# Enhanced mass-spectrometry-based approaches for in-depth profiling of the cancer extracellular matrix

> **NIH NIH R21** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $135,507

## Abstract

Project Summary
Cancer has claimed over 600,000 lives in 2020 in the United States. A better understanding of the mechanisms
underlying cancer progression has led to the development of early detection strategies and novel treatment
modalities that have contributed to the decrease in cancer-related deaths observed for the past few decades.
Yet, cancer remains a deadly disease. There is thus an acute need to identify new cancer vulnerabilities. This
will require exploring understudied aspects of cancers, which requires the development of novel technologies.
One understudied aspect of cancer is the extracellular matrix (ECM). The ECM is a complex meshwork of
proteins providing architectural support and biochemical signals critical for cellular functions required for tumor
progression. Overcoming technical challenges posed by largely insoluble ECM proteins, we previously devised
a proteomic pipeline specifically geared towards ECM proteins and showed that the tumor ECM is composed of
200+ distinct proteins. We further identified ECM signatures predictive of patient outcome and novel ECM
proteins playing functional roles in cancer progression. The ECM thus represents an important reservoir of
potential prognostic biomarkers and therapeutic targets. However, the ECM has many more secrets to reveal.
For example, ECM proteins exist in various isoforms and are extensively post-translationally modified, yet, we
do not know which proteoforms are present in the tumor ECM. ECM protein structure and the architecture of the
ECM meshwork is key to mediate function, yet, very little is known about ECM protein folding and its impact on
protein functions. Since proteomics relies on the generation of peptides from protein via proteolysis and protein
identification via database search, we propose that enhancing these steps will provide a more complete picture
of the cancer ECM and significantly advance cancer research. Here, we propose to use in-silico modeling to
define the optimal cleavage conditions to achieve near-complete coverage of ECM protein sequences (Aim 1).
Standard proteomic protocols rely on protein denaturation prior to protein digestion. Yet, we know that many
ECM functions are governed by its architecture. We thus propose to perform native ECM digestion to gain
insights into the structure of individual proteins, and the secondary and tertiary structures of the ECM meshwork
(Aim 2). To facilitate ECM research, we have previously developed a searchable database, MatrisomeDB,
compiling ECM proteomic dataset. Here, we propose to enhance the content and functionalities of MatrisomeDB
to include our new prediction model and a new tool to the visualize sequence coverage on 3D models of ECM
proteins predicted by Google’s AlphaFold (Aim 3). Our technology, offering substantial improvements over
conventional proteomic approaches, targets the unmet technical need to profile, with deep coverage and high
sensitivity, the protein composition of the tumor ECM. When...

## Key facts

- **NIH application ID:** 10903967
- **Project number:** 5R21CA261642-03
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Yu Gao
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $135,507
- **Award type:** 5
- **Project period:** 2022-09-15 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903967

## Citation

> US National Institutes of Health, RePORTER application 10903967, Enhanced mass-spectrometry-based approaches for in-depth profiling of the cancer extracellular matrix (5R21CA261642-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10903967. Licensed CC0.

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