Glioblastoma (GBM) is a uniformly lethal brain cancer with a median survival time of fewer than 15 months. There is an urgent need to better understand the mechanisms driving treatment resistance and develop effective strategies to overcome resistance. Despite growing evidence that the extracellular matrix (ECM) is a key mediator of resistance — enabling rewiring of signaling pathways to resist treatment and driving tumor cells to adopt more aggressive phenotypes — ECM remains largely understudied in GBM in large part because of a lack of appropriate experimental models. Thus, we have developed a tissue-engineered platform, with precise control over ECM presentation, for 3D culture of cells ...

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Glioblastoma (GBM) is a uniformly lethal brain cancer with a median survival time of fewer than 15 months. There is an urgent need to better understand the mechanisms driving treatment resistance and develop effective strategies to overcome resistance. Despite growing evidence that the extracellular matrix (ECM) is a key mediator of resistance — enabling rewiring of signaling pathways to resist treatment and driving tumor cells to adopt more aggressive phenotypes — ECM remains largely understudied in GBM in large part because of a lack of appropriate experimental models. Thus, we have developed a tissue-engineered platform, with precise control over ECM presentation, for 3D culture of cells from patient tumors.

In Aim 1, we propose to use high-throughput arrays of these tissue-engineered constructs (TE-GBM) to characterize how specific ECM-cell receptor interactions alter downstream signaling pathways to affect resistance. In collaboration with the Center for Computational Oncology at UT-Austin and its Director Dr. Thomas Yankeelov, we propose to use this experimental data to construct a computational model that predicts effects of more complex ECM environments on downstream phospho-protein networks. In Aim 2, we will investigate how treatment resistance induces tumor cells to produce new ECM and build a computational model to explore how ECM may positively reinforce disease progression.

Together, results will provide a quantitative framework for how dynamic interactions between GBM cells and ECM promote resistance and how these interactions contribute to major hurdles to GBM treatment: high rates of resistance and nearly universal recurrence.

We expect this work to:

1. Provide insight into mechanisms by which current therapies fail
2. Identify new strategies to increase therapeutic efficacy
3. Lead to improved preclinical models of GBM
4. Bring us a step closer to developing effective, personalized strategies for cancer treatment

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