BE
Benchling Integration：Benchling是一个cloud 平台 ，用于 life sciences R&D。 访问 registry entities (DNA，proteins)，inventory，electron…
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数据与复现
TO
tooluniverse-immunotherapy-response-prediction
维护者 FreedomIntelligence · 最近更新 2026年4月1日
tooluniverse-immunotherapy-response-prediction：预测 patient response to immune checkpoint inhibitors (ICIs) ，使用 multi-biomarker integration。 Given cancer type，somatic mutations，、 optional biomarkers (TMB，PD-L1，MSI status)，performs systematic analysis across 11 phases covering TMB 分类，neoantigen burden estimation，MSI/MMR assessment，PD-L1 evaluation，immune microenvironment profiling，mutation-based resistance/sensitivity prediction，cl…。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-immunotherapy-response-prediction
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- 预测 patient response to immune checkpoint inhibitors (ICIs) ，使用 multi-biomarker integration. Transforms patient tumor profile (cancer type + mutations + biomarkers) into quantitative ICI Response Score ，支持 drug-specific recommendations，resistance risk assessment，、 monitoring plan。
- KEY PRINCIPLES：1. Report-first approach - Create report file FIRST，then populate progressively 2. Evidence-graded - Every finding has evidence tier (T1-T4) 3. Quantitative output - ICI Response Score (0-100) ，支持 transparent component breakdown 4. Cancer-specific - All thresholds 、 predictions are cancer-type adjusted 5. Multi-biomarker - Integrate TMB + MSI + PD-L1 + neoantigen + mutations 6. Resistance-aware - Always check ，用于 known resistance mutations (STK11，PTEN，JAK1/2，B2M) 7. Drug-specific - Recommend specific ICI agents ，支持 evidence 8. Source-referenced - Every statement cites 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')。
原始文档
SKILL.md 摘录
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
| Format | Example | How 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 |
适用场景
- 适合在oncologists ask about immunotherapy eligibility，checkpoint inhibitor selection，或 biomarker-guided ICI treatment decisions时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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