Prediction of immunotherapy response using mutations to cancer protein assemblies

Area of Science:

• Oncology
• Immunology
• Genetics

Background:

• Immune checkpoint inhibitors offer revolutionary cancer therapy but have limited efficacy for many patients.
• Predicting patient response to immunotherapy remains a significant clinical challenge.

Purpose of the Study:

• To develop a more effective method for predicting immunotherapy response in bladder and non-small cell lung cancers.
• To identify specific molecular features that correlate with treatment outcomes.

Main Methods:

• Developed a novel metric, assembly-level mutation burden (AMB), by integrating tumor mutation burden (TMB) with mutations in specific protein assemblies.
• Validated AMB across multiple patient cohorts for bladder and non-small cell lung cancers.
• Corroborated findings through in vivo mouse models with engineered gene disruptions and histochemical analysis of tumor inflammation.

Main Results:

• Identified 13 specific protein assemblies where AMB effectively predicts immunotherapy treatment outcomes.
• Demonstrated that combining these assemblies significantly separates responders from non-responders (e.g., 76% vs. 37% 1-year survival in bladder cancer).
• Found that predicted responders exhibit elevated tumor inflammation.

Conclusions:

• Assembly-level mutation burden (AMB) offers a powerful predictive biomarker for immunotherapy response.
• The identified assemblies are involved in DNA damage checkpoints, oxidative stress, and JAK/STAT signaling.
• This approach provides a framework for leveraging tumor cell biology to optimize immunotherapy strategies.