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)
- Quantitative output - ICI Response Score (0-100) with transparent component breakdown
- Cancer-specific - All thresholds and predictions are cancer-type adjusted
- Multi-biomarker - Integrate TMB + MSI + PD-L1 + neoantigen + mutations
- 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.
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
Phase 1: Input Standardization & Cancer Context
- Resolve cancer type to EFO ID via
OpenTargets_get_disease_id_description_by_name
- Parse mutations into structured format:
{gene, variant, type}
- Resolve gene IDs via
MyGene_query_genes
- Look up cancer-specific ICI baseline ORR from the cancer context table (see SCORING_TABLES.md)
Phase 2: TMB Analysis
- Classify TMB: Very-Low (<5), Low (5-9.9), Intermediate (10-19.9), High (>=20)
- Check FDA TMB-H biomarker via
fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab')
- Apply cancer-specific TMB thresholds (see SCORING_TABLES.md)
- Note: RCC responds to ICIs despite low TMB; TMB is less predictive in some cancers
Phase 3: Neoantigen Analysis
- Estimate neoantigen burden: missense_count * 0.3 + frameshift_count * 1.5
- Check mutation impact via
UniProt_get_function_by_accession
- Query known epitopes via
iedb_search_epitopes
- POLE/POLD1 mutations indicate ultra-high neoantigen load
Phase 4: MSI/MMR Status Assessment
- Integrate MSI status if provided (MSI-H = 25 pts, MSS = 5 pts)
- Check mutations in MMR genes: MLH1, MSH2, MSH6, PMS2, EPCAM
- Check FDA MSI-H approvals via
fda_pharmacogenomic_biomarkers(biomarker='Microsatellite Instability')
Phase 5: PD-L1 Expression Analysis
- Classify PD-L1: High (>=50%), Positive (1-49%), Negative (<1%)
- Apply cancer-specific PD-L1 thresholds and scoring methods (TPS vs CPS)
- Get baseline expression via
HPA_get_cancer_prognostics_by_gene(gene_name='CD274')
Phase 6: Immune Microenvironment Profiling
- Query immune checkpoint gene expression for: CD274, PDCD1, CTLA4, LAG3, HAVCR2, TIGIT, CD8A, CD8B, GZMA, GZMB, PRF1, IFNG
- Classify tumor: Hot (T cell inflamed), Cold (immune desert), Immune excluded, Immune suppressed
- Run immune pathway enrichment via
enrichr_gene_enrichment_analysis
Phase 7: Mutation-Based Predictors
- Resistance mutations (apply PENALTIES): STK11 (-10), PTEN (-5), JAK1/2 (-10 each), B2M (-15), KEAP1 (-5), MDM2/4 (-5), EGFR (-5)
- Sensitivity mutations (apply BONUSES): POLE (+10), POLD1 (+5), BRCA1/2 (+3), ARID1A (+3), PBRM1 (+5 RCC only)
- Check CIViC and OpenTargets for driver mutation ICI context
- Check DDR pathway genes: ATM, ATR, CHEK1/2, BRCA1/2, PALB2, RAD50, MRE11
Phase 8: Clinical Evidence & ICI Options
- Query FDA indications for ICI drugs via
FDA_get_indications_by_drug_name
- Search clinical trials via
search_clinical_trials (params: condition, intervention, query_term)
- Search PubMed for biomarker-specific response data
- 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
- Check CIViC for resistance evidence via
civic_search_evidence_items
- Assess pathway-level resistance: IFN-g signaling, antigen presentation, WNT/b-catenin, MAPK, PI3K/AKT/mTOR
- 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
- ICI drug selection using cancer-specific algorithm (see SCORING_TABLES.md)
- Monitoring plan: CT/MRI q8-12wk, ctDNA at 4-6wk, thyroid/liver function, irAEs
- Alternative strategies if LOW response: targeted therapy, chemotherapy, ICI+chemo combo, ICI+anti-angiogenic, ICI+CTLA-4 combo, clinical trials
Output Report
Save as immunotherapy_response_prediction_{cancer_type}.md. See REPORT_TEMPLATE.md for the full report structure.
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 arrayensembl_lookup_gene: REQUIRES species='homo_sapiens'
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 |
References
