# Optimal control models of epithelial-mesenchymal transition for the design of pancreas cancer combination therapy

> **NIH NIH U01** · UNIVERSITY OF VIRGINIA · 2022 · $452,330

## Abstract

PROJECT SUMMARY
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and common cancer, with an overall five-year
survival rate of 6%. Among the factors contributing to this dismal statistic is the observation that epithelial-
derived PDAC cells, sometimes in direct response to therapy, can de-differentiate to a mesenchymal state in
which they are more chemoresistant. This observation prompts the question: should epithelial-mesenchymal
transition (EMT) be targeted to promote therapeutic response and increase patient survival? The main barrier
to exploring this idea is that we do not know how to target EMT precisely, especially in light of the complex
multivariate cell signaling dynamics that drive EMT and maintain it as a feedback response to chemotherapy.
We recently undertook a preliminary study to identify a group of druggable cell signaling pathways that may
cooperatively drive the mesenchymal state in PDAC. However, the translational potential of our current
analysis is limited in that it merely identified potential targets; it does not provide any systematic actionable
understanding, nor testable predictions, of how best to schedule combinations of drugs in time to maximize
therapeutic efficacy and minimize unintended toxicity. Consequently, we now seek to extend our preliminary
studies to develop a systems biology platform for the systematic determination of scheduled combination
therapy approaches for PDAC designed to maximally suppress EMT during treatment. In Aim 1, we will make
dynamic measurements of signaling pathway activity and cell phenotypes in PDAC cells treated with drivers of
EMT, antagonists of EMT, and chemotherapeutics. Our measurements will cover those pathways already
identified in our preliminary work as the most likely druggable regulators of EMT, and will include the effects of
hypoxia and cancer-associated fibroblasts, elements of the tumor microenvironment that may impact EMT
regulation. The goal is to obtain an information-rich data set to be used subsequently for model identification
and control computations. In Aim 2, we will use the dynamic data to develop the computational platform for
determining optimal changes to the drivers and antagonists required to achieve maximal suppression of EMT,
to be implemented as scheduled combination therapies for PDAC. This will be accomplished through: (i)
identification of a dynamic model for epithelial or mesenchymal cell state determination in response to
phosphoprotein perturbations (i.e., quantitative characterization, in the form of a computational model, the EMT
response to changes in its drivers and antagonists) and (ii) deploying the model “in reverse” to determine, via
optimal control principles, how best to combine and schedule drugs for optimal maintenance of the epithelial
phenotype. In Aim 3, we will test the model-based schedules for combination therapy in a sequence of in vitro
and in vivo experiments. Ultimately, these studies will provide pre-clinical va...

## Key facts

- **NIH application ID:** 10450032
- **Project number:** 5U01CA243007-04
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Matthew J Lazzara
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $452,330
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10450032

## Citation

> US National Institutes of Health, RePORTER application 10450032, Optimal control models of epithelial-mesenchymal transition for the design of pancreas cancer combination therapy (5U01CA243007-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10450032. Licensed CC0.

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