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

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $425,419

## Abstract

PROJECT SUMMARY
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:** 10248387
- **Project number:** 5R01CA232517-04
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Paul J Campagnola
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $425,419
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10248387

## Citation

> US National Institutes of Health, RePORTER application 10248387, Engineered ECM platforms to analyze progression in high grade serous ovarian cancer (5R01CA232517-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10248387. Licensed CC0.

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