Computational framework for prioritizing candidate compounds overcoming the resistance of pancancer immunotherapy

Area of Science:

• Computational biology
• Cancer research
• Immunotherapy

Background:

• Immunotherapy resistance is a major challenge in cancer treatment.
• Few synergistic drugs have been identified to enhance immunotherapy efficacy.

Purpose of the Study:

• To develop a computational framework (IGeS-BS) for identifying compounds that overcome immunotherapy resistance.
• To generate an immuno-boosting landscape for drug discovery.

Main Methods:

• Meta-analysis of ~1,000 transcriptomes to identify tumor microenvironment (TME) signatures predictive of immunotherapy response.
• Generation of an immuno-boosting landscape using TCGA and LINCS datasets for >10,000 compounds and 13 cancer types.
• In vitro and in vivo validation of high-scoring compounds in hepatocellular carcinoma and other cancers.

Main Results:

• Identified 33 TME signatures for robust immunotherapy response prediction.
• Discovered two compounds, SB-366791 and CGP-60474, that significantly alleviate hepatocellular carcinoma resistance to anti-PD1 therapy.
• Demonstrated that these compounds enhance immunotherapy by activating immune cells.

Conclusions:

• The IGeS-BS framework efficiently identifies compounds to enhance immunotherapy responses.
• SB-366791 and CGP-60474 are promising candidates for combination therapy to overcome immunotherapy resistance.
• This approach offers a valuable tool for discovering novel immuno-boosting agents.