# Engineered ECM platforms to analyze progression in high grade serous ovarian cancer

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $57,513

## Abstract

PROJECT SUMMARY
This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-
22-058.
While standard biological methods such as two-dimensional, in vitro cell culture and in vivo animal models are
useful to examine certain biological questions, the complexity of cancer requires a continuum of model
systems. In this work, we propose to create a foundation for tumor tissue engineering by employing multiple
innovative methodologies to first characterize and then build biomimetic models of the extracellular matrix
(ECM) in the tumor microenvironment. The ECM is a key element in biomimicry, and particularly important to
consider in the context of cancer, where the composition and architecture of the ECM change drastically with
disease progression.
We will specifically focus upon high-grade serous ovarian cancer (HGSOC), which is associated with a
particularly poor survival rate of less than 50%. HGSOC originates in the fallopian tube, metastasizes to the
ovary, and then disseminates throughout the peritoneum to organs such as the omentum and small bowel
mesentery. There is a recognized for additional models to study HGSOC, and the existing models have not
examined the impact of variations in ECM composition or structure. Using state-of-the-art mass spectrometry,
imaging technologies and thorough immunohistochemical analysis, we will characterize differences between
the ECM of normal and diseased tissues across the different stages of disease. We will utilize innovative
engineering approaches including tissue engineering and microfabrication to recreate tissue-specific
pathophysiology in the context of HGSOC, and examine the impact of variations in ECM composition and
architecture on cellular behaviors. Through these experiments in combination with computational/statistical
analysis, we will determine which of the many changes that we identify are causative for disease progression.
The models we develop will have a transformative impact on the study of HGSOC, and the lessons learned
from our process can be applied broadly towards the larger goal of developing pathophysiologically-relevant
models of all cancer types.

## Key facts

- **NIH application ID:** 10614850
- **Project number:** 3R01CA232517-04S1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Paul J Campagnola
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $57,513
- **Award type:** 3
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10614850

## Citation

> US National Institutes of Health, RePORTER application 10614850, Engineered ECM platforms to analyze progression in high grade serous ovarian cancer (3R01CA232517-04S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10614850. Licensed CC0.

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