Glioblastoma (GBM) is one of the most lethal cancers, in part due to its heterogeneous nature. GBM cells are able to re-wire their signaling pathways in response to treatment which allows them to adapt and resist treatment. Furthermore, the GBM tumor microenvironment is composed of multiple cell types, including macrophage/microglia (MF/M). Tumor-associated MF/Ms secrete immunosuppressive factors and lose their ability to clear and destroy cancer cells through phagocytosis. GBM heterogeneity renders available GBM therapeutic treatments inadequate and calls for rational approaches for designing more effective therapeutics that overcome resistance mechanisms. We have identified the multifunctional signaling adaptor protein Sprouty2 (SPRY2) as a potential tumor promoter in GBM. Our new preliminary data shows that SPRY2 regulates GBM cell response to multiple classes of therapeutics, and modulates associated signaling pathways. Based on our most recent data, we hypothesize that SPRY2 modulates GBM cell response to tyrosine kinase inhibitors (TKIs) and DNA damaging agents (DDAs). This hypothesis drives Aim 1: to investigate the role of SPRY2 in regulating drug resistance of GBM tumors. Additional new and preliminary data indicates that GBM SPRY2 expression regulates GBM secreted factor levels and MF transcription factor activation. Based on this recent data, we hypothesize that SPRY2 regulation of GBM secreted factors regulates tumor associated MF/M phenotypes. This hypothesis drives Aim 2: to investigate the role of SPRY2 in regulating interactions between GBM cells and MF/Ms.
For Aim 1, we will quantify cell death and cell cycle arrest in response to TKIs and DDAs in GBM cells with or without SPRY2 knockdown (kd). In parallel we will acquire a high dimensional, quantitative, and dynamic protein phosphorylation dataset spanning signaling networks regulating cell survival, apoptosis, and DNA damage response using multiplexed Luminex assays. We will utilize these paired phenotypic and signaling measurements to construct a partial least squares regression (PLSR) model, to predict which signaling molecules most strongly regulate phenotypes. We will validate model predictions by kd or inhibition of model identified signaling molecules.
For Aim 2, we will quantify conditioned medium (CM) from GBM cells with or without SPRY2 kd for 37 secreted factors by Luminex. We will assess the phenotypes of MFs cultured with GBM CM by measuring transcription factor activity, by immunoblotting, and phagocytic capacity, by a quantitative microscopy assay. We will utilize these measurements to construct a PLSR model to predict which secreted factors most strongly regulate MF phenotypes. We will validate model predictions by kd down or inhibition of MF receptors for model predicted secreted factors. These studies will elucidate the mechanisms underlying SPRY2 regulation of GBM drug resistance and MF phenotypes that support tumor progression. Ultimately, this work has the potential to guide the development of rational combination therapies to improve GBM patient survival.!

Public Health Relevance

Outcomes for Glioblastoma Multiforme (GBM) patients can only be improved by identifying regulators of resistance to therapy whose roles are broad and robust in the face of typical escape routes GBM tumors utilize to evade treatment. We recently identified the protein SPRY2 as one such potential regulator of resistance, showing that it antagonizes GBM cell response to multiple classes of therapeutics and regulates GBM expression of secreted factors that may lead GBM-associated macrophages to support tumor growth. In this project, we will implement a computational and experimental systems biology approach to leverage these new observations for the rational design of robust combination therapies for GBM.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Postdoctoral Individual National Research Service Award (F32)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Jakowlew, Sonia B
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Virginia
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code