basic and Translational Mechanisms of Cancer Initiation of the Urothelium in Veterans Exposed to Carcinogens: Defining the Molecular and Spatial Features of Carcinoma in situ of the Bladder

> **NIH VA I01** · JESSE BROWN VA MEDICAL CENTER · 2022 · —

## Abstract

PROJECT SUMMARY ABSTRACT
Herein, a group of collaborative merit review applications (CMA) aim to advance the precision
management of bladder cancer (BCa), especially focused on the early stage initiation of urothelium as
a model of dynamic epithelial changes in response to smoking and deployment-related carcinogens.
Malignancies are the second most common cause of death among Veterans and BCa is the fourth
most common cancer in the VA. Among tumor types, 70% of BCa is confined to the superficial part of
the bladder (Stages T1, Ta, and CIS), with the remainder invasive of the muscle or metastatic. If BCas
are identified at an earlier stage, nearly all of these tumors are treatable with a combination of surgery
and intracavitary therapy. Yet, there are currently no validated or recommended screening procedures
to identify asymptomatic BCas and there are no methods to identify at-risk patients at an earlier and
more curable stage. The proposed CMAs aim to address these limitations and to significantly disrupt
BCa prevention, detection, risk stratification and precision treatment by dissecting the genetic and
molecular foundations of early stage BCa. The projects include the following: CMA1 aims to determine
the genetic and immune-suppressive landscape of CIS to identify new therapeutics and
immunotherapies. CMA2 investigates the plasticity of the urothelium to determine how PPARg can
direct epithelial differentiation as a possible modulator of CIS. CMA3 will examine the epigenetic basis
of urothelial differentiation and the role of LSD1-inhibitor, Methysticin, as a chemopreventative agent to
restore the epigenetic imbalance of the urothelium. Finally, CMA4 will develop artificial intelligence
algorithms for enhanced cystoscopy imaging technologies or BCa detection and risk stratification.
These CMAs are linked both intrinsically among each other and extrinsically with all contributors already
supported by VA R&D with Merit Awards focused on BCa to maximize synergy and ensure success.
Rationale: More than 80% of Veterans report a history of tobacco smoking with 90% of Veterans with
BCa self-reported smokers. Unlike lung, prostate or colorectal cancer, there are no screening protocols
recommended for Veterans at risk for BCa. There is no primary care recommendation for uniform
evaluation of blood in the urine, and no urinary tests have a high negative predictive value that can
replace cystoscopy. Almost all patients with BCa develop blood in the urine at some time, but there is
often delays in pursuing an evaluation by months to years that lead to tumor progression due to lack of
referrals to urologic surgery for evaluation. Once diagnosed, the urothelium is often challenging to follow
and up 20% of invasive tumors will progress to higher stage cancer. Treatment for early stage invasive
bladder cancer is dependent on BCG immunotherapy, but BCG is frequently unavailable and
underutilized for maintenance and 30% of BCas become BCG unresponsive. Therefor...

## Key facts

- **NIH application ID:** 10258562
- **Project number:** 1I01BX005599-01
- **Recipient organization:** JESSE BROWN VA MEDICAL CENTER
- **Principal Investigator:** Joshua James Meeks
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2021-10-01 → 2025-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10258562

## Citation

> US National Institutes of Health, RePORTER application 10258562, BCCMA: Basic and Translational Mechanisms of Cancer Initiation of the Urothelium in Veterans Exposed to Carcinogens: Defining the Molecular and Spatial Features of Carcinoma in situ of the Bladder (1I01BX005599-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10258562. Licensed CC0.

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