Immunotherapy Response Prediction

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

Phase 1: Input Standardization & Cancer Context
  - Resolve cancer type to EFO ID
  - Parse mutation list
  - Resolve genes to Ensembl/Entrez IDs
  - Get cancer-specific ICI baseline

Phase 2: TMB Analysis
  - TMB classification (low/intermediate/high)
  - Cancer-specific TMB thresholds
  - FDA TMB-H biomarker status

Phase 3: Neoantigen Analysis
  - Estimate neoantigen burden from mutations
  - Mutation type classification (missense/frameshift/nonsense)
  - Neoantigen quality indicators

Phase 4: MSI/MMR Status Assessment
  - MSI status integration
  - MMR gene mutation check
  - FDA MSI-H approval status

Phase 5: PD-L1 Expression Analysis
  - PD-L1 level classification
  - Cancer-specific PD-L1 thresholds
  - FDA-approved PD-L1 cutoffs

Phase 6: Immune Microenvironment Profiling
  - Immune checkpoint gene expression
  - Tumor immune classification (hot/cold)
  - Immune escape signatures

Phase 7: Mutation-Based Predictors
  - Driver mutation analysis
  - Resistance mutations (STK11, PTEN, JAK1/2, B2M)
  - Sensitivity mutations (POLE)
  - DNA damage repair pathway

Phase 8: Clinical Evidence & ICI Options
  - FDA-approved ICIs for this cancer
  - Clinical trial response rates
  - Drug mechanism comparison
  - Combination therapy evidence

Phase 9: Resistance Risk Assessment
  - Known resistance factors
  - Tumor immune evasion mechanisms
  - Prior treatment context

Phase 10: Multi-Biomarker Score Integration
  - Calculate ICI Response Score (0-100)
  - Component breakdown
  - Confidence level

Phase 11: Clinical Recommendations
  - ICI drug recommendation
  - Monitoring plan
  - Alternative strategies

# Get cancer EFO ID
result = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName='melanoma')
# -> {data: {search: {hits: [{id: 'EFO_0000756', name: 'melanoma', description: '...'}]}}}

"BRAF V600E" -> {gene: "BRAF", variant: "V600E", type: "missense"}
"TP53 R273H" -> {gene: "TP53", variant: "R273H", type: "missense"}
"STK11 loss" -> {gene: "STK11", variant: "loss of function", type: "loss"}

# For each gene in mutation list
result = tu.tools.MyGene_query_genes(query='BRAF')
# -> hits[0]: {_id: '673', symbol: 'BRAF', ensembl: {gene: 'ENSG00000157764'}}

# Check FDA TMB-H biomarker approval
result = tu.tools.fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab', limit=100)
# Look for "Tumor Mutational Burden" in Biomarker field
# -> Pembrolizumab approved for TMB-H (>=10 mut/Mb) tissue-agnostic

# Check mutation impact using UniProt
result = tu.tools.UniProt_get_function_by_accession(accession='P15056')  # BRAF UniProt
# Assess if mutation is in functional domain

# Check known epitopes for mutated proteins
result = tu.tools.iedb_search_epitopes(organism_name='homo sapiens', source_antigen_name='BRAF')
# Returns known epitopes, MHC restrictions

# Check FDA approvals for MSI-H
result = tu.tools.fda_pharmacogenomic_biomarkers(biomarker='Microsatellite Instability', limit=100)
# Pembrolizumab: tissue-agnostic for MSI-H/dMMR
# Nivolumab: CRC (MSI-H)
# Dostarlimab: dMMR solid tumors

# PD-L1 (CD274) expression patterns
result = tu.tools.HPA_get_cancer_prognostics_by_gene(gene_name='CD274')
# Cancer-type specific prognostic data

# Key immune genes to check
immune_genes = ['CD274', 'PDCD1', 'CTLA4', 'LAG3', 'HAVCR2', 'TIGIT', 'CD8A', 'CD8B', 'GZMA', 'GZMB', 'PRF1', 'IFNG']

# For each gene, get cancer-specific expression
for gene in immune_genes:
    result = tu.tools.HPA_get_cancer_prognostics_by_gene(gene_name=gene)

# If mutation list includes immune-related genes, do pathway analysis
result = tu.tools.enrichr_gene_enrichment_analysis(
    gene_list=['CD274', 'PDCD1', 'CTLA4', 'IFNG', 'CD8A'],
    libs=['KEGG_2021_Human', 'Reactome_2022']
)

# For each mutation, check CIViC evidence for ICI context
# Use OpenTargets for drug associations
result = tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId='EFO_0000756', size=50)
# Filter for ICI drugs (pembro, nivo, ipi, atezo, durva, avelumab, cemiplimab)

# Get FDA indications for key ICIs
ici_drugs = ['pembrolizumab', 'nivolumab', 'atezolizumab', 'durvalumab', 'ipilimumab', 'avelumab', 'cemiplimab']

for drug in ici_drugs:
    result = tu.tools.FDA_get_indications_by_drug_name(drug_name=drug, limit=3)
    # Extract cancer-specific indications

# Search for ICI trials in this cancer type
result = tu.tools.clinical_trials_search(
    action='search_studies',
    condition='melanoma',
    intervention='pembrolizumab',
    limit=10
)
# Returns: {total_count, studies: [{nctId, title, status, conditions}]}

# Search PubMed for biomarker-specific ICI response data
result = tu.tools.PubMed_search_articles(
    query='pembrolizumab melanoma TMB response biomarker',
    max_results=10
)
# Returns list of {pmid, title, ...}

# Get drug mechanism details
result = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId='CHEMBL3137343')
# -> pembrolizumab: PD-1 inhibitor, targets PDCD1 (ENSG00000188389)

# Check for resistance evidence in CIViC
# CIViC evidence types: PREDICTIVE, PROGNOSTIC, DIAGNOSTIC, PREDISPOSING, ONCOGENIC
result = tu.tools.civic_search_evidence_items(therapy_name='pembrolizumab')
# Filter for resistance-associated evidence

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

Where:
  TMB_score:        5-30 points (based on TMB classification)
  MSI_score:        5-25 points (based on MSI status)
  PDL1_score:       5-20 points (based on PD-L1 level)
  Neoantigen_score: 5-15 points (based on estimated neoantigens)
  Mutation_bonus:   0-10 points (POLE, PBRM1, etc.)
  Resistance_penalty: -20 to 0 points (STK11, PTEN, JAK1/2, B2M)

Minimum score: 0 (floor)
Maximum score: 100 (cap)

IF MSI-H:
  -> Pembrolizumab (tissue-agnostic FDA approval)
  -> Nivolumab (CRC-specific)
  -> Consider nivo+ipi combination

IF TMB-H (>=10) and not MSI-H:
  -> Pembrolizumab (tissue-agnostic for TMB-H)

IF Cancer = Melanoma:
  IF PD-L1 >= 1%: pembrolizumab or nivolumab monotherapy
  ELSE: nivolumab + ipilimumab combination
  IF BRAF V600E: consider targeted therapy first if rapid response needed

IF Cancer = NSCLC:
  IF PD-L1 >= 50% and no STK11/EGFR: pembrolizumab monotherapy
  IF PD-L1 1-49%: pembrolizumab + chemotherapy
  IF PD-L1 < 1%: ICI + chemotherapy combination
  IF STK11 loss: ICI less likely effective
  IF EGFR/ALK positive: targeted therapy preferred over ICI

IF Cancer = RCC:
  -> Nivolumab + ipilimumab (IMDC intermediate/poor risk)
  -> Pembrolizumab + axitinib (all risk)

IF Cancer = Bladder:
  -> Pembrolizumab or atezolizumab (2L)
  -> Avelumab maintenance post-platinum

# Immunotherapy Response Prediction Report

## Executive Summary
[2-3 sentence summary: cancer type, ICI Response Score, recommendation]

## ICI Response Score: XX/100
**Response Likelihood: [HIGH/MODERATE/LOW]**
**Confidence: [HIGH/MODERATE/LOW]**
**Expected ORR: XX-XX%**

### Score Breakdown
| Component | Value | Score | Max |
|-----------|-------|-------|-----|
| TMB | XX mut/Mb | XX | 30 |
| MSI Status | MSI-H/MSS | XX | 25 |
| PD-L1 | XX% | XX | 20 |
| Neoantigen Load | XX est. | XX | 15 |
| Sensitivity Bonus | +XX | XX | 10 |
| Resistance Penalty | -XX | XX | -20 |
| **TOTAL** | | **XX** | **100** |

## Patient Profile
- **Cancer Type**: [cancer]
- **Mutations**: [list]
- **TMB**: XX mut/Mb [classification]
- **MSI Status**: [MSI-H/MSS/Unknown]
- **PD-L1**: XX% [scoring method]

## Biomarker Analysis

### TMB Analysis
[TMB classification, cancer-specific context, FDA TMB-H status]

### MSI/MMR Status
[MSI status, MMR gene mutations, FDA MSI-H approvals]

### PD-L1 Expression
[PD-L1 level, cancer-specific thresholds, scoring method]

### Neoantigen Burden
[Estimated neoantigen count, quality assessment, mutation types]

## Mutation Analysis

### Driver Mutations
[Analysis of each mutation - oncogenic role, ICI implications]

### Resistance Mutations
[Any STK11, PTEN, JAK1/2, B2M, KEAP1 etc. with penalties]

### Sensitivity Mutations
[Any POLE, PBRM1, DDR genes with bonuses]

## Immune Microenvironment
[Hot/cold classification, immune gene expression data]

## ICI Drug Recommendation

### Primary Recommendation
**[Drug name]** - [monotherapy/combination]
- Evidence: [FDA approval, trial data]
- Expected response: XX-XX%
- Key trial: [trial name/NCT#]

### Alternative Options
1. [Alternative 1] - [rationale]
2. [Alternative 2] - [rationale]

### Combination Strategies
[ICI+ICI, ICI+chemo, ICI+targeted recommendations]

## Clinical Evidence
[Key trials, response rates, PFS/OS data for this cancer + biomarker profile]

## Resistance Risk
- **Risk Level**: [LOW/MODERATE/HIGH]
- **Key Factors**: [list resistance mutations/mechanisms]
- **Mitigation**: [combination strategies]

## Monitoring Plan
- **Response assessment**: [schedule]
- **Biomarkers to track**: [ctDNA, imaging, labs]
- **irAE monitoring**: [schedule]
- **Resistance monitoring**: [when to suspect progression]

## Alternative Strategies (if ICI unlikely effective)
[Targeted therapy, chemotherapy, clinical trials]

## Evidence Grading
| Finding | Evidence Tier | Source |
|---------|-------------|--------|
| [finding 1] | T1 (FDA/Guidelines) | [source] |
| [finding 2] | T2 (Clinical trial) | [source] |

## Data Completeness
| Biomarker | Status | Impact |
|-----------|--------|--------|
| TMB | Provided/Estimated/Unknown | XX points |
| MSI | Provided/Unknown | XX points |
| PD-L1 | Provided/Unknown | XX points |
| Neoantigen | Estimated | XX points |
| Mutations | X provided | +/-XX points |

## Missing Data Recommendations
[What additional tests would improve prediction accuracy]

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*Generated by ToolUniverse Immunotherapy Response Prediction Skill*
*Sources: OpenTargets, CIViC, FDA, DrugBank, PubMed, IEDB, HPA, cBioPortal*