Netie: inferring the evolution of neoantigen-T cell interactions in tumors

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• Neoantigens are crucial targets for anti-tumor cytotoxic T cell responses.
• Understanding neoantigen-T cell interactions is vital for predicting tumor progression and immunotherapy efficacy.
• The evolutionary dynamics of these interactions within tumors remain largely unexplored.

Purpose of the Study:

• To develop a novel computational model for inferring historical neoantigen-CD8+ T cell interactions within tumors.
• To analyze molecular patterns across diverse cancer types and identify key evolutionary pressures.
• To investigate the relationship between neoantigen landscapes, T cell states, and patient outcomes.

Main Methods:

• Development of a hierarchical Bayesian model named neoantigen-T cell interaction estimation (netie).
• Systematic validation of netie using a dataset of 3,219 tumors from 18 cancer types.
• Analysis of T cell activation signatures, exhausted T cell subsets, and T cell inflammation gene expression profiles (TIGEP).

Main Results:

• Tumors experiencing increased immune selection pressure over time exhibit T cells with activation-related expression signatures.
• A subset of exhausted cytotoxic T cells is associated with novel tumor clones emerging post-immunotherapy.
• The T cell inflammation gene expression profile (TIGEP) predicts patient outcomes in tumors with rising immune pressure.

Conclusions:

• Netie provides a powerful tool to interrogate neoantigen-T cell dynamics and their impact on tumor evolution.
• Increased immune pressure correlates with T cell activation, while T cell exhaustion is linked to post-treatment tumor evolution.
• A synergy exists between T cell responses and neoantigen distribution, influencing patient prognosis and highlighting potential immunotherapy targets.