# Elucidating the Role of Perivascular Niche in Glioblastoma Invasion and Therapeutic Resistance at Single Cell Resolution using Biomimetic Tumor Microenvironment Models > **NIH NIH R01** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2022 · $377,239 ## Abstract Summary One of the critical challenges in the treatment of Glioblastoma (GBM) is the presence of highly resistant cells with stem-like properties, called glioma stem cells (GSCs), that evade surgical resection, resist conventional treatments and are primarily responsible for tumor recurrence. The perivascular niche within the GBM tumor microenvironment (TME) has been well recognized as a critical site that shelters GSCs and promotes their stemness, invasion, and therapeutic resistance. Extensive studies from others and our lab, using in vitro and in vivo models, have demonstrated that the crosstalk between the endothelial cells (ECs) and GSCs regulates GSC proliferation, tumorigenicity and self- renewal capacity. However, the perivascular niche is a complex microenvironment comprised not only of ECs but multiple other cell types including astrocytes, pericytes, and immune cells. How the cell-cell interactions between the various cellular components of the perivascular niche modulate GSC behavior (proliferation vs. quiescence and invasion vs. homing) and therapy resistance is poorly understood. To address these unmet biological knowledge gaps, there is a critical need for sophisticated and more realistic ex vivo tumor models that better recapitulate the physiological complexities of the GBM perivascular niche to advance our fundamental understanding of the biology of the disease and predict therapeutic responses. Recently, we have established and validated an on-chip microfluidic tumor model of GBM, with a unique 3D organotypic architecture, to study the influence of the perivascular niche on GSC invasion. We have shown that co-culturing of astrocytes enhances EC-induced invasion of GSCs, where RNA-seq analysis of mono-culture vs. tri-culture provided a mechanistic insight into the receptor-ligand pairs that mediate the interactions between cells. Based on these foundational developments, in this study our goal is to develop an ex vivo tumor model of GBM, bioinspired from the native perivascular niche, with patient-derived cells to dissect the role of cellular components within the niche on GSC biology and response to treatment at single cell resolution. In Aim 1, our objective is to determine the influence of the key cell types within the perivascular niche on GSC-EC interactions. In Aim 2, we plan to mechanistically unveil the impact of radiation treatment on GSCs- perivascular niche interactions, while in Aim 3, we will blunt invasion and sensitize GSCs through disruption of niche-tumor cell interactions. Our study design uniquely employs an interdisciplinary approach including microengineering of a bioinspired ex vivo tumor model, single-cell level resolution analysis, molecular-level transcriptomics, and validation using ex vivo patient tumor samples. Successful completion of these studies will not only further our understanding of the interactions of GSCs with the perivascular niche but will also facilitate identification of novel target... ## Key facts - **NIH application ID:** 10487570 - **Project number:** 5R01NS123038-02 - **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS - **Principal Investigator:** Mehdi Nikkhah - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $377,239 - **Award type:** 5 - **Project period:** 2021-09-15 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10487570 ## Citation > US National Institutes of Health, RePORTER application 10487570, Elucidating the Role of Perivascular Niche in Glioblastoma Invasion and Therapeutic Resistance at Single Cell Resolution using Biomimetic Tumor Microenvironment Models (5R01NS123038-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10487570. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*