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Massachusetts Institute of Technology

Signaling at the Tumor-Immune Interface in Glioblastoma

Abstract

dc:description.abstract

Glioblastoma (GBM) is a devastating brain cancer, and the standard of care has not changed in over 20 years. GBM tumors are composed of a milieu of cancer cells and innate immune cells, which are co-opted by the cancer cells to promote an anti-inflammatory environment. Despite tremendous success in immunotherapy in several cancers over the past 10 years, immunotherapies have failed to show efficacy in GBM. A systems biology approach to characterizing temporal changes in tumor-immune interface of glioblastoma could illuminate new strategies to activate an anti-tumor immune response by examining changes in cell signaling and antigen presentation. In the first part of my thesis, I investigated how macrophages alter their phenotype in response to tumor co-culture and how these changes are reflected at the level of the phosphoproteome. To characterize signaling changes in distinct cell populations during co-culture, I developed a method to preserve and analyze cell-type-specific signaling using fixation. This approach enables phosphoproteomic profiling of two interacting cell types, capturing dynamic signaling events with cell-type resolution. I applied this method to study co-cultures of glioblastoma (GBM) cells and primary human macrophages. When cultured together, GBM cells induced an anti-inflammatory, immunosuppressive phenotype in macrophages, mirroring features observed in the glioblastoma tumor microenvironment. Phosphoproteomic analysis revealed that this phenotypic shift was accompanied by distinct signaling alterations in macrophages, including the upregulation of ABL kinase activity. To test this finding, I treated macrophages with an ABL kinase inhibitor and observed a reduction in the anti-inflammatory phenotype, suggesting that ABL signaling plays a role in supporting immunosuppressive macrophage polarization. Furthermore, in a mouse model of GBM, treatment with an ABL kinase inhibitor led to a reduction in the abundance of anti-inflammatory macrophages within the tumor and was associated with a modest extension of survival. In the second part, I examined changes in antigen presentation and signaling in glioblastoma tumors in response to treatment with an oncolytic virus (OV). In patient derived tumor (PDX) models in mice, mice treated with OV have increased antigen presentation, pointing to the use of OV therapy to reshape the tumor micro-environment to a more inflammatory state. Finally, tissue obtained from serial biopsies of GBM patients treated with OV shows an increase in antigen presentation and both Class I and Class II MHC protein expression. We also observed an increase in interferon alpha and interferon gamma signaling pathways as well as early induction of apoptotic pathways. These findings highlight the role of therapeutics in altering the tumor microenvironment and potentially priming it for combination immunotherapies. This thesis explores the dynamic nature of the tumor and immune compartments in glioblastoma and underscores how therapies can act on the immune compartment to promote anti-tumor activity.

Degree

thesis:*
Name thesis:degree_name
Doctoral
Department dc:contributor.department
Harvard-MIT Program in Health Sciences and Technology
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2025

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • D'Souza, Alicia D.
Advisor dc:contributor.advisor
  • White, Forest M.

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/164857
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/164857

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

D'Souza, Alicia D.. Signaling at the Tumor-Immune Interface in Glioblastoma. Massachusetts Institute of Technology, 2025. https://hdl.handle.net/1721.1/164857