tooluniverse-immunotherapy-response-prediction

name

tooluniverse-immunotherapy-response-prediction

description

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.

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

# 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

# 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

# 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

# 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)

# 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

# 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)

# 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:

# 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

# 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

# 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

# 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

# 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

# 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

# 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:

# 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

---

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Extended Reference: For detailed tool tables, examples, and templates, read REFERENCE.md in this skill directory. The agent can access it via: read skills/tooluniverse-immunotherapy-response-prediction/REFERENCE.md