Skills
skills/wu-yc/labclaw/tooluniverse-precision-medicine-stratification

tooluniverse-precision-medicine-stratification

SKILL.md

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

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

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
OpenTargets_get_diseases_phenotypes_by_target_ensembl ensemblId Disease-phenotype associations Gene-disease links
OpenTargets_target_disease_evidence ensemblId, efoId, size Evidence for target-disease pair Specific gene-disease evidence
OpenTargets_search_gwas_studies_by_disease diseaseIds (array), size {data: {studies: {count, rows}}} GWAS studies
OpenTargets_drug_pharmacogenomics_data chemblId Pharmacogenomic data Drug PGx
MyGene_query_genes query (NOT q) {hits: [{_id, symbol, name, ensembl: {gene}}]} Gene resolution
ensembl_lookup_gene gene_id, species='homo_sapiens' {data: {id, display_name, description, biotype}} REQUIRES species
EnsemblVEP_annotate_rsid variant_id (NOT rsid) VEP annotation with SIFT/PolyPhen Variant impact
EnsemblVEP_annotate_hgvs hgvs_notation, species VEP annotation HGVS variant annotation
ensembl_get_variation variant_id, species Variant details rsID lookup
clinvar_search_variants gene, significance, limit Variant list Search ClinVar
clinvar_get_variant_details variant_id Variant details with clinical significance ClinVar details
clinvar_get_clinical_significance variant_id Clinical significance only Quick pathogenicity
civic_search_evidence_items therapy_name, disease_name {data: {evidenceItems: {nodes}}} Clinical evidence
civic_search_variants name, gene_name {data: {variants: {nodes}}} Variant clinical significance
civic_search_assertions therapy_name, disease_name {data: {assertions: {nodes}}} Clinical assertions
cBioPortal_get_mutations study_id, gene_list (STRING, not array) {status, data: [{...}]} Somatic mutation data
gwas_get_associations_for_trait trait GWAS associations Trait-SNP associations
gwas_search_associations query GWAS associations Broad GWAS search
gwas_get_snps_for_gene gene SNPs associated with gene Gene GWAS hits
GWAS_search_associations_by_gene gene_name Gene GWAS associations Gene-trait links
PharmGKB_get_clinical_annotations query Clinical annotations Drug-gene-phenotype
PharmGKB_get_dosing_guidelines query Dosing guidelines PGx dosing
PharmGKB_search_variants query Variant PGx data PGx variant search
PharmGKB_get_gene_details query Gene PGx details PGx gene info
PharmGKB_get_drug_details query Drug PGx details Drug PGx info
fda_pharmacogenomic_biomarkers drug_name, biomarker, limit {count, shown, results: [{Drug, Biomarker, ...}]} FDA PGx biomarkers
FDA_get_pharmacogenomics_info_by_drug_name drug_name, limit {meta, results} FDA PGx label info
FDA_get_indications_by_drug_name drug_name, limit {meta, results} FDA indications
FDA_get_clinical_studies_info_by_drug_name drug_name, limit {meta, results} Clinical study data
FDA_get_contraindications_by_drug_name drug_name, limit {meta, results} Contraindications
FDA_get_warnings_by_drug_name drug_name, limit {meta, results} Warnings
FDA_get_boxed_warning_info_by_drug_name drug_name, limit May return NOT_FOUND Boxed warnings
FDA_get_drug_interactions_by_drug_name drug_name, limit {meta, results} DDI info
drugbank_get_drug_basic_info_by_drug_name_or_id query, case_sensitive, exact_match, limit Drug basic info ALL 4 REQUIRED
drugbank_get_targets_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit Drug targets ALL 4 REQUIRED
drugbank_get_pharmacology_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit Pharmacology ALL 4 REQUIRED
drugbank_get_indications_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit Indications ALL 4 REQUIRED
drugbank_get_drug_interactions_by_drug_name_or_id query, case_sensitive, exact_match, limit DDI data ALL 4 REQUIRED
drugbank_get_safety_by_drug_name_or_drugbank_id query, case_sensitive, exact_match, limit Safety data ALL 4 REQUIRED
enrichr_gene_enrichment_analysis gene_list (array), libs (array, REQUIRED) Enrichment results Key libs: KEGG_2021_Human, Reactome_2022, GO_Biological_Process_2023
ReactomeAnalysis_pathway_enrichment identifiers (space-separated string) {data: {pathways: [{pathway_id, name, p_value, ...}]}} Pathway enrichment
Reactome_map_uniprot_to_pathways id (UniProt accession) List of pathways Gene-to-pathway
STRING_get_interaction_partners protein_ids (array), species (9606), limit Interaction partners PPI network
STRING_functional_enrichment protein_ids (array), species (9606) Functional enrichment Network enrichment
HPA_get_cancer_prognostics_by_gene gene_name Cancer prognostic data Prognostic markers
HPA_get_rna_expression_by_source gene_name, source_type, source_name (ALL 3) Expression data Tissue expression
gnomad_get_gene_constraints gene_symbol Gene constraint metrics LoF intolerance
gnomad_get_variant variant_id Variant frequency Population frequency
clinical_trials_search action='search_studies', condition, intervention, limit {total_count, studies} Trial search
search_clinical_trials query_term (REQUIRED), condition, intervention, pageSize {studies, total_count} Alternative trial search
PubMed_search_articles query, max_results Plain list of dicts Literature
PubMed_Guidelines_Search query, limit (REQUIRED) List of guideline articles Clinical guidelines (may require API key)
UniProt_get_function_by_accession accession List of strings Protein function
UniProt_get_disease_variants_by_accession accession Disease variants Known pathogenic variants

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

Phase 3: Disease-Specific Molecular Stratification

CANCER PATH (Phase 3C)

Step 3C.1: Molecular Subtyping

# Get somatic mutation landscape from cBioPortal
result = tu.tools.cBioPortal_get_mutations(
    study_id='brca_tcga_pub',  # breast cancer TCGA
    gene_list='BRCA1 BRCA2 TP53 PIK3CA ESR1 ERBB2'  # STRING, not array
)
# Returns mutation frequencies, types

# Check cancer prognostic markers
result = tu.tools.HPA_get_cancer_prognostics_by_gene(gene_name='ESR1')
# Returns prognostic data for breast cancer

Cancer-Specific Subtype Definitions:

Cancer Subtype System Key Markers High-Risk Features
Breast Luminal A/B, HER2+, TNBC ER, PR, HER2, Ki67 TNBC, high Ki67, TP53 mut
NSCLC Adenocarcinoma, squamous EGFR, ALK, ROS1, KRAS, PD-L1 KRAS G12C, no driver = chemoIO
CRC MSI-H vs MSS, CMS1-4 KRAS, BRAF, MSI, CMS BRAF V600E, MSS
Melanoma BRAF-mut, NRAS-mut, wild-type BRAF, NRAS, KIT, NF1 NRAS, uveal
Prostate Luminal vs basal, BRCA status AR, BRCA1/2, SPOP, TMPRSS2:ERG BRCA2, neuroendocrine

Step 3C.2: TMB/MSI/HRD Assessment

If TMB provided:

# Check FDA TMB-H approvals
result = tu.tools.fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab', limit=100)
# Look for "Tumor Mutational Burden" in Biomarker field
Biomarker High-Risk Threshold Clinical Significance
TMB >= 10 mut/Mb (FDA cutoff) Pembrolizumab eligible (tissue-agnostic)
MSI-H MSI-high or dMMR Pembrolizumab/nivolumab eligible
HRD HRD-positive PARP inhibitor eligible

Step 3C.3: Prognostic Stratification

Combine stage + molecular features:

Stage Low-Risk Molecular High-Risk Molecular Score (0-30 clinical)
I Favorable subtype Unfavorable subtype 5-10
II Favorable subtype Unfavorable subtype 10-18
III Any Any 18-25
IV Any Any 25-30

METABOLIC PATH (Phase 3M)

Step 3M.1: Clinical Risk Integration

# Check genetic risk factors for T2D
result = tu.tools.GWAS_search_associations_by_gene(gene_name='TCF7L2')
# TCF7L2 is strongest T2D risk gene

# Check monogenic diabetes genes
result = tu.tools.OpenTargets_target_disease_evidence(
    ensemblId='ENSG00000148737',  # TCF7L2
    efoId='EFO_0001360',         # T2D
    size=20
)

T2D Stratification:

Risk Factor Low Risk Moderate Risk High Risk Score Points
HbA1c <6.5% 6.5-8.0% >8.0% 5-30
Genetic risk No risk alleles 1-3 risk alleles MODY gene/many risk alleles 5-25
Complications None Microalbuminuria Retinopathy, neuropathy 0-20
Duration <5 years 5-15 years >15 years 0-10

CVD PATH (Phase 3V)

# Check PCSK9 and LDLR variants
result = tu.tools.clinvar_search_variants(gene='LDLR', significance='pathogenic', limit=20)
# Familial hypercholesterolemia check

# Check statin-relevant PGx
result = tu.tools.PharmGKB_get_clinical_annotations(query='SLCO1B1')
# SLCO1B1 *5 -> increased statin myopathy risk

CVD Risk Integration:

Factor Score Points
LDL >190 mg/dL 15
FH gene mutation (LDLR/APOB/PCSK9) 20
ASCVD >20% 10-year risk 30
Family hx premature CVD 10
Lipoprotein(a) elevated 8
Multiple GWAS risk alleles 5-15

RARE DISEASE PATH (Phase 3R)

# Check causal variant in disease gene
result = tu.tools.clinvar_search_variants(gene='FBN1', significance='pathogenic', limit=50)
# Marfan syndrome - FBN1 pathogenic variants

# Genotype-phenotype correlation
result = tu.tools.UniProt_get_disease_variants_by_accession(accession='P35555')  # FBN1 UniProt
# Known disease variants and their phenotypes

Rare Disease Risk Assessment:

Finding Risk Level Score Points
Pathogenic variant in causal gene Definitive 30
Likely pathogenic in causal gene Strong 25
VUS in causal gene Moderate 15
Family history + partial phenotype Suggestive 10
Single phenotype feature only Low 5

Phase 4: Pharmacogenomic Profiling

Step 4.1: Drug-Metabolizing Enzyme Genotypes

# PharmGKB clinical annotations for CYP2C19
result = tu.tools.PharmGKB_get_clinical_annotations(query='CYP2C19')
# Returns drug-gene pairs with clinical annotation levels

# FDA pharmacogenomic biomarkers
result = tu.tools.fda_pharmacogenomic_biomarkers(drug_name='clopidogrel', limit=50)
# CYP2C19 poor metabolizer -> reduced clopidogrel efficacy

# PharmGKB dosing guidelines
result = tu.tools.PharmGKB_get_dosing_guidelines(query='CYP2C19')
# CPIC dosing guidelines

Key Pharmacogenes and Clinical Impact:

Gene Star Alleles Metabolizer Status Clinical Impact Score Points
CYP2D6 *4/*4, *5/*5 Poor metabolizer Codeine, tamoxifen, many antidepressants 8
CYP2C19 *2/*2, *2/*3 Poor metabolizer Clopidogrel, voriconazole, PPIs 8
CYP2C9 *2/*3, *3/*3 Poor metabolizer Warfarin, NSAIDs, phenytoin 5
SLCO1B1 *5/*5 Decreased function Statin myopathy (simvastatin) 5
DPYD *2A DPD deficient 5-FU/capecitabine severe toxicity 10
VKORC1 -1639G>A Warfarin sensitive Lower warfarin dose needed 5
UGT1A1 *28/*28 Poor glucuronidator Irinotecan toxicity 5
TPMT *2, *3A, *3C Poor metabolizer Thiopurine toxicity 8
HLA-B*5701 Present N/A Abacavir hypersensitivity 10
HLA-B*1502 Present N/A Carbamazepine SJS/TEN 10

Step 4.2: Treatment-Specific PGx

# For the specific disease, identify relevant drugs and check PGx
# Example: breast cancer -> tamoxifen -> CYP2D6
result = tu.tools.PharmGKB_get_drug_details(query='tamoxifen')
# Returns PGx annotations for tamoxifen

# Get FDA PGx biomarkers for disease area
result = tu.tools.fda_pharmacogenomic_biomarkers(biomarker='CYP2D6', limit=100)
# All drugs with CYP2D6 PGx in FDA labels

Step 4.3: Drug Target Variants

# Check if patient has variants in drug targets
result = tu.tools.PharmGKB_search_variants(query='VKORC1')
# VKORC1 variants affecting warfarin response

Pharmacogenomic Risk Score (0-10 points):

  • Poor metabolizer for treatment-relevant CYP: 8-10 points
  • Intermediate metabolizer: 4-5 points
  • High-risk HLA allele: 8-10 points
  • Drug target variant: 3-5 points
  • Normal metabolizer, no actionable PGx: 0 points

Phase 5: Comorbidity & Drug Interaction Risk

Step 5.1: Comorbidity Analysis

# Check disease-disease overlap via shared genetic targets
result = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(
    efoId='EFO_0001360',  # T2D
    size=50
)
# Compare top targets between primary disease and comorbidities

# Literature on comorbidity
result = tu.tools.PubMed_search_articles(
    query='type 2 diabetes cardiovascular comorbidity risk',
    max_results=5
)

Step 5.2: Drug-Drug Interaction Risk

# If current medications provided, check DDI
result = tu.tools.drugbank_get_drug_interactions_by_drug_name_or_id(
    query='metformin',
    case_sensitive=False,
    exact_match=False,
    limit=20
)

# FDA DDI data
result = tu.tools.FDA_get_drug_interactions_by_drug_name(drug_name='metformin', limit=5)

Step 5.3: PGx-Amplified DDI Risk

If patient is a CYP2D6 poor metabolizer AND taking a CYP2D6 inhibitor -> compounded risk.

Interaction Type Risk Level Management
PGx PM + CYP inhibitor Very high Alternative drug or dose reduction
PGx IM + CYP inhibitor High Monitor closely, possible dose reduction
PGx normal + CYP inhibitor Moderate Standard monitoring
No interacting drugs Low Standard care

Phase 6: Molecular Pathway Analysis

Step 6.1: Dysregulated Pathways

# Pathway enrichment for affected genes
gene_list = ['BRCA1', 'TP53', 'PIK3CA']  # from patient mutations
result = tu.tools.enrichr_gene_enrichment_analysis(
    gene_list=gene_list,
    libs=['KEGG_2021_Human', 'Reactome_2022']
)
# Returns enriched pathways with p-values

# Reactome pathway analysis
# First get UniProt IDs, then map to pathways
result = tu.tools.Reactome_map_uniprot_to_pathways(id='P38398')  # BRCA1 UniProt
# Returns list of pathways involving BRCA1

Step 6.2: Network Analysis

# Protein-protein interaction network
result = tu.tools.STRING_get_interaction_partners(
    protein_ids=['BRCA1', 'TP53'],
    species=9606,
    limit=20
)

# Functional enrichment of network
result = tu.tools.STRING_functional_enrichment(
    protein_ids=['BRCA1', 'TP53', 'PALB2', 'RAD51'],
    species=9606
)

Step 6.3: Druggable Pathway Targets

# Check tractability of pathway nodes
for gene in pathway_genes:
    result = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblId=ensembl_id)
    # Returns small molecule, antibody, PROTAC tractability

Key Druggable Pathways:

Pathway Key Nodes Drug Classes Cancer Relevance
PI3K/AKT/mTOR PIK3CA, AKT1, MTOR PI3K inhibitors, mTOR inhibitors Breast, endometrial
RAS/MAPK KRAS, BRAF, MEK1/2 KRAS G12C inhibitors, BRAF inhibitors Lung, CRC, melanoma
DNA damage repair BRCA1/2, ATM, PALB2 PARP inhibitors Breast, ovarian, prostate
Cell cycle CDK4/6, RB1, CCND1 CDK4/6 inhibitors Breast
Immunocheckpoint PD-1, PD-L1, CTLA-4 ICIs Pan-cancer
Wnt/beta-catenin APC, CTNNB1, TCF Wnt inhibitors (investigational) CRC

Phase 7: Clinical Evidence & Guidelines

Step 7.1: Guideline-Based Risk Categories

# Search clinical guidelines in PubMed
result = tu.tools.PubMed_Guidelines_Search(
    query='NCCN breast cancer BRCA1 treatment guidelines',
    max_results=5
)

# Search general evidence
result = tu.tools.PubMed_search_articles(
    query='BRCA1 breast cancer treatment stratification',
    max_results=10
)

Guideline References by Disease:

Disease Category Guidelines Key Stratification
Breast cancer NCCN, ASCO, St. Gallen Luminal A/B, HER2+, TNBC, BRCA status
NSCLC NCCN, ESMO Driver mutation status, PD-L1, TMB
CRC NCCN MSI, RAS/BRAF, sidedness
T2D ADA Standards HbA1c, CVD risk, CKD stage
CVD ACC/AHA ASCVD risk score, LDL goals, PGx
AF ACC/AHA/HRS CHA2DS2-VASc, anticoagulant selection
Rare disease ACMG/AMP Variant classification, genetic counseling

Step 7.2: FDA-Approved Therapies

# Get approved drugs for disease
result = tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(
    efoId='EFO_0000305',  # breast cancer
    size=50
)
# Returns all known drugs with clinical status

# Check specific drug FDA info
result = tu.tools.FDA_get_indications_by_drug_name(drug_name='olaparib', limit=5)
# PARP inhibitor for BRCA-mutated breast cancer

# Get drug mechanism
result = tu.tools.FDA_get_mechanism_of_action_by_drug_name(drug_name='olaparib', limit=5)

Step 7.3: Biomarker-Drug Evidence

# CIViC evidence for biomarker-drug pair
result = tu.tools.civic_search_evidence_items(
    therapy_name='olaparib',
    disease_name='breast cancer'
)
# Returns clinical evidence items with evidence levels

# DrugBank for drug details
result = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(
    query='olaparib',
    case_sensitive=False,
    exact_match=False,
    limit=5
)

Phase 8: Clinical Trial Matching

Step 8.1: Biomarker-Driven Trials

# Search trials matching molecular profile
result = tu.tools.clinical_trials_search(
    action='search_studies',
    condition='breast cancer',
    intervention='PARP inhibitor',
    limit=10
)
# Returns {total_count, studies: [{nctId, title, status, conditions}]}

# Alternative search
result = tu.tools.search_clinical_trials(
    query_term='BRCA1 breast cancer',
    condition='breast cancer',
    intervention='olaparib',
    pageSize=10
)

Step 8.2: Precision Medicine Trials

# Search basket/umbrella trials
result = tu.tools.search_clinical_trials(
    query_term='precision medicine biomarker-driven',
    condition='breast cancer',
    pageSize=10
)

# Search risk-adapted trials
result = tu.tools.search_clinical_trials(
    query_term='high risk BRCA1',
    condition='breast cancer',
    pageSize=10
)

Step 8.3: Trial Details

# Get details for promising trials
result = tu.tools.clinical_trials_get_details(
    action='get_study_details',
    nct_id='NCT03344965'
)
# Returns full study protocol

Phase 9: Integrated Scoring & Recommendations

Precision Medicine Risk Score (0-100)

Score Components

Genetic Risk Component (0-35 points):

Scenario Points
Pathogenic variant in high-penetrance disease gene (BRCA1, LDLR, FBN1) 30-35
Multiple moderate-risk variants (GWAS hits + moderate penetrance) 20-28
High PRS (>90th percentile) with no known pathogenic variants 25-30
Single moderate-risk variant 12-18
VUS in relevant gene 8-12
Average PRS, no pathogenic variants 5-10
Low genetic risk (low PRS, no risk alleles) 0-5

Clinical Risk Component (0-30 points):

Disease Type Factor Low (0-8) Moderate (10-20) High (22-30)
Cancer Stage I II-III IV
T2D HbA1c <7% 7-9% >9%
CVD ASCVD 10-yr <10% 10-20% >20%
Neuro Biomarker status No biomarkers Mild changes Established
Rare Phenotype match Partial Moderate Full phenotype

Molecular Features Component (0-25 points):

Feature Points
Cancer: High-risk driver mutations (TP53+PIK3CA, KRAS G12C) 20-25
Cancer: Actionable mutation (EGFR, BRAF V600E) 15-20
Cancer: High TMB or MSI-H (favorable for ICI) 10-15
Metabolic: Monogenic form (MODY, FH) 20-25
Metabolic: Multiple metabolic risk variants 10-15
CVD: FH gene mutation 20-25
Rare: Complete genotype-phenotype match 20-25
VUS requiring further workup 5-10

Pharmacogenomic Risk Component (0-10 points):

Finding Points
Poor metabolizer for treatment-critical CYP + high-risk HLA 10
Poor metabolizer for treatment-critical CYP 7-8
Intermediate metabolizer for relevant CYP 4-5
Drug target variant (e.g., VKORC1 for warfarin) 3-5
No actionable PGx findings 0-2

Risk Tier Assignment

Total Score Risk Tier Management Intensity
75-100 VERY HIGH Intensive treatment, subspecialty referral, clinical trial enrollment
50-74 HIGH Aggressive treatment, close monitoring, molecular tumor board
25-49 INTERMEDIATE Standard treatment, guideline-based care, PGx-guided dosing
0-24 LOW Surveillance, prevention, risk factor modification

Treatment Algorithm

Based on disease type + risk tier + molecular profile + PGx:

Cancer Treatment Algorithm

IF actionable mutation present:
  1st line: Targeted therapy (e.g., EGFR TKI, BRAF inhibitor, PARP inhibitor)
  2nd line: Immunotherapy (if TMB-H or MSI-H) OR chemotherapy
  3rd line: Clinical trial OR alternative targeted therapy

IF no actionable mutation:
  IF TMB-H or MSI-H:
    1st line: Immunotherapy (pembrolizumab)
    2nd line: Chemotherapy
  ELSE:
    1st line: Standard chemotherapy (disease-specific)
    2nd line: Consider clinical trials

PGx adjustments:
  - DPYD deficient -> AVOID fluoropyrimidines or reduce dose 50%
  - UGT1A1 *28/*28 -> Reduce irinotecan dose
  - CYP2D6 PM + tamoxifen -> Switch to aromatase inhibitor

Metabolic/CVD Treatment Algorithm

IF monogenic form (MODY, FH):
  Disease-specific therapy (e.g., sulfonylureas for HNF1A-MODY, PCSK9i for FH)

IF polygenic risk:
  Standard guidelines (ADA, ACC/AHA)
  PGx-guided drug selection:
    - CYP2C19 PM -> Alternative to clopidogrel (ticagrelor, prasugrel)
    - SLCO1B1 *5 -> Lower statin dose or alternative statin
    - VKORC1 variant -> Warfarin dose adjustment or DOAC

Monitoring Plan

Component Frequency Method
Molecular biomarkers Per guideline Liquid biopsy, tissue biopsy
Clinical markers 3-6 months Labs, imaging
PGx-guided drug levels As needed TDM
Disease progression Per stage/risk Imaging, biomarkers
Comorbidity screening Annually Labs, risk calculators

Output Report Structure

Generate a comprehensive markdown report saved to: [PATIENT_ID]_precision_medicine_report.md

Required Sections

# Precision Medicine Stratification Report

## Executive Summary
- **Patient Profile**: [Disease, key features]
- **Precision Medicine Risk Score**: [X]/100
- **Risk Tier**: [LOW / INTERMEDIATE / HIGH / VERY HIGH]
- **Key Finding**: [One-line summary of most actionable finding]
- **Primary Recommendation**: [One-line treatment recommendation]

## 1. Patient Profile
### Disease Classification
### Genomic Data Summary
### Clinical Parameters

## 2. Genetic Risk Assessment
### Germline Variant Analysis
### Gene-Disease Association Evidence
### Polygenic Risk Estimation
### Population Frequency Data

## 3. Disease-Specific Stratification
### [Cancer: Molecular Subtype / Metabolic: Risk Integration / etc.]
### Prognostic Markers
### Risk Group Assignment

## 4. Pharmacogenomic Profile
### Drug-Metabolizing Enzymes
### Drug Target Variants
### Treatment-Specific PGx Recommendations
### FDA PGx Biomarker Status

## 5. Comorbidity & Drug Interaction Risk
### Disease-Disease Overlap
### Drug-Drug Interactions
### PGx-Amplified DDI Risk

## 6. Dysregulated Pathways
### Key Pathways Affected
### Druggable Targets
### Network Analysis

## 7. Clinical Evidence & Guidelines
### Guideline-Based Classification
### FDA-Approved Therapies
### Biomarker-Drug Evidence

## 8. Clinical Trial Matches
### Biomarker-Driven Trials
### Precision Medicine Trials
### Risk-Adapted Trials

## 9. Integrated Risk Score
### Score Breakdown
| Component | Points | Max | Basis |
|-----------|--------|-----|-------|
| Genetic Risk | X | 35 | [Details] |
| Clinical Risk | X | 30 | [Details] |
| Molecular Features | X | 25 | [Details] |
| Pharmacogenomic Risk | X | 10 | [Details] |
| **TOTAL** | **X** | **100** | |

### Risk Tier: [TIER]
### Confidence Level: [HIGH/MODERATE/LOW]

## 10. Treatment Algorithm
### 1st Line Recommendation
### 2nd Line Options
### 3rd Line / Investigational
### PGx Dose Adjustments

## 11. Monitoring Plan
### Biomarker Surveillance
### Imaging Schedule
### Risk Reassessment Timeline

## 12. Outcome Predictions
### Disease-Specific Prognosis
### Treatment Response Prediction
### Projected Timeline

## Completeness Checklist
| Data Layer | Available | Analyzed | Key Finding |
|-----------|-----------|----------|-------------|
| Disease disambiguation | Y/N | Y/N | [EFO ID] |
| Germline variants | Y/N | Y/N | [Pathogenicity] |
| Somatic mutations | Y/N | Y/N | [Drivers] |
| Gene expression | Y/N | Y/N | [Subtype] |
| PGx genotypes | Y/N | Y/N | [Metabolizer status] |
| Clinical biomarkers | Y/N | Y/N | [Key values] |
| GWAS/PRS | Y/N | Y/N | [Risk percentile] |
| Pathway analysis | Y/N | Y/N | [Key pathways] |
| Clinical trials | Y/N | Y/N | [N matches] |
| Guidelines | Y/N | Y/N | [Guideline tier] |

## Evidence Sources
[List all databases and tools used with specific citations]

Evidence Grading

All findings must be graded:

Tier Level Sources Weight
T1 Clinical/regulatory evidence FDA labels, NCCN guidelines, PharmGKB Level 1A/1B, ClinVar pathogenic Highest
T2 Strong experimental evidence CIViC Level A/B, OpenTargets high-score, GWAS p<5e-8, clinical trials High
T3 Moderate evidence PharmGKB Level 2, CIViC Level C, GWAS suggestive, preclinical data Moderate
T4 Computational/predicted VEP predictions, pathway inference, network analysis, PRS estimates Supportive

Completeness Requirements

Minimum deliverables for a valid stratification report:

  1. Disease resolved to EFO/ontology ID
  2. At least one genetic risk assessment completed (germline OR somatic OR PRS)
  3. Disease-specific stratification with risk group
  4. At least one pharmacogenomic assessment (even if "no actionable findings")
  5. Pathway analysis with at least one pathway identified
  6. Treatment recommendation with evidence tier
  7. At least one clinical trial match attempted
  8. Precision Medicine Risk Score calculated with all available components
  9. Risk tier assigned
  10. Monitoring plan outlined

Common Use Patterns

Pattern 1: Cancer Patient with Actionable Mutation

Input: "Breast cancer, BRCA1 pathogenic variant, ER+/HER2-, stage IIA, age 45" Key phases: Phase 1 (cancer classification) -> Phase 2 (BRCA1 pathogenicity) -> Phase 3C (molecular subtype = Luminal B, BRCA+) -> Phase 4 (check CYP2D6 for tamoxifen) -> Phase 7 (NCCN guidelines: PARP inhibitor eligible) -> Phase 8 (PARP inhibitor trials) -> Phase 9 (Risk Score ~55-65, HIGH tier)

Pattern 2: Metabolic Disease with PGx Concern

Input: "Type 2 diabetes, HbA1c 8.5%, CYP2C19 *2/*2, on clopidogrel for CAD stent" Key phases: Phase 1 (T2D + CAD) -> Phase 2 (T2D genetic risk) -> Phase 3M (HbA1c-based risk) -> Phase 4 (CYP2C19 PM: clopidogrel ineffective!) -> Phase 5 (T2D-CAD comorbidity) -> Phase 9 (Risk Score ~50-60, HIGH, clopidogrel switch urgent)

Pattern 3: CVD Risk Stratification

Input: "LDL 190 mg/dL, SLCO1B1*5 heterozygous, family history of MI at age 48" Key phases: Phase 1 (CVD/FH evaluation) -> Phase 2 (FH gene check: LDLR, APOB, PCSK9) -> Phase 3V (ASCVD risk) -> Phase 4 (SLCO1B1 *5: statin myopathy risk) -> Phase 7 (ACC/AHA guidelines) -> Phase 9 (Risk Score ~45-55, statin dose reduction or rosuvastatin)

Pattern 4: Rare Disease Diagnosis

Input: "Marfan syndrome suspected, FBN1 c.4082G>A, tall stature, aortic root dilation" Key phases: Phase 1 (Marfan/rare) -> Phase 2 (FBN1 variant pathogenicity) -> Phase 3R (genotype-phenotype match) -> Phase 7 (Ghent criteria) -> Phase 9 (Risk Score depends on aortic involvement)

Pattern 5: Neurological Risk Assessment

Input: "Family history of Alzheimer's, APOE e4/e4, age 55" Key phases: Phase 1 (AD/neuro) -> Phase 2 (APOE e4/e4 = highest genetic risk) -> Phase 3 (AD-specific risk) -> Phase 4 (PGx for potential treatments) -> Phase 7 (guidelines) -> Phase 9 (Risk Score ~60-75, HIGH)

Pattern 6: Comprehensive Cancer with Full Molecular

Input: "NSCLC, EGFR L858R, TMB 25 mut/Mb, PD-L1 80%, stage IV, no EGFR T790M" Key phases: All phases. Phase 3C critical: EGFR L858R = EGFR TKI eligible, high TMB + PD-L1 = ICI eligible. Treatment algorithm: 1st line osimertinib (EGFR TKI), 2nd line ICI (if progression). Risk Score ~70-80 (VERY HIGH due to stage IV).

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