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tooluniverse-immunotherapy-response-prediction

Maintainer FreedomIntelligence · Last updated April 1, 2026

Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Given a cancer type, somatic mutations, and optional biomarkers (TMB, PD-L1, MSI status), performs systematic analysis across 11 phases covering TMB classification, neoantigen burden estimation, MSI/MMR assessment, PD-L1 evaluation, immune microenvironment profiling, mutation-based resistance/sensitivity prediction, cl….

OpenClawNanoClawAnalysisReproductiontooluniverse-immunotherapy-response-prediction🏥 medical & clinicalmedical toolspredict

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-immunotherapy-response-prediction

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • 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.
  • KEY PRINCIPLES: 1. Report-first approach - Create report file FIRST, then populate progressively 2. Evidence-graded - Every finding has an evidence tier (T1-T4) 3. Quantitative output - ICI Response Score (0-100) with transparent component breakdown 4. Cancer-specific - All thresholds and predictions are cancer-type adjusted 5. Multi-biomarker - Integrate TMB + MSI + PD-L1 + neoantigen + mutations 6. Resistance-aware - Always check for known resistance mutations (STK11, PTEN, JAK1/2, B2M) 7. Drug-specific - Recommend specific ICI agents with evidence 8. Source-referenced - Every statement cites the tool/database source 9. English-first queries - Always use English terms in tool calls.
  • result = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName='melanoma').

Source Doc

Excerpt From SKILL.md

When to Use

Apply when user asks:

  • "Will this patient respond to immunotherapy?"
  • "Should I give pembrolizumab to this melanoma patient?"
  • "Patient has NSCLC with TMB 25, PD-L1 80% - predict ICI response"
  • "MSI-high colorectal cancer - which checkpoint inhibitor?"
  • "Patient has BRAF V600E melanoma, TMB 15 - immunotherapy or targeted?"
  • "Low TMB NSCLC with STK11 mutation - should I try immunotherapy?"
  • "Compare pembrolizumab vs nivolumab for this patient profile"
  • "What biomarkers predict checkpoint inhibitor response?"

Input Parsing

Required: Cancer type + at least one of: mutation list OR TMB value Optional: PD-L1 expression, MSI status, immune infiltration data, HLA type, prior treatments, intended ICI

Accepted Input Formats

FormatExampleHow to Parse
Cancer + mutations"Melanoma, BRAF V600E, TP53 R273H"cancer=melanoma, mutations=[BRAF V600E, TP53 R273H]
Cancer + TMB"NSCLC, TMB 25 mut/Mb"cancer=NSCLC, tmb=25
Cancer + full profile"Melanoma, BRAF V600E, TMB 15, PD-L1 50%, MSS"cancer=melanoma, mutations=[BRAF V600E], tmb=15, pdl1=50, msi=MSS
Cancer + MSI status"Colorectal cancer, MSI-high"cancer=CRC, msi=MSI-H
Resistance query"NSCLC, TMB 2, STK11 loss, PD-L1 <1%"cancer=NSCLC, tmb=2, mutations=[STK11 loss], pdl1=0
ICI selection"Which ICI for NSCLC PD-L1 90%?"cancer=NSCLC, pdl1=90, query_type=drug_selection

Use cases

  • Use when oncologists ask about immunotherapy eligibility, checkpoint inhibitor selection, or biomarker-guided ICI treatment decisions.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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