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

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.