netie

netie estimates historical neoantigen–CD8^+ T cell interactions in tumors using a hierarchical Bayesian model to infer immune selection pressure and its impact on tumor evolution.

Key Features:

• Hierarchical Bayesian Framework: Employs a hierarchical Bayesian model to infer the history of neoantigen–CD8^+ T cell interactions and immune selection pressure in tumors.
• Large-Scale Validation: Validated on 3,219 tumors across 18 different cancer types.
• Immune Selection Pressure Analysis: Identifies temporal patterns of immune selection pressure and correlates them with T cell activation–related expression signatures.
• Post-Immunotherapy Insights: Detects subsets of exhausted cytotoxic T cells emerging after immunotherapy and associates them with newly arising tumor clones post-treatment.
• Predictive Capabilities: Analyzes neoantigen imprints to predict future tumor progression and shows that T cell inflammation gene expression profiles (TIGEP) are more predictive of outcomes in tumors with increasing immune pressure, indicating synergies between T cells and neoantigen distributions.

Scientific Applications:

• Understanding Immune-Tumor Dynamics: Links individual neoantigen repertoires with tumor molecular profiles to elucidate how immune responses shape tumor evolution.
• Personalized Immunotherapy Strategies: Supports prediction of tumor response to immune pressure to inform personalized immunotherapy approaches.
• Research into Tumor Evolution: Provides insights into mechanisms of tumor adaptation and resistance within evolving immune landscapes.

Methodology:

Integrates PyClone, SciClone, and PhyloWGS estimation frameworks with a hierarchical Bayesian model to analyze neoantigen–T cell interactions.