# Project 1:  Deciphering the Dynamic Evolution of the Tumor-Neural Interface

> **NIH NIH U54** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2024 · $407,357

## Abstract

ABSTRACT – PROJECT 1
The central premise of our CSBC MIT/DFCI Center for Systems Biology in Glioblastoma is that high-content
systems-level measurements at molecular, microscopic, and macroscopic scales with spatial resolution will
enable the development of computational models to map and predict tumor dynamics leveraging data integration
and deconvolution for computational modeling of the glioblastoma-microenvironment. The establishment of this
novel GBM model will support the identification of critical signaling and metabolic pathways and networks
regulating tumor progression and therapeutic resistance, while providing biomarkers of tumor state and efficacy
for therapeutic developement. Project 1 will focus on elucidating networks coordinating the tumor-neuronal
interface. Recent results uncovered the ability of subpopulations of glioblastoma cells to organize in brain tumor
cell networks that include the formation of glutamatergic synapses formed between individual glioblastoma cells
and neural cells from the normal brain. The establishment of synaptic connectivity was proposed to promote
tumor cell movement along white matter, therefore implying that the mutually connected glioma cells may drive
invasion of the normal brain, which is the primary mechanism of progression and aggressiveness of malignant
glioma that ultimately renders these tumors incurable. We will now leverage key preclinical resources established
by our labs, including an integrated computational-experimental framework, annotated GBM patient-derived
xenografts (PDXs) for ex vivo and in vivo mechanistic experiments to derive a model of glioma-neuron
interactions that drive the malignant nature of glioblastoma and how perturbation of this signaling network affects
tumor proliferation, invasion, and therapeutic resistance. In Aim 1, we will use our innovative multi-omics platform
with ex vivo slice culture models to investigate the ability of neurons to support tumor cell growth and invasion
and affect cell state and develop and implement computational modeling strategies to model the dynamic
evolution of different cell types and tumor cell clones over time and in response to stimulation. Aim 2 will allow
further parametrization of the computational model with data acquired from in vivo orthotopic models tested
for the effect of anti-epileptic drugs on tumor proliferation and invasion in the context of standard of care treatment
with radiotherapy and recurrence. In Aim 3, we will analyze the impact of anti-epileptic therapeutics on the
glioma-brain network in clinical trial tissue specimens with our multi-omics platform, allowing to test and
optimize the model accuracy with clinically relevant data.

## Key facts

- **NIH application ID:** 10930067
- **Project number:** 5U54CA283114-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Nathalie YR Agar
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $407,357
- **Award type:** 5
- **Project period:** 2023-09-15 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930067

## Citation

> US National Institutes of Health, RePORTER application 10930067, Project 1:  Deciphering the Dynamic Evolution of the Tumor-Neural Interface (5U54CA283114-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10930067. Licensed CC0.

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