tooluniverse-immunotherapy-response-prediction

name	tooluniverse-immunotherapy-response-prediction
description	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.
disable-model-invocation	true

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:

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

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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"

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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.

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Workflow Overview

Input: Cancer type + Mutations/TMB + Optional biomarkers (PD-L1, MSI, etc.)

Phase 1: Input Standardization & Cancer Context
Phase 2: TMB Analysis
Phase 3: Neoantigen Analysis
Phase 4: MSI/MMR Status Assessment
Phase 5: PD-L1 Expression Analysis
Phase 6: Immune Microenvironment Profiling
Phase 7: Mutation-Based Predictors
Phase 8: Clinical Evidence & ICI Options
Phase 9: Resistance Risk Assessment
Phase 10: Multi-Biomarker Score Integration
Phase 11: Clinical Recommendations

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Phase 1: Input Standardization & Cancer Context

1. Resolve cancer type to EFO ID via OpenTargets_get_disease_id_description_by_name
2. Parse mutations into structured format: {gene, variant, type}
3. Resolve gene IDs via MyGene_query_genes
4. Look up cancer-specific ICI baseline ORR from the cancer context table (see SCORING_TABLES.md)

Phase 2: TMB Analysis

1. Classify TMB: Very-Low (<5), Low (5-9.9), Intermediate (10-19.9), High (>=20)
2. Check FDA TMB-H biomarker via fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab')
3. Apply cancer-specific TMB thresholds (see SCORING_TABLES.md)
4. Note: RCC responds to ICIs despite low TMB; TMB is less predictive in some cancers

Phase 3: Neoantigen Analysis

1. Estimate neoantigen burden: missense_count * 0.3 + frameshift_count * 1.5
2. Check mutation impact via UniProt_get_function_by_accession
3. Query known epitopes via iedb_search_epitopes
4. POLE/POLD1 mutations indicate ultra-high neoantigen load

Phase 4: MSI/MMR Status Assessment

1. Integrate MSI status if provided (MSI-H = 25 pts, MSS = 5 pts)
2. Check mutations in MMR genes: MLH1, MSH2, MSH6, PMS2, EPCAM
3. Check FDA MSI-H approvals via fda_pharmacogenomic_biomarkers(biomarker='Microsatellite Instability')

Phase 5: PD-L1 Expression Analysis

1. Classify PD-L1: High (>=50%), Positive (1-49%), Negative (<1%)
2. Apply cancer-specific PD-L1 thresholds and scoring methods (TPS vs CPS)
3. Get baseline expression via HPA_get_cancer_prognostics_by_gene(gene_name='CD274')

Phase 6: Immune Microenvironment Profiling

1. Query immune checkpoint gene expression for: CD274, PDCD1, CTLA4, LAG3, HAVCR2, TIGIT, CD8A, CD8B, GZMA, GZMB, PRF1, IFNG
2. Classify tumor: Hot (T cell inflamed), Cold (immune desert), Immune excluded, Immune suppressed
3. Run immune pathway enrichment via enrichr_gene_enrichment_analysis

Phase 7: Mutation-Based Predictors

1. Resistance mutations (apply PENALTIES): STK11 (-10), PTEN (-5), JAK1/2 (-10 each), B2M (-15), KEAP1 (-5), MDM2/4 (-5), EGFR (-5)
2. Sensitivity mutations (apply BONUSES): POLE (+10), POLD1 (+5), BRCA1/2 (+3), ARID1A (+3), PBRM1 (+5 RCC only)
3. Check CIViC and OpenTargets for driver mutation ICI context
4. Check DDR pathway genes: ATM, ATR, CHEK1/2, BRCA1/2, PALB2, RAD50, MRE11

Phase 8: Clinical Evidence & ICI Options

1. Query FDA indications for ICI drugs via FDA_get_indications_by_drug_name
2. Search clinical trials via search_clinical_trials (params: condition, intervention, query_term)
3. Search PubMed for biomarker-specific response data
4. Get drug mechanisms via OpenTargets_get_drug_mechanisms_of_action_by_chemblId

See SCORING_TABLES.md for ICI drug profiles and ChEMBL IDs.

Phase 9: Resistance Risk Assessment

1. Check CIViC for resistance evidence via civic_search_evidence_items
2. Assess pathway-level resistance: IFN-g signaling, antigen presentation, WNT/b-catenin, MAPK, PI3K/AKT/mTOR
3. Summarize risk: Low / Moderate / High

Phase 10: Multi-Biomarker Score Integration

TOTAL SCORE = TMB_score + MSI_score + PDL1_score + Neoantigen_score + Mutation_bonus + Resistance_penalty

TMB_score:        5-30 points     MSI_score:        5-25 points
PDL1_score:       5-20 points     Neoantigen_score: 5-15 points
Mutation_bonus:   0-10 points     Resistance_penalty: -20 to 0 points

Floor: 0, Cap: 100

Response Likelihood Tiers:

• 70-100 HIGH (50-80% ORR): Strong ICI candidate
• 40-69 MODERATE (20-50% ORR): Consider ICI, combo preferred
• 0-39 LOW (<20% ORR): ICI alone unlikely effective

Confidence: HIGH (all 4 biomarkers), MODERATE-HIGH (3/4), MODERATE (2/4), LOW (1), VERY LOW (cancer only)

Phase 11: Clinical Recommendations

1. ICI drug selection using cancer-specific algorithm (see SCORING_TABLES.md)
2. Monitoring plan: CT/MRI q8-12wk, ctDNA at 4-6wk, thyroid/liver function, irAEs
3. Alternative strategies if LOW response: targeted therapy, chemotherapy, ICI+chemo combo, ICI+anti-angiogenic, ICI+CTLA-4 combo, clinical trials

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Output Report

Save as immunotherapy_response_prediction_{cancer_type}.md. See REPORT_TEMPLATE.md for the full report structure.

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Tool Parameter Reference

BEFORE calling ANY tool, verify parameters. See TOOLS_REFERENCE.md for verified tool parameters table.

Key reminders:

• MyGene_query_genes: use query (NOT q)
• EnsemblVEP_annotate_rsid: use variant_id (NOT rsid)
• drugbank_* tools: ALL 4 params required (query, case_sensitive, exact_match, limit)
• cBioPortal_get_mutations: gene_list is a STRING not array
• ensembl_lookup_gene: REQUIRES species='homo_sapiens'

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Evidence Tiers

Tier	Description	Source Examples
T1	FDA-approved biomarker/indication	FDA labels, NCCN guidelines
T2	Phase 2-3 clinical trial evidence	Published trial data, PubMed
T3	Preclinical/computational evidence	Pathway analysis, in vitro data
T4	Expert opinion/case reports	Case series, reviews

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References

• OpenTargets: https://platform.opentargets.org
• CIViC: https://civicdb.org
• FDA Drug Labels: https://dailymed.nlm.nih.gov
• DrugBank: https://go.drugbank.com
• PubMed: https://pubmed.ncbi.nlm.nih.gov
• IEDB: https://www.iedb.org
• HPA: https://www.proteinatlas.org
• cBioPortal: https://www.cbioportal.org