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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.
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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.
LOOK UP DON'T GUESS: Never assume FDA approval for a biomarker-ICI combination — always verify with fda_pharmacogenomic_biomarkers or FDA_get_indications_by_drug_name. Cancer-specific thresholds differ from pan-cancer approvals.
KEY PRINCIPLES:
Report-first approach - Create report file FIRST, then populate progressively
Evidence-graded - Every finding has an evidence tier (T1-T4)
Resistance-aware - Always check for known resistance mutations (STK11, PTEN, JAK1/2, B2M)
Drug-specific - Recommend specific ICI agents with evidence
Source-referenced - Every statement cites the tool/database source
English-first queries - Always use English terms in tool calls
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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?"
"Compare pembrolizumab vs nivolumab for this patient profile"
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
See INPUT_REFERENCE.md for input format examples, cancer type normalization, and gene symbol normalization tables.
