Generalizable AI Predicts Immunotherapy Outcomes Across Cancers and Treatments

Concept Bottleneck Architecture: COMPASS transforms transcriptomic data into 44 high-level immune-related concepts (e.g., T cell exhaustion, IFN-γ signaling, macrophage activity) derived from 132 curated gene sets. This structure provides mechanistic interpretability while enabling pan-cancer modeling.

Pan-Cancer Pretraining and Flexible Fine-Tuning: Trained on 10,184 tumors across 33 cancer types using contrastive learning, and evaluated on 16 ICI-treated clinical cohorts (7 cancers, 6 ICI drugs). COMPASS supports full, partial, linear, and zero-shot fine-tuning modes, making it robust in both data-rich and data-poor settings.

Superior Generalization and Accuracy: In leave-one-cohort-out testing, COMPASS improved precision by 8.5%, AUPRC by 15.7%, and MCC by 12.3% over 22 baseline methods. It also outperformed in zero-shot settings, across drug classes (e.g., predicting anti-CTLA4 outcomes after training on anti-PD1), and in small-cohort fine-tuning.

Mechanistic Insight into Resistance: Personalized response maps reveal actionable biological mechanisms. For instance, inflamed non-responders show resistance via TGF-β signaling, vascular exclusion, CD4+ T cell dysfunction, or B cell deficiency. These go beyond classical “inflamed/desert/excluded” phenotypes, offering nuanced patient stratification.

Clinical Utility and Survival Stratification: COMPASS-predicted responders had significantly better survival in a held-out phase II bladder cancer trial (HR = 4.7, *p* = 1.7e-7), outperforming standard biomarkers (TMB, PD-L1 IHC, immune phenotype).