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

---

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).