Tooluniverse Immunotherapy Response Prediction

This is a multi-biomarker integration tool for predicting whether cancer patients will respond to checkpoint inhibitors like pembrolizumab or nivolumab. It ingests tumor mutational burden, MSI status, PD-L1 expression, and resistance mutations, then outputs a 0-100 ICI Response Score with drug-specific recommendations. The workflow is thorough: it checks FDA biomarker approvals, applies cancer-specific thresholds (melanoma and NSCLC have different PD-L1 cutoffs), flags resistance genes like STK11 and JAK1/2, and distinguishes hot from cold tumors. Useful when you need to justify an immunotherapy decision with quantitative evidence rather than gut feel. The scoring tables and resistance penalty system are well documented, though you'll need actual patient biomarker data to get meaningful output.

skills.sh installs: 362
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-immunotherapy-response-prediction --agent claude-code

Installs into .claude/skills of the current project.

Files

Immunotherapy Response Prediction

Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.