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tooluniverse-immunotherapy-response-prediction
Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment. Use for melanoma/NSCLC/RCC immunotherapy decision support.
skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
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EXAMPLES.mddoc · 4,807 bytesINPUT_REFERENCE.mddoc · 1,585 bytesREPORT_TEMPLATE.mddoc · 5,146 bytesSCORING_TABLES.mddoc · 7,341 bytesSKILL.mdcore · 10,200 bytesSKILL.mdcore · 10,200 bytesTOOLS_REFERENCE.mddoc · 6,406 bytestest_skill.pyruns · 47,111 bytes
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# 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.
## Reasoning Before Searching
Not all tumors respond to checkpoint inhibitors. Reason through the biology before running tools:
- **TMB (tumor mutational burden)**: More somatic mutations produce more neoantigens, which are recognized by T cells. High TMB (>=10 mut/Mb, FDA-approved threshold for pembrolizumab) generally predicts better response — but this varies by cancer type (e.g., RCC responds despite low TMB).
- **MSI-H (microsatellite instability-high)**: Caused by defective DNA mismatch repair (MMR). MSI-H tumors have very high TMB and are pan-cancer approved for pembrolizumab. Check MLH1, MSH2, MSH6, PMS2 mutations.
- **PD-L1 expression**: The direct target of pembrolizumab/atezolizumab. High PD-L1 (TPS >=50% or CPS >=10 depending on cancer) predicts response in some cancers (NSCLC) but not all (melanoma, where TMB is more predictive).
- **Resistance factors** are equally important: STK11, KEAP1, JAK1/2 loss, B2M mutations can render an otherwise TMB-high tumor non-responsive.
Before calling any tool, determine which biomarkers are available for this patient and which are unknown. This determines which phases can be scored with data vs. must use cancer-type priors. Do not default to "moderate" for unknowns — flag them explicitly as missing.