tooluniverse-precision-medicine-stratification

name

tooluniverse-precision-medicine-stratification

description

Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.

Precision Medicine Patient Stratification

Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies. Integrates germline genetics, somatic alterations, pharmacogenomics, pathway biology, and clinical evidence to produce a quantitative risk score with tiered management recommendations.

KEY PRINCIPLES:

1. Report-first approach - Create report file FIRST, then populate progressively
2. Disease-specific logic - Cancer vs metabolic vs rare disease pipelines diverge at Phase 2
3. Multi-level integration - Germline + somatic + expression + clinical data layers
4. Evidence-graded - Every finding has an evidence tier (T1-T4)
5. Quantitative output - Precision Medicine Risk Score (0-100) with transparent components
6. Pharmacogenomic guidance - Drug selection AND dosing recommendations
7. Guideline-concordant - Reference NCCN, ACC/AHA, ADA, and other guidelines
8. Source-referenced - Every statement cites the tool/database source
9. Completeness checklist - Mandatory section showing data availability and analysis coverage
10. English-first queries - Always use English terms in tool calls. Respond in user's language

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When to Use

Apply when user asks:

• "Stratify this breast cancer patient: ER+/HER2-, BRCA1 mutation, stage II"
• "What is the risk profile for this diabetes patient with HbA1c 8.5 and CYP2C19 poor metabolizer?"
• "NSCLC patient with EGFR L858R, stage IV, TMB 25 - treatment strategy?"
• "Predict prognosis and recommend treatment for this cardiovascular patient"
• "Patient has Marfan syndrome with FBN1 mutation - risk stratification"
• "Alzheimer's risk assessment: APOE e4/e4, family history positive"
• "Personalized treatment plan for type 2 diabetes with genetic risk factors"
• "Which therapy is best for this patient's molecular profile?"

NOT for (use other skills instead):

• Single variant interpretation -> Use tooluniverse-variant-interpretation or tooluniverse-cancer-variant-interpretation
• Immunotherapy-specific prediction -> Use tooluniverse-immunotherapy-response-prediction
• Drug safety profiling only -> Use tooluniverse-adverse-event-detection
• Target validation -> Use tooluniverse-drug-target-validation
• Clinical trial search only -> Use tooluniverse-clinical-trial-matching
• Drug-drug interaction analysis only -> Use tooluniverse-drug-drug-interaction
• PRS calculation only -> Use tooluniverse-polygenic-risk-score

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Input Parsing

Required Input

• Disease/condition: Free-text disease name (e.g., "breast cancer", "type 2 diabetes", "Marfan syndrome")
• At least one of: Germline variants, somatic mutations, gene list, or clinical biomarkers

Strongly Recommended

• Genomic data: Specific variants (e.g., "BRCA1 c.68_69delAG", "EGFR L858R"), gene names, or expression changes
• Clinical parameters: Age, sex, disease stage, biomarkers (HbA1c, PSA, LDL-C)

Optional (improves stratification)

• Comorbidities: Other conditions (e.g., "hypertension", "diabetes")
• Prior treatments: Previous therapies and responses
• Family history: Affected relatives, inheritance pattern
• Ethnicity: For population-specific risk calibration
• Current medications: For DDI and pharmacogenomic analysis
• Stratification goal: Risk assessment, treatment selection, prognosis, prevention

Input Format Examples

Format	Example	How to Parse
Cancer + mutations + stage	"Breast cancer, BRCA1 mut, ER+, HER2-, stage II"	disease=breast_cancer, mutations=[BRCA1], biomarkers={ER:+, HER2:-}, stage=II
Metabolic + biomarkers + PGx	"T2D, HbA1c 8.5, CYP2C19 *2/*2"	disease=T2D, biomarkers={HbA1c:8.5}, pgx={CYP2C19:poor_metabolizer}
CVD risk profile	"High LDL 190, SLCO1B1*5, family hx MI"	disease=CVD, biomarkers={LDL:190}, pgx={SLCO1B1:*5}, family_hx=positive
Rare disease + variant	"Marfan, FBN1 c.4082G>A"	disease=Marfan, mutations=[FBN1 c.4082G>A], disease_type=rare
Neuro risk	"Alzheimer risk, APOE e4/e4, age 55"	disease=AD, genotype={APOE:e4/e4}, clinical={age:55}
Cancer + comprehensive	"NSCLC, EGFR L858R, TMB 25, PD-L1 80%, stage IV"	disease=NSCLC, mutations=[EGFR L858R], biomarkers={TMB:25, PDL1:80}, stage=IV

Disease Type Classification

Classify the disease into one of these categories (determines Phase 2 routing):

Category	Examples	Key Stratification Axes
CANCER	Breast, lung, colorectal, melanoma, prostate	Stage, molecular subtype, TMB, driver mutations, hormone receptors
METABOLIC	Type 2 diabetes, obesity, metabolic syndrome, NAFLD	HbA1c, BMI, genetic risk, comorbidities, CYP genotypes
CARDIOVASCULAR	CAD, heart failure, atrial fibrillation, hypertension	ASCVD risk, LDL, genetic risk, statin PGx, anticoagulant PGx
NEUROLOGICAL	Alzheimer, Parkinson, epilepsy, multiple sclerosis	APOE status, genetic risk, age of onset, PGx for anticonvulsants
RARE/MONOGENIC	Marfan, CF, sickle cell, Huntington, PKU	Causal variant, penetrance, genotype-phenotype correlation
AUTOIMMUNE	RA, lupus, MS, Crohn's, ulcerative colitis	HLA associations, genetic risk, biologics PGx

Gene Symbol Normalization

Common Alias	Official Symbol	Notes
HER2	ERBB2	Breast cancer biomarker
PD-L1	CD274	Immunotherapy biomarker
EGFR	EGFR	Lung cancer driver
BRCA1/2	BRCA1, BRCA2	Hereditary cancer
CYP2D6	CYP2D6	Drug metabolism
CYP2C19	CYP2C19	Clopidogrel, PPIs
CYP3A4	CYP3A4	Major drug metabolism
VKORC1	VKORC1	Warfarin dosing
SLCO1B1	SLCO1B1	Statin myopathy
DPYD	DPYD	Fluoropyrimidine toxicity
UGT1A1	UGT1A1	Irinotecan toxicity
TPMT	TPMT	Thiopurine toxicity

---

Phase 0: Tool Parameter Reference (CRITICAL)

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

Verified Tool Parameters

Tool	Parameters	Response Structure	Notes
OpenTargets_get_disease_id_description_by_name	diseaseName	{data: {search: {hits: [{id, name, description}]}}}	Disease to EFO ID
OpenTargets_get_drug_id_description_by_name	drugName	{data: {search: {hits: [{id, name, description}]}}}	Drug to ChEMBL ID
OpenTargets_get_associated_drugs_by_disease_efoId	efoId, size	{data: {disease: {knownDrugs: {count, rows}}}}	Drugs for disease
OpenTargets_get_associated_targets_by_disease_efoId	efoId, size	{data: {disease: {associatedTargets: {count, rows}}}}	Genetic associations
OpenTargets_get_drug_mechanisms_of_action_by_chemblId	chemblId	{data: {drug: {mechanismsOfAction: {rows}}}}	Drug MOA
OpenTargets_get_approved_indications_by_drug_chemblId	chemblId	Approved indications list	Check drug approvals
OpenTargets_get_drug_adverse_events_by_chemblId	chemblId	{data: {drug: {adverseEvents: {count, rows}}}}	Drug safety
OpenTargets_get_associated_drugs_by_target_ensemblID	ensemblId, size	Drug-target associations	Drugs targeting gene
OpenTargets_get_target_safety_profile_by_ensemblID	ensemblId	Safety profile data	Target safety
OpenTargets_get_target_tractability_by_ensemblID	ensemblId	Tractability assessment	Druggability

Response Format Notes

• OpenTargets: Always nested {data: {entity: {field: ...}}} structure
• FDA label tools: Return {meta: {disclaimer, terms, license, ...}, results: [...]}. Access via result['results'][0]['field']
• DrugBank: ALL tools require 4 params: query, case_sensitive (bool), exact_match (bool), limit (int)
• PharmGKB: Returns complex nested objects. Check for data wrapper
• PubMed_search_articles: Returns a plain list of dicts, NOT {articles: [...]}
• ClinVar: clinvar_search_variants returns list of variants with clinical significance
• gnomAD: May return "Service overloaded" - treat as transient, retry or skip
• fda_pharmacogenomic_biomarkers: Default limit=10, use limit=1000 to get all
• cBioPortal_get_mutations: gene_list is a STRING, not array. cBioPortal tools may have URL bugs
• ClinVar: May return either a plain list or {status, data: {esearchresult: {count, idlist}}} - handle both
• EnsemblVEP: May return either a list [{...}] or {data: {...}, metadata: {...}} - handle both
• PubMed_Guidelines_Search: Requires limit parameter (NOT max_results), may require API key. Use PubMed_search_articles as fallback
• gwas_get_associations_for_trait: May return errors; use gwas_search_associations instead
• MyGene CYP2D6: First result may be LOC110740340; always filter by symbol match

---

Workflow Overview

Input: Disease + Genomic data + Clinical parameters + Stratification goal

Phase 1: Disease Disambiguation & Profile Standardization
  - Resolve disease to EFO/MONDO IDs
  - Classify disease type (cancer/metabolic/CVD/neuro/rare/autoimmune)
  - Parse genomic data (variants, genes, expression)
  - Resolve gene IDs (Ensembl, Entrez, UniProt)

Phase 2: Genetic Risk Assessment
  - Germline variant pathogenicity (ClinVar, VEP)
  - Gene-disease association strength (OpenTargets)
  - GWAS-based polygenic risk estimation
  - Population frequency (gnomAD)
  - Gene constraint/intolerance (gnomAD)

Phase 3: Disease-Specific Molecular Stratification
  CANCER PATH:
    - Molecular subtyping (driver mutations, receptor status)
    - Prognostic markers (stage + grade + molecular)
    - TMB/MSI/HRD assessment
    - Somatic mutation landscape (cBioPortal)
  METABOLIC PATH:
    - Genetic risk + clinical risk integration
    - Complication risk (nephropathy, neuropathy, CVD)
    - Monogenic subtypes (MODY, lipodystrophy)
  CVD PATH:
    - ASCVD risk integration
    - Familial hypercholesterolemia genes
    - Statin/anticoagulant PGx
  RARE DISEASE PATH:
    - Causal variant identification
    - Genotype-phenotype correlation
    - Penetrance estimation

Phase 4: Pharmacogenomic Profiling
  - Drug-metabolizing enzyme genotypes (CYP2D6, CYP2C19, CYP3A4)
  - Drug transporter variants (SLCO1B1, ABCB1)
  - Drug target variants (VKORC1, DPYD, UGT1A1)
  - HLA alleles (drug hypersensitivity risk)
  - PharmGKB clinical annotations
  - FDA pharmacogenomic biomarkers

Phase 5: Comorbidity & Drug Interaction Risk
  - Disease-disease genetic overlap
  - Impact on treatment selection
  - Drug-drug interaction risk
  - Pharmacogenomic DDI amplification

Phase 6: Molecular Pathway Analysis
  - Dysregulated pathway identification (Reactome, KEGG)
  - Network disruption analysis (STRING)
  - Druggable pathway targets
  - Pathway-based therapeutic opportunities

Phase 7: Clinical Evidence & Guidelines
  - Guideline-based risk categories (NCCN, ACC/AHA, ADA)
  - FDA-approved therapies for patient profile
  - Literature evidence (PubMed)
  - Biomarker-guided treatment evidence

Phase 8: Clinical Trial Matching
  - Trials matching molecular profile
  - Biomarker-driven trials
  - Precision medicine basket/umbrella trials
  - Risk-adapted trials

Phase 9: Integrated Scoring & Recommendations
  - Calculate Precision Medicine Risk Score (0-100)
  - Risk tier assignment (Low/Int/High/Very High)
  - Treatment algorithm (1st/2nd/3rd line)
  - Monitoring plan
  - Outcome predictions

---

Phase 1: Disease Disambiguation & Profile Standardization

Step 1.1: Resolve Disease to EFO ID

# Get disease EFO ID
result = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName='breast cancer')
# -> {data: {search: {hits: [{id: 'EFO_0000305', name: 'breast carcinoma', description: '...'}]}}}
efo_id = result['data']['search']['hits'][0]['id']

Common Disease EFO IDs (for reference):

Disease	EFO ID	Category
Breast carcinoma	EFO_0000305	CANCER
Non-small cell lung carcinoma	EFO_0003060	CANCER
Colorectal cancer	EFO_0000365	CANCER
Melanoma	EFO_0000756	CANCER
Prostate carcinoma	EFO_0001663	CANCER
Type 2 diabetes	EFO_0001360	METABOLIC
Coronary artery disease	EFO_0001645	CVD
Atrial fibrillation	EFO_0000275	CVD
Alzheimer disease	MONDO_0004975	NEUROLOGICAL
Parkinson disease	EFO_0002508	NEUROLOGICAL
Rheumatoid arthritis	EFO_0000685	AUTOIMMUNE
Marfan syndrome	Orphanet_558	RARE
Cystic fibrosis	EFO_0000508	RARE

Step 1.2: Classify Disease Type

Based on disease name and EFO ID, classify into: CANCER, METABOLIC, CVD, NEUROLOGICAL, RARE, AUTOIMMUNE. This determines the Phase 3 routing.

Step 1.3: Parse Genomic Data

Parse each variant/gene into structured format:

"BRCA1 c.68_69delAG" -> {gene: "BRCA1", variant: "c.68_69delAG", type: "frameshift"}
"EGFR L858R" -> {gene: "EGFR", variant: "L858R", type: "missense"}
"CYP2C19 *2/*2" -> {gene: "CYP2C19", genotype: "*2/*2", metabolizer_status: "poor"}
"APOE e4/e4" -> {gene: "APOE", genotype: "e4/e4", risk_allele: "e4"}

Step 1.4: Resolve Gene IDs

# For each gene in profile
result = tu.tools.MyGene_query_genes(query='BRCA1')
# -> hits[0]: {_id: '672', symbol: 'BRCA1', ensembl: {gene: 'ENSG00000012048'}}
ensembl_id = result['hits'][0]['ensembl']['gene']
entrez_id = result['hits'][0]['_id']

Critical Gene IDs (pre-resolved):

Gene	Ensembl ID	Entrez ID	Category
BRCA1	ENSG00000012048	672	Cancer predisposition
BRCA2	ENSG00000139618	675	Cancer predisposition
TP53	ENSG00000141510	7157	Tumor suppressor
EGFR	ENSG00000146648	1956	Cancer driver
BRAF	ENSG00000157764	673	Cancer driver
KRAS	ENSG00000133703	3845	Cancer driver
CYP2D6	ENSG00000100197	1565	Pharmacogenomics
CYP2C19	ENSG00000165841	1557	Pharmacogenomics
SLCO1B1	ENSG00000134538	10599	Pharmacogenomics
VKORC1	ENSG00000167397	79001	Pharmacogenomics
DPYD	ENSG00000188641	1806	Pharmacogenomics
APOE	ENSG00000130203	348	Neurological risk
LDLR	ENSG00000130164	3949	CVD risk
PCSK9	ENSG00000169174	255738	CVD risk
FBN1	ENSG00000166147	2200	Marfan syndrome
CFTR	ENSG00000001626	1080	Cystic fibrosis

---

Phase 2: Genetic Risk Assessment

Step 2.1: Germline Variant Pathogenicity

For each germline variant provided:

# Search ClinVar for variant pathogenicity
result = tu.tools.clinvar_search_variants(gene='BRCA1', significance='pathogenic', limit=50)
# Check if patient's specific variant is in ClinVar

# For rsID variants, get VEP annotation
result = tu.tools.EnsemblVEP_annotate_rsid(variant_id='rs80357906')
# Returns SIFT, PolyPhen predictions, consequence type

# For HGVS variants
result = tu.tools.EnsemblVEP_annotate_hgvs(hgvs_notation='ENST00000357654.9:c.5266dupC', species='homo_sapiens')

Pathogenicity Classification (ACMG-aligned):

Classification	ClinVar Term	Risk Score Points
Pathogenic	Pathogenic	25 (molecular component)
Likely pathogenic	Likely pathogenic	20
VUS	Uncertain significance	10 (conservative)
Likely benign	Likely benign	2
Benign	Benign	0

Step 2.2: Gene-Disease Association Strength

# Get genetic evidence for gene-disease pair
result = tu.tools.OpenTargets_target_disease_evidence(
    ensemblId='ENSG00000012048',  # BRCA1
    efoId='EFO_0000305',         # breast cancer
    size=20
)
# Returns evidence items with scores

Step 2.3: GWAS-Based Polygenic Risk

# Search GWAS associations for disease
result = tu.tools.gwas_get_associations_for_trait(trait='breast cancer')
# Returns associated SNPs with effect sizes

# Search GWAS studies via OpenTargets
result = tu.tools.OpenTargets_search_gwas_studies_by_disease(
    diseaseIds=['EFO_0000305'], size=25
)

# For specific genes, check GWAS hits
result = tu.tools.GWAS_search_associations_by_gene(gene_name='BRCA1')

PRS Estimation (from available GWAS data):

PRS Percentile	Risk Category	Score Points (0-35)
>95th percentile	Very high genetic risk	35
90-95th	High genetic risk	30
75-90th	Elevated genetic risk	25
50-75th	Average-high	18
25-50th	Average-low	12
10-25th	Below average	8
<10th	Low genetic risk	5

Note: With user-provided variants only (not full genotype), estimate approximate PRS by counting known risk alleles and their effect sizes from GWAS catalog. Flag as "estimated - full genotyping recommended for precise PRS."

Step 2.4: Population Frequency

# Check variant frequency in gnomAD
result = tu.tools.gnomad_get_variant(variant_id='1-55505647-G-T')
# Returns allele frequency across populations

Step 2.5: Gene Constraint

# Gene intolerance to loss of function
result = tu.tools.gnomad_get_gene_constraints(gene_symbol='BRCA1')
# Returns pLI, LOEUF scores - high pLI/low LOEUF = haploinsufficiency

Genetic Risk Score Component (0-35 points):

Combine pathogenicity + gene-disease association + PRS:

• Pathogenic variant in disease gene: 25+ points
• Strong GWAS associations (multiple risk alleles): up to 35 points
• VUS in relevant gene: 10-15 points
• No known pathogenic variants but some risk alleles: 5-15 points

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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-medicine-stratification/REFERENCE.md