tooluniverse-precision-oncology

name	tooluniverse-precision-oncology
description	Provide actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, finds resistance mechanisms, matches clinical trials. Use when oncologist asks about treatment options for specific mutations (EGFR, KRAS, BRAF, etc.), therapy resistance, or clinical trial eligibility.

Precision Oncology Treatment Advisor

Provide actionable treatment recommendations for cancer patients based on their molecular profile using CIViC, ClinVar, OpenTargets, ClinicalTrials.gov, and structure-based analysis.

KEY PRINCIPLES:

1. Report-first - Create report file FIRST, update progressively
2. Evidence-graded - Every recommendation has evidence level
3. Actionable output - Prioritized treatment options, not data dumps
4. Clinical focus - Answer "what should we do?" not "what exists?"
5. English-first queries - Always use English terms in tool calls (mutations, drug names, cancer types), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

---

When to Use

Apply when user asks:

• "Patient has [cancer] with [mutation] - what treatments?"
• "What are options for EGFR-mutant lung cancer?"
• "Patient failed [drug], what's next?"
• "Clinical trials for KRAS G12C?"
• "Why isn't [drug] working anymore?"

---

Phase 0: Tool Verification

CRITICAL: Verify tool parameters before first use.

Tool	WRONG	CORRECT
civic_get_variant	variant_name	id (numeric)
civic_get_evidence_item	variant_id	id
OpenTargets_*	ensemblID	ensemblId (camelCase)
search_clinical_trials	disease	condition

---

Workflow Overview

Input: Cancer type + Molecular profile (mutations, fusions, amplifications)

Phase 1: Profile Validation
├── Validate variant nomenclature
├── Resolve gene identifiers
└── Confirm cancer type (EFO/ICD)

Phase 2: Variant Interpretation
├── CIViC → Evidence for each variant
├── ClinVar → Pathogenicity
├── COSMIC → Somatic mutation frequency
├── GDC/TCGA → Real tumor data
├── DepMap → Target essentiality
├── OncoKB → FDA actionability levels (NEW)
├── cBioPortal → Cross-study mutation data (NEW)
├── Human Protein Atlas → Expression validation (NEW)
├── OpenTargets → Target-disease evidence
└── OUTPUT: Variant significance table + target validation + expression

Phase 2.5: Tumor Expression Context (NEW)
├── CELLxGENE → Cell-type specific expression in tumor
├── ChIPAtlas → Regulatory context
├── Cancer-specific expression patterns
└── OUTPUT: Expression validation

Phase 3: Treatment Options
├── Approved therapies (FDA label)
├── NCCN-recommended (literature)
├── Off-label with evidence
└── OUTPUT: Prioritized treatment list

Phase 3.5: Pathway & Network Analysis (NEW)
├── KEGG/Reactome → Pathway context
├── IntAct → Protein interactions
├── Drug combination rationale
└── OUTPUT: Biological context for combinations

Phase 4: Resistance Analysis (if prior therapy)
├── Known resistance mechanisms
├── Structure-based analysis (NvidiaNIM)
├── Network-based bypass pathways (IntAct)
└── OUTPUT: Resistance explanation + strategies

Phase 5: Clinical Trial Matching
├── Active trials for indication + biomarker
├── Eligibility filtering
└── OUTPUT: Matched trials

Phase 5.5: Literature Evidence (NEW)
├── PubMed → Published evidence
├── BioRxiv/MedRxiv → Recent preprints
├── OpenAlex → Citation analysis
└── OUTPUT: Supporting literature

Phase 6: Report Synthesis
├── Executive summary
├── Treatment recommendations (prioritized)
└── Next steps

---

Phase 1: Profile Validation

1.1 Resolve Gene Identifiers

def resolve_gene(tu, gene_symbol):
    """Resolve gene to all needed IDs."""
    ids = {}
    
    # Ensembl ID (for OpenTargets)
    gene_info = tu.tools.MyGene_query_genes(q=gene_symbol, species="human")
    ids['ensembl'] = gene_info.get('ensembl', {}).get('gene')
    
    # UniProt (for structure)
    uniprot = tu.tools.UniProt_search(query=gene_symbol, organism="human")
    ids['uniprot'] = uniprot[0].get('primaryAccession') if uniprot else None
    
    # ChEMBL target
    target = tu.tools.ChEMBL_search_targets(query=gene_symbol, organism="Homo sapiens")
    ids['chembl_target'] = target[0].get('target_chembl_id') if target else None
    
    return ids

1.2 Validate Variant Nomenclature

• HGVS protein: p.L858R, p.V600E
• cDNA: c.2573T>G
• Common names: T790M, G12C

---

Phase 2: Variant Interpretation

2.1 CIViC Evidence Query

def get_civic_evidence(tu, gene_symbol, variant_name):
    """Get CIViC evidence for variant."""
    # Search for variant
    variants = tu.tools.civic_search_variants(query=f"{gene_symbol} {variant_name}")
    
    evidence_items = []
    for var in variants:
        # Get evidence items for this variant
        evi = tu.tools.civic_get_variant(id=var['id'])
        evidence_items.extend(evi.get('evidence_items', []))
    
    # Categorize by evidence type
    return {
        'predictive': [e for e in evidence_items if e['evidence_type'] == 'Predictive'],
        'prognostic': [e for e in evidence_items if e['evidence_type'] == 'Prognostic'],
        'diagnostic': [e for e in evidence_items if e['evidence_type'] == 'Diagnostic']
    }

2.2 COSMIC Somatic Mutation Analysis (NEW)

def get_cosmic_mutations(tu, gene_symbol, variant_name=None):
    """Get somatic mutation data from COSMIC database."""
    
    # Get all mutations for gene
    gene_mutations = tu.tools.COSMIC_get_mutations_by_gene(
        operation="get_by_gene",
        gene=gene_symbol,
        max_results=100,
        genome_build=38
    )
    
    # If specific variant, search for it
    if variant_name:
        specific = tu.tools.COSMIC_search_mutations(
            operation="search",
            terms=f"{gene_symbol} {variant_name}",
            max_results=20
        )
        return {
            'specific_variant': specific.get('results', []),
            'all_gene_mutations': gene_mutations.get('results', [])
        }
    
    return gene_mutations

def get_cosmic_hotspots(tu, gene_symbol):
    """Identify mutation hotspots in COSMIC."""
    mutations = tu.tools.COSMIC_get_mutations_by_gene(
        operation="get_by_gene",
        gene=gene_symbol,
        max_results=500
    )
    
    # Count by position
    position_counts = Counter(m['MutationAA'] for m in mutations.get('results', []))
    hotspots = position_counts.most_common(10)
    
    return hotspots

Why COSMIC matters:

• Gold standard for somatic cancer mutations
• Provides cancer type distribution (which cancers have this mutation)
• FATHMM pathogenicity prediction for novel variants
• Identifies hotspots vs. rare mutations

2.3 GDC/TCGA Pan-Cancer Analysis (NEW)

Access real patient tumor data from The Cancer Genome Atlas:

def get_tcga_mutation_data(tu, gene_symbol, cancer_type=None):
    """
    Get somatic mutations from TCGA via GDC.
    
    Answers: "How often is this mutation seen in real tumors?"
    """
    
    # Get mutation frequency across all TCGA
    frequency = tu.tools.GDC_get_mutation_frequency(
        gene_symbol=gene_symbol
    )
    
    # Get specific mutations
    mutations = tu.tools.GDC_get_ssm_by_gene(
        gene_symbol=gene_symbol,
        project_id=f"TCGA-{cancer_type}" if cancer_type else None,
        size=50
    )
    
    return {
        'frequency': frequency.get('data', {}),
        'mutations': mutations.get('data', {}),
        'note': 'Real patient tumor data from TCGA'
    }

def get_tcga_expression_profile(tu, gene_symbol, cancer_type):
    """Get gene expression data from TCGA."""
    
    # Map cancer type to TCGA project
    project_map = {
        'lung': 'TCGA-LUAD',
        'breast': 'TCGA-BRCA', 
        'colorectal': 'TCGA-COAD',
        'melanoma': 'TCGA-SKCM',
        'glioblastoma': 'TCGA-GBM'
    }
    project_id = project_map.get(cancer_type.lower(), f'TCGA-{cancer_type.upper()}')
    
    expression = tu.tools.GDC_get_gene_expression(
        project_id=project_id,
        size=20
    )
    
    return expression.get('data', {})

 ():
    
    
    project_map = {
        : ,
        : 
    }
    project_id = project_map.get(cancer_type.lower(), )
    
    cnv = tu.tools.GDC_get_cnv_data(
        project_id=project_id,
        gene_symbol=gene_symbol,
        size=
    )
    
     cnv.get(, {})

GDC Tools Summary:

Tool	Purpose	Key Parameters
GDC_get_mutation_frequency	Pan-cancer mutation stats	gene_symbol
GDC_get_ssm_by_gene	Specific mutations	gene_symbol, project_id
GDC_get_gene_expression	RNA-seq data	project_id
GDC_get_cnv_data	Copy number	project_id, gene_symbol
GDC_list_projects	Find TCGA projects	program="TCGA"

Why TCGA/GDC matters:

• Real patient data - Not cell line or curated, actual tumor sequencing
• Pan-cancer view - Same gene across 33 cancer types
• Multi-omic - Mutations, expression, CNV together
• Clinical correlation - Survival data available

2.4 DepMap Target Validation (NEW)

Assess gene essentiality using CRISPR knockout data from cancer cell lines:

def assess_target_essentiality(tu, gene_symbol, cancer_type=None):
    """
    Is this gene essential in cancer cell lines?
    
    Essential genes have negative dependency scores.
    Answers: "If we target this gene, will cancer cells die?"
    """
    
    # Get gene dependency data
    dependencies = tu.tools.DepMap_get_gene_dependencies(
        gene_symbol=gene_symbol
    )
    
    # Get cell lines for specific cancer type
    if cancer_type:
        cell_lines = tu.tools.DepMap_get_cell_lines(
            cancer_type=cancer_type,
            page_size=20
        )
        return {
            'gene': gene_symbol,
            'dependencies': dependencies.get('data', {}),
            'cell_lines': cell_lines.get('data', {}),
            'interpretation': 'Negative scores = gene is essential for cell survival'
        }
    
    return dependencies

def get_depmap_drug_sensitivity(tu, drug_name, cancer_type=None):
    """Get drug sensitivity data from DepMap."""
    
    drugs = tu.tools.DepMap_get_drug_response(
        drug_name=drug_name
    )
    
    return drugs.get('data', {})

DepMap Tools Summary:

Tool	Purpose	Key Parameters
DepMap_get_gene_dependencies	CRISPR essentiality	gene_symbol
DepMap_get_cell_lines	Cell line metadata	cancer_type, tissue
DepMap_search_cell_lines	Search by name	query
DepMap_get_drug_response	Drug sensitivity	drug_name

Why DepMap matters for Precision Oncology:

• Target validation - Proves gene is essential for cancer survival
• Cancer selectivity - Essential in cancer but not normal cells?
• Resistance prediction - What other genes become essential when you knockout target?
• Combination rationale - Identify synthetic lethal partners

Example Clinical Application:

### Target Essentiality Assessment (DepMap)

**KRAS dependency in pancreatic cancer cell lines**:
| Cell Line | KRAS Effect Score | Interpretation |
|-----------|-------------------|----------------|
| PANC-1 | -0.82 | Strongly essential |
| MIA PaCa-2 | -0.75 | Essential |
| BxPC-3 | -0.21 | Less dependent (KRAS WT) |

*Interpretation: KRAS-mutant pancreatic cancer lines are highly dependent on KRAS - validates targeting strategy.*

*Source: DepMap via `DepMap_get_gene_dependencies`*

2.5 OncoKB Actionability Assessment (NEW)

OncoKB provides FDA-approved therapeutic actionability annotations:

def get_oncokb_annotations(tu, gene_symbol, variant_name, tumor_type=None):
    """
    Get OncoKB actionability annotations.
    
    OncoKB Level of Evidence:
    - Level 1: FDA-approved
    - Level 2: Standard care
    - Level 3A: Compelling clinical evidence
    - Level 3B: Standard care in different tumor type
    - Level 4: Biological evidence
    - R1/R2: Resistance evidence
    """
    
    # Annotate the specific variant
    annotation = tu.tools.OncoKB_annotate_variant(
        operation="annotate_variant",
        gene=gene_symbol,
        variant=variant_name,  # e.g., "V600E"
        tumor_type=tumor_type  # OncoTree code e.g., "MEL", "LUAD"
    )
    
    result = {
        'oncogenic': annotation.get('data', {}).get('oncogenic'),
        'mutation_effect': annotation.get('data', {}).get('mutationEffect'),
        'highest_sensitive_level': annotation.get('data', {}).get('highestSensitiveLevel'),
        'treatments': annotation.get('data', {}).get('treatments', [])
    }
    
    # Get gene-level info
    gene_info = tu.tools.OncoKB_get_gene_info(
        operation="get_gene_info",
        gene=gene_symbol
    )
    
    result['is_oncogene'] = gene_info.get('data', {}).get('oncogene', False)
    result['is_tumor_suppressor'] = gene_info.get('data', {}).get('tsg', False)
    
    return result

def get_oncokb_cnv_annotation(tu, gene_symbol, alteration_type, tumor_type=None):
    """Get OncoKB annotation for copy number alterations."""
    
    annotation = tu.tools.OncoKB_annotate_copy_number(
        operation=,
        gene=gene_symbol,
        copy_number_type=alteration_type,  
        tumor_type=tumor_type
    )
    
     {
        : annotation.get(, {}).get(),
        : annotation.get(, {}).get(, [])
    }

OncoKB Level Mapping:

OncoKB Level	Our Tier	Description
LEVEL_1	★★★	FDA-recognized biomarker
LEVEL_2	★★★	Standard care
LEVEL_3A	★★☆	Compelling clinical evidence
LEVEL_3B	★★☆	Different tumor type
LEVEL_4	★☆☆	Biological evidence
LEVEL_R1	Resistance	FDA-approved resistance marker
LEVEL_R2	Resistance	Compelling resistance evidence

2.6 cBioPortal Cross-Study Analysis (NEW)

Aggregate mutation data across multiple cancer studies:

def get_cbioportal_mutations(tu, gene_symbols, study_id="brca_tcga"):
    """
    Get mutation data from cBioPortal across cancer studies.
    
    Provides: Mutation types, protein changes, co-mutations.
    """
    
    # Get mutations for genes in study
    mutations = tu.tools.cBioPortal_get_mutations(
        study_id=study_id,
        gene_list=",".join(gene_symbols)  # e.g., "EGFR,KRAS"
    )
    
    # Parse results
    results = []
    for mut in mutations or []:
        results.append({
            'gene': mut.get('gene', {}).get('hugoGeneSymbol'),
            'protein_change': mut.get('proteinChange'),
            'mutation_type': mut.get('mutationType'),
            'sample_id': mut.get('sampleId'),
            'validation_status': mut.get('validationStatus')
        })
    
    return results

def get_cbioportal_cancer_studies(tu, cancer_type=None):
    """Get available cancer studies from cBioPortal."""
    
    studies = tu.tools.cBioPortal_get_cancer_studies(limit=50)
    
    if cancer_type:
        studies = [s for s in studies if cancer_type.lower() in s.get('cancerTypeId', '').lower()]
    
    return studies

def analyze_co_mutations(tu, gene_symbol, study_id):
    
    
    
    profiles = tu.tools.cBioPortal_get_molecular_profiles(study_id=study_id)
    
    
    mutations = tu.tools.cBioPortal_get_mutations(
        study_id=study_id,
        gene_list=gene_symbol
    )
    
     {
        : profiles,
        : mutations,
        : study_id
    }

cBioPortal Use Cases:

Use Case	Tool	Parameters
Find mutation frequency	cBioPortal_get_mutations	study_id, gene_list
List available studies	cBioPortal_get_cancer_studies	limit
Get molecular profiles	cBioPortal_get_molecular_profiles	study_id
Analyze co-mutations	Multiple tools	Combined analysis

2.7 Human Protein Atlas Expression (NEW)

Validate target expression in tumor vs normal tissues:

def get_hpa_expression(tu, gene_symbol):
    """
    Get protein expression data from Human Protein Atlas.
    
    Critical for validating:
    - Target is expressed in tumor tissue
    - Target has differential tumor vs normal expression
    """
    
    # Search for gene
    gene_info = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol)
    
    if not gene_info:
        return None
    
    # Get tissue expression data
    ensembl_id = gene_info[0].get('Ensembl') if gene_info else None
    
    # Comparative expression in cancer cell lines
    cell_line_data = tu.tools.HPA_get_comparative_expression_by_gene_and_cellline(
        gene_name=gene_symbol,
        cell_line="a549"  # Lung cancer cell line
    )
    
    return {
        'gene_info': gene_info,
        'cell_line_expression': cell_line_data
    }

def check_tumor_specific_expression(tu, gene_symbol, cancer_type):
    """Check if target has tumor-specific expression pattern."""
    
    # Map cancer type to cell line
    cancer_to_cellline = {
        'lung': 'a549',
        'breast': 'mcf7',
        'liver': 'hepg2',
        'cervical': 'hela',
        'prostate': 'pc3'
    }
    
    cell_line = cancer_to_cellline.get(cancer_type.lower(), 'a549')
    
    expression = tu.tools.HPA_get_comparative_expression_by_gene_and_cellline(
        gene_name=gene_symbol,
        cell_line=cell_line
    )
    
     expression

HPA Expression Validation Output:

### Expression Validation (Human Protein Atlas)

| Gene | Tumor Cell Line | Expression | Normal Tissue | Differential |
|------|-----------------|------------|---------------|--------------|
| EGFR | A549 (lung) | High | Low-Medium | Tumor-elevated |
| ALK | H3122 (lung) | High | Not detected | Tumor-specific |
| HER2 | MCF7 (breast) | Medium | Low | Elevated |

*Source: Human Protein Atlas via `HPA_get_comparative_expression_by_gene_and_cellline`*

2.8 Evidence Level Mapping

CIViC Level	Our Tier	Meaning
A	★★★	FDA-approved, guideline
B	★★☆	Clinical evidence
C	★★☆	Case study
D	★☆☆	Preclinical
E	☆☆☆	Inferential

2.4 Output Table

## Variant Interpretation

| Variant | Gene | Significance | Evidence Level | Clinical Implication |
|---------|------|--------------|----------------|---------------------|
| L858R | EGFR | Oncogenic driver | ★★★ (Level A) | Sensitive to EGFR TKIs |
| T790M | EGFR | Resistance | ★★★ (Level A) | Resistant to 1st/2nd gen TKIs |

### COSMIC Mutation Frequency

| Gene | Mutation | COSMIC Count | Primary Cancer Types | FATHMM Prediction |
|------|----------|--------------|---------------------|-------------------|
| EGFR | L858R | 15,234 | Lung (85%), Colorectal (5%) | Pathogenic |
| EGFR | T790M | 8,567 | Lung (95%) | Pathogenic |
| BRAF | V600E | 45,678 | Melanoma (50%), Colorectal (15%) | Pathogenic |

### TCGA/GDC Patient Tumor Data (NEW)

| Gene | TCGA Project | SSM Cases | CNV Amp | CNV Del | % Samples |
|------|-------------|-----------|---------|---------|-----------|
| EGFR | TCGA-LUAD | 156 | 89 | 5 | 28% |
| EGFR | TCGA-GBM | 45 | 312 | 2 | 57% |
| KRAS | TCGA-PAAD | 134 | 8 | 1 | 92% |

*Source: GDC via `GDC_get_mutation_frequency`, `GDC_get_cnv_data`*

### DepMap Target Essentiality (NEW)

| Gene | Mean Effect (All) | Mean Effect (Cancer Type) | Selectivity | Interpretation |
|------|-------------------|---------------------------|-------------|----------------|
| EGFR | -0.15 | -0.45 (lung) | Cancer-selective | Good target |
| KRAS | -0.82 | -0.91 (pancreatic) | Essential | Hard to target |
| MYC | -0.95 | -0.93 | Pan-essential | Challenging target |

*Effect score <-0.5 = strongly essential for cell survival*
*Source: DepMap via `DepMap_get_gene_dependencies`*

*Combined Sources: CIViC, ClinVar, COSMIC, GDC/TCGA, DepMap*

---

Phase 2.5: Tumor Expression Context (NEW)

2.5.1 Cell-Type Expression in Tumor (CELLxGENE)

def get_tumor_expression_context(tu, gene_symbol, cancer_type):
    """Get cell-type specific expression in tumor microenvironment."""
    
    # Get expression in tumor and normal cells
    expression = tu.tools.CELLxGENE_get_expression_data(
        gene=gene_symbol,
        tissue=cancer_type  # e.g., "lung", "breast"
    )
    
    # Cell metadata for context
    cell_metadata = tu.tools.CELLxGENE_get_cell_metadata(
        gene=gene_symbol
    )
    
    # Identify tumor vs normal expression
    tumor_expression = [c for c in expression if 'tumor' in c.get('cell_type', '').lower()]
    normal_expression = [c for c in expression if 'normal' in c.get('cell_type', '').lower()]
    
    return {
        'tumor_expression': tumor_expression,
        'normal_expression': normal_expression,
        'ratio': calculate_tumor_normal_ratio(tumor_expression, normal_expression)
    }

Why it matters:

• Confirms target is expressed in tumor cells (not just stroma)
• Identifies potential resistance from tumor heterogeneity
• Supports drug selection based on expression patterns

2.5.2 Output for Report

## 2.5 Tumor Expression Context

### Target Expression in Tumor Microenvironment (CELLxGENE)

| Gene | Tumor Cells | Normal Cells | Tumor/Normal Ratio | Interpretation |
|------|-------------|--------------|-------------------|----------------|
| EGFR | High (TPM=85) | Medium (TPM=25) | 3.4x | Good target |
| MET | Medium (TPM=35) | Low (TPM=8) | 4.4x | Potential bypass |
| AXL | High (TPM=120) | Low (TPM=15) | 8.0x | Resistance marker |

### Cell Type Distribution

- **EGFR-high cells**: Tumor epithelial (85%), CAFs (10%), immune (5%)
- **MET-high cells**: Tumor epithelial (70%), endothelial (20%), immune (10%)

**Clinical Relevance**: EGFR highly expressed in tumor epithelial cells. AXL overexpression in tumor suggests potential resistance mechanism.

*Source: CELLxGENE Census*

---

Phase 3: Treatment Options

3.1 Approved Therapies

Query order:

1. OpenTargets_get_associated_drugs_by_target_ensemblId → Approved drugs
2. DailyMed_search_spls → FDA label details
3. ChEMBL_get_drug_mechanisms_of_action_by_chemblId → Mechanism

3.2 Treatment Prioritization

Priority	Criteria
1st Line	FDA-approved for indication + biomarker (★★★)
2nd Line	Clinical trial evidence, guideline-recommended (★★☆)
3rd Line	Off-label with mechanistic rationale (★☆☆)

3.3 Output Format

## Treatment Recommendations

### First-Line Options
**1. Osimertinib (Tagrisso)** ★★★
- FDA-approved for EGFR T790M+ NSCLC
- Evidence: AURA3 trial (ORR 71%, mPFS 10.1 mo)
- Source: FDA label, PMID:27959700

### Second-Line Options
**2. Combination: Osimertinib + [Agent]** ★★☆
- Evidence: Phase 2 data
- Source: NCT04487080

---

Phase 3.5: Pathway & Network Analysis (NEW)

3.5.1 Pathway Context (KEGG/Reactome)

def get_pathway_context(tu, gene_symbols, cancer_type):
    """Get pathway context for drug combinations and resistance."""
    
    pathway_map = {}
    for gene in gene_symbols:
        # KEGG pathways
        kegg_gene = tu.tools.kegg_find_genes(query=f"hsa:{gene}")
        if kegg_gene:
            pathways = tu.tools.kegg_get_gene_info(gene_id=kegg_gene[0]['id'])
            pathway_map[gene] = pathways.get('pathways', [])
        
        # Reactome disease score
        reactome = tu.tools.reactome_disease_target_score(
            disease=cancer_type,
            target=gene
        )
        pathway_map[f"{gene}_reactome"] = reactome
    
    return pathway_map

3.5.2 Protein Interaction Network (IntAct)

def get_resistance_network(tu, drug_target, bypass_candidates):
    """Find protein interactions that may mediate resistance."""
    
    # Get interaction network for drug target
    network = tu.tools.intact_get_interaction_network(
        gene=drug_target,
        depth=2  # Include 2nd degree connections
    )
    
    # Find bypass pathway candidates in network
    bypass_in_network = [
        node for node in network['nodes']
        if node['gene'] in bypass_candidates
    ]
    
    return {
        'network': network,
        'bypass_connections': bypass_in_network,
        'total_interactors': len(network['nodes'])
    }

3.5.3 Output for Report

## 3.5 Pathway & Network Analysis

### Signaling Pathway Context (KEGG)

| Pathway | Genes Involved | Relevance | Drug Targets |
|---------|---------------|-----------|--------------|
| EGFR signaling (hsa04012) | EGFR, MET, ERBB3 | Primary pathway | Osimertinib, Capmatinib |
| PI3K-AKT (hsa04151) | PIK3CA, AKT1 | Downstream | Alpelisib |
| RAS-MAPK (hsa04010) | KRAS, BRAF, MEK | Bypass potential | Sotorasib, Trametinib |

### Drug Combination Rationale

**Biological basis for combinations**:
- EGFR inhibition → compensatory MET activation (60% of cases)
- **Rationale for EGFR + MET inhibition**: Block primary and bypass pathways
- Network shows direct EGFR-MET interaction (IntAct: MI-score 0.75)

### Protein Interaction Network (IntAct)

| Target | Direct Interactors | Key Partners | Relevance |
|--------|-------------------|--------------|-----------|
| EGFR | 156 | MET, ERBB2, ERBB3, GRB2 | Bypass pathways |
| MET | 89 | EGFR, HGF, GAB1 | Resistance mediator |

*Source: KEGG, Reactome, IntAct*

---

Phase 4: Resistance Analysis

4.1 Known Mechanisms (Literature + CIViC)

def analyze_resistance(tu, drug_name, gene_symbol):
    """Find known resistance mechanisms."""
    # CIViC resistance evidence
    resistance = tu.tools.civic_search_evidence_items(
        drug=drug_name,
        evidence_type="Predictive",
        clinical_significance="Resistance"
    )
    
    # Literature search
    papers = tu.tools.PubMed_search_articles(
        query=f'"{drug_name}" AND "{gene_symbol}" AND resistance',
        limit=20
    )
    
    return {'civic': resistance, 'literature': papers}

4.2 Structure-Based Analysis (NvidiaNIM)

When mutation affects drug binding:

def model_resistance_mechanism(tu, gene_ids, mutation, drug_smiles):
    """Model structural impact of resistance mutation."""
    # Get/predict structure
    structure = tu.tools.NvidiaNIM_alphafold2(sequence=wild_type_sequence)
    
    # Dock drug to wild-type
    wt_docking = tu.tools.NvidiaNIM_diffdock(
        protein=structure['structure'],
        ligand=drug_smiles,
        num_poses=5
    )
    
    # Compare binding site changes
    # Report: "T790M introduces bulky methionine, steric clash with erlotinib"

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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-precision-oncology/REFERENCE.md