# CMA- Marker-assisted prevention and risk stratification (MAPRS):  Mucin signatures and molecular imaging for the early detection of colorectal cancer.

> **NIH VA I01** · VA SAN DIEGO HEALTHCARE SYSTEM · 2021 · —

## Abstract

PROJECT SUMMARY ABSTRACT
CMA: Marker-assisted prevention and risk stratification (MAPRS): Mucin signatures and their molecular
imaging for the early detection of colorectal cancer Herein, a group of collaborative merit review applications
(CMA) aim to advance precision management of cancers, especially focusing on marker-assisted prevention
and risk stratification (MAPRS) of colorectal cancers (CRCs), which is the third major cancer in the USA and
accounts for 9.5% of all cancers among Veterans. While screening colonoscopy has emerged as perhaps the
most effective lifesaving intervention against CRC to date, their successes have been limited by ease-of-use and
downward cost pressures. Also, despite high R0 resection rates in patients with CRC, local and distant recurrence
is still a significant problem and has been cited as high as 40 per cent. The proposed CMAs aim to address
these limitations and to significantly disrupt CRC prevention, detection, risk stratification and precision treatment
by advancing MAPRS. The projects include the followings. CMA1 aims to develop artificial intelligence enhanced
endoscopy for colorectal cancer prevention. CMA2 plans to examine mucin-based markers for improved
endoscopic detection, resection, histological classification and surveillance of pre-malignant colonic polyp
(sessile serrated adenoma/polyps and adenoma) and examine their clinical utility as an adjunct to screening
colonoscopy. CMA3 proposes to validate tissue and blood-based combinatorial biomarker panels, derived from
functional pathway-specific studies, to improve the early detection of colon cancer and stratify populations
according to their risk for developing CRC. Finally, CMA4 plans to examine the genomic and/or cellomic drug
response profiling using patients’ tumor discards and develop a tumor-on-chip platform toward an evidence-
based precision treatment strategy for CRCs. These CMRs are linked both intrinsically among each other and
extrinsically with VA colorectal cancer cellgenomics consortium (VA4C) to maximize synergy and ensure
success. Rationale: With a lifetime development risk of 5%, CRC is the third-most common cancer and the
second major cause of cancer-related deaths. Colonoscopy polypectomy during screening have significantly
reduced both incidence and overall mortality. Further, even after widespread use of screening colonoscopy,
there is an age-adjusted incidence of and mortality from prevalent, right-sided CRCs. Major proportion of these
tumors emerge from sessile serrated adenoma/polyps (SSA/Ps) that have gone undetected during initial
colonoscopy. Furthermore, only 40% of CRCs are diagnosed at early stage, in part due to lack of compliance
and to low sensitivity and specificity of the more common tests, including fecal occult blood tests and the insidious
(asymptomatic) nature of localized disease. Our preliminary data suggest altered expression of various mucins
during CRC progression, characterized by aberrant localizatio...

## Key facts

- **NIH application ID:** 10043822
- **Project number:** 5I01BX004494-02
- **Recipient organization:** VA SAN DIEGO HEALTHCARE SYSTEM
- **Principal Investigator:** Michael Bouvet
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-10-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10043822

## Citation

> US National Institutes of Health, RePORTER application 10043822, CMA- Marker-assisted prevention and risk stratification (MAPRS):  Mucin signatures and molecular imaging for the early detection of colorectal cancer. (5I01BX004494-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10043822. Licensed CC0.

---

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