Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations

Area of Science:

• Immunology
• Systems Biology
• Oncology

Background:

• Cancer immunotherapy, particularly immune checkpoint blockade (ICB), has revolutionized cancer treatment.
• However, a significant proportion of patients do not respond to ICB therapies like anti-CTLA4.
• Predicting and improving response to immunotherapy remains a critical challenge.

Purpose of the Study:

• To investigate the potential of visualizing tumor effector response as a gene product interaction network.
• To identify network modules associated with response to anti-CTLA4 blockade.
• To demonstrate the efficacy of targeting these network modules for enhanced immunotherapy.

Main Methods:

• Utilized network analysis on gene expression profiling data from responding and non-responding murine mesothelioma tumors.
• Identified key gene modules and hub genes within the tumor immune microenvironment.
• Employed targeted modulation of identified modules and repurposed pharmaceuticals based on expression signatures.

Main Results:

• Successfully mapped the gene interaction network underlying tumor response to anti-CTLA4 blockade.
• Identified specific network modules significantly associated with treatment response.
• Demonstrated that targeting these modules, via hub gene modulation or repurposed drugs, dramatically enhanced anti-CTLA4 blockade efficacy in a preclinical model.

Conclusions:

• Network analysis of tumor gene expression provides a valid approach to predict and enhance immunotherapy response.
• Targeting identified network modules offers a powerful strategy for drug repurposing in cancer immunotherapy.
• This systems biology approach can define novel, contextually relevant therapeutic targets to overcome resistance to immune checkpoint blockade.