Tissue-Engineered Models of Glioblastoma for Evaluating Treatment Responses and it is a Research Scholar Grant, which are given to early-stage investigators (https://www.cancer.org/research/we-fund-cancer-research/apply-research-grant/grant-types/research-scholar-grants.html). It’s 3 years and $792,000 (including direct costs)

Glioblastoma (GBM) is a highly lethal brain cancer that inevitably acquires resistance to multiple treatments. Although many treatments have shown promise in the laboratory studies, these results have not translated to clinical efficacy. We posit that these disappointments are because common experimental test beds do not account for 1) the extracellular matrix (ECM) or 2) the diverse cell population present within a single tumor. The ECM, proteins and sugars that make up the space surrounding cells, interacts with tumor cells, facilitating their ability to acquire resistance to multiple treatments. In addition, different cells within a single tumor acquire diverse functions, acting together in a complex ecosystem to fuel cancer progression.

Thus, we are engineering artificial tumor tissues that incorporate key aspects of the brain ECM and a patient’s own tumor cells. The proposed approach, in which patient-derived tumor cells are cultured in brain-mimetic biomaterials, is less costly, more time efficient and better controlled than animal studies — yet, unlike other cell culture methods, yields results with comparable clinical relevance. Using a patient’s own cells to create patient-tailored test beds for treatment screening will allow these test beds to capture the unique characteristics of each patient’s disease. While the majority of approaches to personalized cancer treatment rely solely on a patient’s genetic characteristics, this proposal aims to integrate this genetic information with patient-specific functional assessments to better predict treatment response.

Ultimately, we anticipate these patient-specific tumor models will be able to directly inform clinical actions to improve patient outcomes. This proposal describes the next steps towards accomplishing this long-term goal. First, we propose to evaluate the ability of these tissue-engineered tumor models to predict responses to a variety of treatments across a heterogeneous patient population. Second, we aim to improve the ability of tissue-engineered tumors to capture the heterogeneous cell population that composes an individual GBM tumor. Together, we expect the proposed studies will improve robustness of tissue-engineered models of GBM tumors by characterizing their ability to faithfully capture heterogeneity both across patients and within individual patient tumors.

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UCLA Bioengineering

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