Resolution of tissue signatures of therapy response in patients with recurrent GBM treated with neoadjuvant anti-PD1

Area of Science:

• Oncology
• Immunology
• Computational Biology

Background:

• Interpreting patient responses to neoadjuvant immune checkpoint blockade (ICB) in recurrent glioblastoma multiforme (GBM) is challenging due to significant patient and tumor heterogeneity.
• Comparative analysis between GBM and high-risk melanoma treated with ICB offers a potential framework for understanding treatment response.

Purpose of the Study:

• To develop and apply a framework integrating multiplex spatial protein profiling, machine learning image analysis, and computational modeling.
• To investigate pathophysiological and molecular factors within the tumor microenvironment influencing ICB response in GBM and melanoma.
• To identify similarities and differences in the tumor microenvironment between GBM and melanoma to guide GBM treatment strategies.

Main Methods:

• Multiplex spatial protein profiling of tumor tissues from patients with recurrent GBM and high-risk melanoma.
• Machine learning-based image analysis to interrogate protein expression in microscopic tumor compartments.
• Data-driven computational models to analyze immune cell-cell interactions and treatment response correlates.

Main Results:

• Identified shared pathophysiological and molecular factors influencing ICB response in both GBM and melanoma.
• Revealed immunosuppressive factors unique to the GBM microenvironment.
• Established correlates of cytotoxic CD8+ T cell infiltration, tumor growth, and treatment response.

Conclusions:

• Melanoma serves as a valuable model for interpreting complex GBM immune responses to ICB.
• The GBM microenvironment presents unique immunosuppressive challenges distinct from melanoma.
• Identified potential co-targets for enhancing neoadjuvant ICB efficacy in glioblastoma.