# Project 1

> **NIH NIH P01** · UNIVERSITY OF MINNESOTA · 2022 · $552,743

## Abstract

Abstract
In glioblastoma (GBM), cancer cells break away from the tumor mass and infiltrate into adjacent brain tissue.
Like other poor-prognosis cancers, GBM has been extensively analyzed by genome-wide transcriptomic
analyses. This has led to the identification of 3-4 subtypes that span a spectrum of states from “Proneural” (PN)
to “Mesenchymal” (MES). While the identification of subtypes is intriguing, it has yet to produce clinically-
actionable mechanistic insight. In our unpublished work, we discovered key mechanical signatures of these two
subtypes. Using our Sleeping Beauty (SB) immunocompetent genetically-induced mouse glioma model, we
found that the oncogenic driver NRasG12V promotes a MES-like phenotype and the oncogenic driver PDGFβ
promotes a PN-like phenotype. In addition, we found that NRas-driven tumors migrate fast and generate large
traction forces, while PDGFβ-driven tumors migrate slowly and generate weaker traction forces, features we also
observe with human cells in brain tissue. Thus, the two subtypes may each have their own distinct mechanical
weaknesses that can be effectively targeted. Since brute force trial-and-error of possible targets is not feasible,
we will manage complexity using the modeling approach that is widely used in engineering. As pointed out in the
Overall section of this proposal, the mobility of the cancer cells and the antitumoral T cells are both critical
determinants of tumor progression/regression, so we will apply our recently published “Cell Migration
Simulator” (CMS1.0) to cancer and immune cell migration and use experimental microscopy measurements
made in brain tissue to identify the model parameters for the two GBM subtypes. This will then allow us to identify
key mechanical vulnerabilities that will be tested using digital multiplex T cell genome engineering (as described
in Project 3) and will provide a computational platform for application to pancreatic cancer and immune cells (in
Project 2). To simulate the multicellular migration, proliferation, and immune-mediated killing dynamics, we will
apply our “Brownian Dynamics Tumor Simulator” (BDTS1.0) to predict the overall tumor dynamics of the
NRas (MES) and PDGFβ (PN) tumors. Interestingly, like the human disease, the NRas (MES) tumors are
immunologically ‘hot’, while the PDGFβ (PN) tumors are immunologically ‘cold’. Thus, the BDTS1.0, once
developed for these two subtypes of brain tumors, will allow us to predict the effects of emergent immunotherapy
concepts developed by our team, including CD200 peptide therapy and Peptide Alarm Therapy. By
constraining the simulators with data obtained by live cell fluorescence microscopy, we will develop a multiscale
computational model that provides mechanistic de-risking and optimization to maximize the physical proximity
and encounter frequency between antitumoral T cells and cancer cells. Together the modeling and experiments
will allow us to test our central hypothesis that T cell proximity to can...

## Key facts

- **NIH application ID:** 10489757
- **Project number:** 5P01CA254849-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** David J. Odde
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $552,743
- **Award type:** 5
- **Project period:** 2021-09-16 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10489757

## Citation

> US National Institutes of Health, RePORTER application 10489757, Project 1 (5P01CA254849-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10489757. Licensed CC0.

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