Agent skill

tooluniverse-immunotherapy-response-prediction

Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Given a cancer type, somatic mutations, and optional biomarkers (TMB, PD-L1, MSI status), performs systematic analysis across 11 phases covering TMB classification, neoantigen burden estimation, MSI/MMR assessment, PD-L1 evaluation, immune microenvironment profiling, mutation-based resistance/sensitivity prediction, clinical evidence retrieval, and multi-biomarker score integration. Generates a quantitative ICI Response Score (0-100), response likelihood tier, specific ICI drug recommendations with evidence, resistance risk factors, and a monitoring plan. Use when oncologists ask about immunotherapy eligibility, checkpoint inhibitor selection, or biomarker-guided ICI treatment decisions.

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SKILL.md

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.

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

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?"
  • "Low TMB NSCLC with STK11 mutation - should I try immunotherapy?"
  • "Compare pembrolizumab vs nivolumab for this patient profile"
  • "What biomarkers predict checkpoint inhibitor response?"

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

Accepted Input Formats

Format Example How to Parse
Cancer + mutations "Melanoma, BRAF V600E, TP53 R273H" cancer=melanoma, mutations=[BRAF V600E, TP53 R273H]
Cancer + TMB "NSCLC, TMB 25 mut/Mb" cancer=NSCLC, tmb=25
Cancer + full profile "Melanoma, BRAF V600E, TMB 15, PD-L1 50%, MSS" cancer=melanoma, mutations=[BRAF V600E], tmb=15, pdl1=50, msi=MSS
Cancer + MSI status "Colorectal cancer, MSI-high" cancer=CRC, msi=MSI-H
Resistance query "NSCLC, TMB 2, STK11 loss, PD-L1 <1%" cancer=NSCLC, tmb=2, mutations=[STK11 loss], pdl1=0
ICI selection "Which ICI for NSCLC PD-L1 90%?" cancer=NSCLC, pdl1=90, query_type=drug_selection

Cancer Type Normalization

Common aliases to resolve:

  • NSCLC -> non-small cell lung carcinoma
  • SCLC -> small cell lung carcinoma
  • CRC -> colorectal cancer
  • RCC -> renal cell carcinoma
  • HNSCC -> head and neck squamous cell carcinoma
  • UC / bladder -> urothelial carcinoma
  • HCC -> hepatocellular carcinoma
  • TNBC -> triple-negative breast cancer
  • GEJ -> gastroesophageal junction cancer

Gene Symbol Normalization

  • PD-L1 -> CD274
  • PD-1 -> PDCD1
  • CTLA-4 -> CTLA4
  • HER2 -> ERBB2
  • MSH2/MLH1/MSH6/PMS2 -> MMR genes

Phase 0: Tool Parameter Reference (CRITICAL)

BEFORE calling ANY tool, verify parameters using this reference table.

Verified Tool Parameters

Tool Parameters Notes
OpenTargets_get_disease_id_description_by_name diseaseName Returns {data: {search: {hits: [{id, name, description}]}}}
OpenTargets_get_drug_id_description_by_name drugName Returns {data: {search: {hits: [{id, name, description}]}}}
OpenTargets_get_associated_drugs_by_disease_efoId efoId, size Returns {data: {disease: {knownDrugs: {count, rows}}}}
OpenTargets_get_drug_mechanisms_of_action_by_chemblId chemblId Returns {data: {drug: {mechanismsOfAction: {rows}}}}
OpenTargets_get_approved_indications_by_drug_chemblId chemblId Approved indications list
OpenTargets_get_drug_description_by_chemblId chemblId Drug description text
OpenTargets_get_associated_targets_by_drug_chemblId chemblId Drug targets
MyGene_query_genes query (NOT q) Returns {hits: [{_id, symbol, name, ensembl: {gene}}]}
ensembl_lookup_gene gene_id, species='homo_sapiens' REQUIRES species. Returns {data: {id, display_name}}
EnsemblVEP_annotate_rsid variant_id (NOT rsid) VEP annotation with SIFT/PolyPhen
civic_search_evidence_items therapy_name, disease_name Returns {data: {evidenceItems: {nodes}}} - may not filter accurately
civic_search_variants name, gene_name Returns {data: {variants: {nodes}}} - returns many unrelated variants
civic_get_variants_by_gene gene_id (CIViC numeric ID) Requires CIViC gene ID, NOT Entrez
civic_search_assertions therapy_name, disease_name Returns {data: {assertions: {nodes}}}
civic_search_therapies name Search therapies by name
cBioPortal_get_mutations study_id, gene_list (string) gene_list is a STRING not array
cBioPortal_get_cancer_studies (no params needed) May fail with keyword param
drugbank_get_drug_basic_info_by_drug_name_or_id query, case_sensitive, exact_match, limit ALL 4 REQUIRED
drugbank_get_targets_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit ALL 4 REQUIRED
drugbank_get_pharmacology_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit ALL 4 REQUIRED
drugbank_get_indications_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit ALL 4 REQUIRED
FDA_get_indications_by_drug_name drug_name, limit Returns {meta, results}
FDA_get_clinical_studies_info_by_drug_name drug_name, limit Returns {meta, results}
FDA_get_adverse_reactions_by_drug_name drug_name, limit Returns {meta, results}
FDA_get_mechanism_of_action_by_drug_name drug_name, limit Returns {meta, results}
FDA_get_boxed_warning_info_by_drug_name drug_name, limit May return NOT_FOUND
FDA_get_warnings_by_drug_name drug_name, limit Returns {meta, results}
fda_pharmacogenomic_biomarkers drug_name, biomarker, limit Returns {count, shown, results: [{Drug, Biomarker, TherapeuticArea, LabelingSection}]}
clinical_trials_search action='search_studies', condition, intervention, limit Returns {total_count, studies}
clinical_trials_get_details action='get_study_details', nct_id Full study object
search_clinical_trials query_term (REQUIRED), condition, intervention, pageSize Returns {studies, total_count}
PubMed_search_articles query, max_results Returns plain list of dicts
UniProt_get_function_by_accession accession Returns list of strings
UniProt_get_disease_variants_by_accession accession Disease-associated variants
HPA_get_rna_expression_by_source gene_name, source_type, source_name ALL 3 REQUIRED
HPA_get_cancer_prognostics_by_gene gene_name Cancer prognostic data
iedb_search_epitopes organism_name, source_antigen_name Returns {status, data, count}
iedb_search_mhc various MHC binding data
enrichr_gene_enrichment_analysis gene_list (array), libs (array, REQUIRED) Key libs: KEGG_2021_Human, Reactome_2022
PharmGKB_get_clinical_annotations query Clinical annotations
gnomad_get_gene_constraints gene_symbol Gene constraint metrics

Workflow Overview

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

Phase 1: Input Standardization & Cancer Context

Step 1.1: Resolve Cancer Type

python
# 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: '...'}]}}}

Cancer-specific ICI context (hardcoded knowledge base):

Cancer Type EFO ID Baseline ICI ORR Key Biomarkers FDA-Approved ICIs
Melanoma EFO_0000756 30-45% TMB, PD-L1 pembro, nivo, ipi, nivo+ipi, nivo+rela
NSCLC EFO_0003060 15-50% (PD-L1 dependent) PD-L1, TMB, STK11 pembro, nivo, atezo, durva, cemiplimab
Bladder/UC EFO_0000292 15-25% PD-L1, TMB pembro, nivo, atezo, avelumab, durva
RCC EFO_0000681 25-40% PD-L1 nivo, pembro, nivo+ipi, nivo+cabo, pembro+axitinib
HNSCC EFO_0000181 15-20% PD-L1 CPS pembro, nivo
MSI-H (any) N/A 30-50% MSI, dMMR pembro (tissue-agnostic)
TMB-H (any) N/A 20-30% TMB >=10 pembro (tissue-agnostic)
CRC (MSI-H) EFO_0000365 30-50% MSI, dMMR pembro, nivo, nivo+ipi
CRC (MSS) EFO_0000365 <5% Generally poor Generally not recommended
HCC EFO_0000182 15-20% PD-L1 atezo+bev, durva+treme, nivo+ipi
TNBC EFO_0005537 10-20% PD-L1 CPS pembro+chemo
Gastric/GEJ EFO_0000178 10-20% PD-L1 CPS, MSI pembro, nivo

Step 1.2: Parse Mutations

Parse each mutation into structured format:

"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"}

Step 1.3: Resolve Gene IDs

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

Phase 2: TMB Analysis

Step 2.1: TMB Classification

If TMB value provided directly, classify:

TMB Range Classification ICI Score Component
>= 20 mut/Mb TMB-High 30 points
10-19.9 mut/Mb TMB-Intermediate 20 points
5-9.9 mut/Mb TMB-Low 10 points
< 5 mut/Mb TMB-Very-Low 5 points

If only mutations provided, estimate TMB:

  • Count total mutations provided
  • Note: User-provided lists are typically key mutations, not full exome
  • Flag as "estimated from provided mutations - clinical TMB testing recommended"

Step 2.2: TMB FDA Context

python
# 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

Step 2.3: Cancer-Specific TMB Thresholds

Cancer Type Typical TMB Range High-TMB Threshold Notes
Melanoma 5-50+ >20 High baseline TMB; UV-induced
NSCLC 2-30 >10 Smoking-related; FDA cutoff 10
Bladder 5-25 >10 Moderate baseline
CRC (MSI-H) 20-100+ >10 Very high in MSI-H
CRC (MSS) 2-10 >10 Generally low
RCC 1-8 >10 Low TMB but ICI-responsive
HNSCC 2-15 >10 Moderate

IMPORTANT: RCC responds to ICIs despite low TMB. TMB is less predictive in some cancers.

Phase 3: Neoantigen Analysis

Step 3.1: Neoantigen Burden Estimation

From mutation list:

  • Missense mutations -> Each has ~20-50% chance of generating a neoantigen
  • Frameshift mutations -> High neoantigen-generating potential (novel peptides)
  • Nonsense mutations -> Moderate potential (truncated proteins)
  • Splice site mutations -> Moderate potential (aberrant peptides)

Estimate: neoantigen_count ~= missense_count * 0.3 + frameshift_count * 1.5

Step 3.2: Neoantigen Quality Assessment

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

Quality indicators:

  • Mutations in protein kinase domains -> high immunogenicity potential
  • Mutations in surface-exposed regions -> better MHC presentation
  • POLE/POLD1 mutations -> ultra-high neoantigen load (ultramutated)

Step 3.3: IEDB Epitope Data (if relevant)

python
# 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

Neoantigen Score Component

Estimated Neoantigen Load Classification Score
>50 neoantigens High 15 points
20-50 neoantigens Moderate 10 points
<20 neoantigens Low 5 points

Phase 4: MSI/MMR Status Assessment

Step 4.1: MSI Status Integration

If MSI status provided directly:

MSI Status Classification Score Component
MSI-H / dMMR MSI-High 25 points
MSS / pMMR Microsatellite Stable 5 points
Unknown Not tested 10 points (neutral)

Step 4.2: MMR Gene Mutation Check

Check if any provided mutations are in MMR genes:

  • MLH1 (ENSG00000076242) - mismatch repair
  • MSH2 (ENSG00000095002) - mismatch repair
  • MSH6 (ENSG00000116062) - mismatch repair
  • PMS2 (ENSG00000122512) - mismatch repair
  • EPCAM (ENSG00000119888) - can silence MSH2

If MMR gene mutations found but MSI status not provided -> flag as "possible MSI-H, recommend testing"

Step 4.3: FDA MSI-H Approvals

python
# 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

Phase 5: PD-L1 Expression Analysis

Step 5.1: PD-L1 Level Classification

PD-L1 Level Classification Score Component
>= 50% (TPS) PD-L1 High 20 points
1-49% (TPS) PD-L1 Positive 12 points
< 1% (TPS) PD-L1 Negative 5 points
Unknown Not tested 10 points (neutral)

Step 5.2: Cancer-Specific PD-L1 Thresholds

Cancer Scoring Method Key Thresholds ICI Monotherapy Recommended?
NSCLC TPS >=50%: first-line mono; >=1%: after chemo Yes at >=50%, combo at >=1%
Melanoma Not routinely required N/A Yes regardless of PD-L1
Bladder CPS or IC CPS>=10 preferred Yes with PD-L1 positive
HNSCC CPS CPS>=1: pembro; CPS>=20: mono preferred CPS>=20 for monotherapy
Gastric CPS CPS>=1 Pembro+chemo
TNBC CPS CPS>=10 Pembro+chemo

Step 5.3: PD-L1 Gene Expression (Baseline Reference)

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

Phase 6: Immune Microenvironment Profiling

Step 6.1: Key Immune Checkpoint Genes

Query expression data for immune microenvironment markers:

python
# 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)

Step 6.2: Tumor Immune Classification

Based on available data, classify:

Classification Characteristics ICI Likelihood
Hot (T cell inflamed) High CD8+ T cells, IFN-g, PD-L1+ High response
Cold (immune desert) Low immune infiltration Low response
Immune excluded Immune cells at margin, not infiltrating Moderate response
Immune suppressed High Tregs, MDSCs, immunosuppressive Low-moderate

Step 6.3: Immune Pathway Enrichment

python
# 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']
)

Phase 7: Mutation-Based Predictors

Step 7.1: ICI-Resistance Mutations (CRITICAL)

Known resistance mutations - apply PENALTIES:

Gene Mutation Cancer Context Mechanism Penalty
STK11/LKB1 Loss/inactivation NSCLC (esp. KRAS+) Immune exclusion, cold TME -10 points
PTEN Loss/deletion Multiple Reduced T cell infiltration -5 points
JAK1 Loss of function Multiple IFN-g signaling loss -10 points
JAK2 Loss of function Multiple IFN-g signaling loss -10 points
B2M Loss/mutation Multiple MHC-I loss, immune escape -15 points
KEAP1 Loss/mutation NSCLC Oxidative stress, cold TME -5 points
MDM2 Amplification Multiple Hyperprogression risk -5 points
MDM4 Amplification Multiple Hyperprogression risk -5 points
EGFR Activating mutation NSCLC Low TMB, cold TME -5 points

Step 7.2: ICI-Sensitivity Mutations (BONUS)

Gene Mutation Cancer Context Mechanism Bonus
POLE Exonuclease domain Any Ultramutation, high neoantigens +10 points
POLD1 Proofreading domain Any Ultramutation +5 points
BRCA1/2 Loss of function Multiple Genomic instability +3 points
ARID1A Loss of function Multiple Chromatin remodeling, TME +3 points
PBRM1 Loss of function RCC ICI response in RCC +5 points (RCC only)

Step 7.3: Driver Mutation Context

python
# 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)

Step 7.4: DNA Damage Repair (DDR) Pathway

Check if mutations are in DDR genes (associated with ICI response):

  • ATM, ATR, CHEK1, CHEK2 - DNA damage sensing
  • BRCA1, BRCA2, PALB2 - homologous recombination
  • RAD50, MRE11, NBN - double-strand break repair
  • POLE, POLD1 - polymerase proofreading

DDR mutations -> likely higher TMB -> better ICI response

Phase 8: Clinical Evidence & ICI Options

Step 8.1: FDA-Approved ICIs

python
# 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

Step 8.2: ICI Drug Profiles

Drug Target Type Key Indications
Pembrolizumab (Keytruda) PD-1 IgG4 mAb Melanoma, NSCLC, HNSCC, Bladder, MSI-H, TMB-H, many others
Nivolumab (Opdivo) PD-1 IgG4 mAb Melanoma, NSCLC, RCC, CRC (MSI-H), HCC, HNSCC
Atezolizumab (Tecentriq) PD-L1 IgG1 mAb NSCLC, Bladder, HCC, Melanoma
Durvalumab (Imfinzi) PD-L1 IgG1 mAb NSCLC (Stage III), Bladder, HCC, BTC
Ipilimumab (Yervoy) CTLA-4 IgG1 mAb Melanoma, RCC (combo), CRC (MSI-H combo)
Avelumab (Bavencio) PD-L1 IgG1 mAb Merkel cell, Bladder (maintenance)
Cemiplimab (Libtayo) PD-1 IgG4 mAb CSCC, NSCLC, Basal cell
Dostarlimab (Jemperli) PD-1 IgG4 mAb dMMR endometrial, dMMR solid tumors
Tremelimumab (Imjudo) CTLA-4 IgG2 mAb HCC (combo with durva)

Step 8.3: Clinical Trial Evidence

python
# 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}]}

Step 8.4: Literature Evidence

python
# 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, ...}

Step 8.5: OpenTargets Drug-Target Evidence

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

Key ICI ChEMBL IDs

Drug ChEMBL ID
Pembrolizumab CHEMBL3137343
Nivolumab CHEMBL2108738
Atezolizumab CHEMBL3707227
Durvalumab CHEMBL3301587
Ipilimumab CHEMBL1789844
Avelumab CHEMBL3833373
Cemiplimab CHEMBL4297723

Phase 9: Resistance Risk Assessment

Step 9.1: Known Resistance Factors Check

For each mutation in the patient profile, check against resistance database:

python
# 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

Step 9.2: Pathway-Level Resistance

Pathway Resistance Mechanism Genes
IFN-g signaling Loss of IFN-g response JAK1, JAK2, STAT1, IRF1
Antigen presentation MHC-I downregulation B2M, TAP1, TAP2, HLA-A/B/C
WNT/b-catenin T cell exclusion CTNNB1 activating mutations
MAPK pathway Immune suppression MEK, ERK hyperactivation
PI3K/AKT/mTOR Immune suppression PTEN loss, PIK3CA

Step 9.3: Resistance Risk Score

Summarize resistance risk as:

  • Low risk: No resistance mutations, favorable TME
  • Moderate risk: 1 resistance factor OR uncertain TME
  • High risk: Multiple resistance mutations OR known resistant phenotype

Phase 10: Multi-Biomarker Score Integration

ICI Response Score Calculation (0-100)

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)

Response Likelihood Tiers

Score Range Tier Expected ORR Recommendation
70-100 HIGH 50-80% Strong ICI candidate; monotherapy or combo
40-69 MODERATE 20-50% Consider ICI; combo preferred; monitor closely
0-39 LOW <20% ICI alone unlikely effective; consider alternatives

Confidence Level

Data Completeness Confidence
All biomarkers (TMB + MSI + PD-L1 + mutations) HIGH
3 of 4 biomarkers MODERATE-HIGH
2 of 4 biomarkers MODERATE
1 biomarker only LOW
Cancer type only VERY LOW

Phase 11: Clinical Recommendations

Step 11.1: ICI Drug Selection Algorithm

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

Step 11.2: Monitoring Plan

During ICI treatment, monitor:

  • Tumor response (CT/MRI every 8-12 weeks)
  • Circulating tumor DNA (ctDNA) for early response
  • Immune-related adverse events (irAEs)
  • Thyroid function (TSH every 6 weeks)
  • Liver function (every 2-4 weeks initially)
  • Cortisol if symptoms

Early response biomarkers:

  • ctDNA decrease at 4-6 weeks
  • PET-CT metabolic response
  • Circulating immune cell phenotyping

Step 11.3: Alternative Strategies

If ICI response predicted to be LOW:

  1. Targeted therapy (if actionable mutations: BRAF, EGFR, ALK, ROS1)
  2. Chemotherapy (standard of care)
  3. ICI + chemotherapy combination (may overcome low PD-L1)
  4. ICI + anti-angiogenic (may convert cold to hot tumor)
  5. ICI + CTLA-4 combo (nivolumab + ipilimumab)
  6. Clinical trial enrollment (novel combinations)

Output Report Format

Save report as immunotherapy_response_prediction_{cancer_type}.md

Report Structure

markdown
# 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]

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

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

Use Case Examples

Use Case 1: NSCLC with High TMB

Input: "NSCLC, TMB 25, PD-L1 80%, no STK11 mutation" Expected: ICI Score 70-85, HIGH response, pembrolizumab monotherapy recommended

Use Case 2: Melanoma with BRAF

Input: "Melanoma, BRAF V600E, TMB 15, PD-L1 50%" Expected: ICI Score 50-65, MODERATE response, discuss ICI vs BRAF-targeted

Use Case 3: MSI-H Colorectal

Input: "Colorectal cancer, MSI-high, TMB 40" Expected: ICI Score 80-95, HIGH response, pembrolizumab first-line

Use Case 4: Low Biomarker NSCLC

Input: "NSCLC, TMB 2, PD-L1 <1%, STK11 mutation" Expected: ICI Score 5-20, LOW response, chemotherapy preferred

Use Case 5: Bladder Cancer

Input: "Bladder cancer, TMB 12, PD-L1 10%, no resistance mutations" Expected: ICI Score 45-55, MODERATE response, ICI+chemo or maintenance

Use Case 6: Checkpoint Inhibitor Selection

Input: "Which ICI for NSCLC with PD-L1 90%?" Expected: Pembrolizumab monotherapy first-line, evidence from KEYNOTE-024

Completeness Checklist

Before finalizing the report, verify:

  • Cancer type resolved to EFO ID
  • All mutations parsed and genes resolved
  • TMB classified with cancer-specific context
  • MSI/MMR status assessed
  • PD-L1 integrated (or flagged as unknown)
  • Neoantigen burden estimated
  • Resistance mutations checked (STK11, PTEN, JAK1/2, B2M, KEAP1)
  • Sensitivity mutations checked (POLE, PBRM1, DDR)
  • FDA-approved ICIs identified for this cancer
  • Clinical trial evidence retrieved
  • ICI Response Score calculated with component breakdown
  • Drug recommendation provided with evidence
  • Monitoring plan included
  • Alternative strategies for low responders
  • Evidence grading applied to all findings
  • Data completeness documented
  • Missing data recommendations provided
  • Report saved to file

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