Immunotherapy Response Prediction (ToolUniverse Claude Skill)

A ToolUniverse agent skill that predicts a patient’s response to immune checkpoint inhibitors by integrating tumor mutational burden, microsatellite instability, PD-L1 expression, HLA status, and immune-related gene expression into a scored, evidence-graded report.

   
Type Claude Skill (one of ToolUniverse’s pre-built agent skills)
Supplier Zitnik Lab, Harvard Medical School
Availability GA — part of the ToolUniverse skills collection (skills/tooluniverse-immunotherapy-response-prediction/)
Pricing Free / OSS (Apache-2.0); wraps public APIs (OpenTargets, CIViC, FDA pharmacogenomics, Human Protein Atlas, IEDB, Enrichr, ClinicalTrials.gov, PubMed)
Capabilities Read-only — drives ToolUniverse tool calls; no data writes
Verified works · 2026-07-20
Security cleared · 2026-07-20 — provenance matches Zitnik Lab, Apache-2.0, skill dir confirmed, no OSV advisories

How to install

This skill calls ToolUniverse tools, so the ToolUniverse MCP server must be installed first (see the ToolUniverse page). Simplest registration:

claude mcp add --transport stdio tooluniverse -- uvx tooluniverse

Then add the skills:

  • Claude Code — install the whole skill collection (the skill resolves as tooluniverse-immunotherapy-response-prediction): 
    npx skills add mims-harvard/ToolUniverse
  • Manual / other agents — copy just this skill directory into your skills folder: 
    git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-immunotherapy-response-prediction ~/.claude/skills/

    (replace ~/.claude/skills/ with your agent’s skills directory if you are not using Claude Code/Desktop.)

The skill sets disable-model-invocation: true upstream, so invoke it explicitly (e.g. ask Claude to “use the immunotherapy-response-prediction skill”) rather than relying on automatic dispatch.

What it does

Transforms a tumor profile into an ICI Response Score across eleven phases:

  1. Phases 1–4 — input standardization, TMB classification, neoantigen estimation, and MMR/MSI assessment with cancer-type-specific thresholds.
  2. Phases 5–7 — PD-L1 expression analysis, immune-microenvironment profiling, and mutation-based predictor evaluation (resistance vs. sensitivity mutations).
  3. Phases 8–11 — clinical-evidence synthesis, resistance risk stratification, multi-biomarker score integration (0–100), and drug-specific recommendations.

Key integrations: OpenTargets / MyGene / Ensembl (disease/gene), FDA pharmacogenomics + HPA cancer prognostics (biomarker validation), CIViC / UniProt / EnsemblVEP (mutation analysis), IEDB + Enrichr (immune profiling), FDA indications + trial search + PubMed (clinical evidence).

Primary use cases: checkpoint-inhibitor eligibility triage, multi-biomarker immunotherapy scoring, resistance-factor review.

Notes

It is a reasoning layer over ToolUniverse; without the MCP server registered, the tool calls fail. Output is report-first with evidence-graded (T1–T4) component scoring and cancer-specific thresholds; it is a research aid, not a clinical decision tool. ToolUniverse ships ~150 such skills; other oncology and drug-discovery workflows are catalogued separately.

Sources

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