PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not specified in abstract

• Primary Outcome: Superior predictive performance for immunotherapy response compared to established biomarkers

• Nnt Or Sensitivity: Sensitivity and specificity metrics not provided

• Confidence Interval: Not reported

Clinical Application

Requires genomic sequencing data and computational infrastructure; may be feasible in major cancer centres Could complement existing PBS criteria for immunotherapy eligibility; relevant to Australian cancer centres with genomic medicine capabilities and may align with Genomics Health Futures Mission initiatives Cancer patients being considered for immunotherapy, particularly those where current biomarkers are insufficient

Abstract

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient subsets. Here, we present PathTIGR, a pathway topology-informed graph representation learning framework that systematically integrates biological pathway network topology knowledge with genome variation information for immunotherapy response prediction. PathTIGR uses a three-component design: (i) pathway graph encoder with multihead attention embeding pathway topology knowledge and cancer genomic variants to pathway representation, (ii) transformer module capturing pathway regulatory dependencies, and (iii) multilayer perceptron synthesizing pathway-level representations to predict immunotherapy response. This architecture enables PathTIGR to capture complex molecular interactions underlying immunotherapy response. Comprehensive validation across multiple independent immunotherapy cohorts demonstrates that PathTIGR achieves superior predictive performance compared to established biomarkers and state-of-the-art deep learning approaches while maintaining biological interpretability through identification of key signatures underlying response heterogeneity. PathTIGR represents an interpretable graph-based learning framework that enhances immunotherapy response prediction and elucidates molecular determinants of therapeutic efficacy, thereby facilitating the advancement of precision cancer immunotherapy.

References

1. 1.Li, X., He, Y., Wu, J., Wang, Z., Zhao, X., Zhang, Y., Pan, B., Tang, Y., & Han, J. (2026). PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction. Science Advances. https://doi.org/10.1126/sciadv.aed6373