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Precision Medicine Patient Stratification

Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies.

KEY PRINCIPLES :

1. Report-first - Create report file FIRST, then populate progressively
2. Disease-specific logic - Cancer vs metabolic vs rare disease pipelines diverge at Phase 3
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)
6. Source-referenced - Every statement cites the tool/database source
7. English-first queries - Always use English terms in tool calls

Reference files (same directory):

• TOOLS_REFERENCE.md - Tool parameters, response formats, phase-by-phase tool lists
• SCORING_REFERENCE.md - Scoring matrices, risk tiers, pathogenicity tables, PGx tables
• REPORT_TEMPLATE.md - Output report template, treatment algorithms, completeness requirements
• EXAMPLES.md - Six worked examples (cancer, metabolic, NSCLC, CVD, rare, neuro)
• QUICK_START.md - Sample prompts and output summary

When to Use

Apply when user asks about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy for any disease with genomic/clinical data.

NOT for (use other skills instead):

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

Input Parsing

Required

• Disease/condition : Free-text disease name
• At least one of : Germline variants, somatic mutations, gene list, or clinical biomarkers

Optional (improves stratification)

• Age, sex, ethnicity, disease stage, comorbidities, prior treatments, family history
• Current medications (for DDI and PGx), stratification goal

Disease Type Classification

Classify into one category (determines Phase 3 routing):

Critical Tool Parameter Notes

See TOOLS_REFERENCE.md for full details. Key gotchas:

• MyGene_query_genes : param is query (NOT q)
• EnsemblVEP_annotate_rsid : param is variant_id (NOT rsid)
• ensembl_lookup_gene : REQUIRES species='homo_sapiens'
• DrugBank tools : ALL require 4 params: query, case_sensitive, exact_match, limit
• cBioPortal_get_mutations : gene_list is a STRING (space-separated), not array

Workflow Overview

Phase 1: Disease Disambiguation & Profile Standardization
Phase 2: Genetic Risk Assessment
Phase 3: Disease-Specific Molecular Stratification (routes by disease type)
Phase 4: Pharmacogenomic Profiling
Phase 5: Comorbidity & Drug Interaction Risk
Phase 6: Molecular Pathway Analysis
Phase 7: Clinical Evidence & Guidelines
Phase 8: Clinical Trial Matching
Phase 9: Integrated Scoring & Recommendations

Phase 1: Disease Disambiguation & Profile Standardization

1. Resolve disease to EFO ID using OpenTargets_get_disease_id_description_by_name
2. Classify disease type (CANCER/METABOLIC/CVD/NEUROLOGICAL/RARE/AUTOIMMUNE)
3. Parse genomic data into structured format (gene, variant, type)
4. Resolve gene IDs using MyGene_query_genes to get Ensembl/Entrez IDs

Phase 2: Genetic Risk Assessment

1. Germline variant pathogenicity : clinvar_search_variants, EnsemblVEP_annotate_rsid/_hgvs
2. Gene-disease association : OpenTargets_target_disease_evidence
3. GWAS polygenic risk : gwas_get_associations_for_trait, OpenTargets_search_gwas_studies_by_disease
4. Population frequency : gnomad_get_variant
5. Gene constraint : gnomad_get_gene_constraints (pLI, LOEUF scores)

Scoring: See SCORING_REFERENCE.md for genetic risk score component (0-35 points).

Phase 3: Disease-Specific Molecular Stratification

CANCER PATH

1. Molecular subtyping : cBioPortal_get_mutations, HPA_get_cancer_prognostics_by_gene
2. TMB/MSI/HRD : fda_pharmacogenomic_biomarkers for FDA cutoffs
3. Prognostic stratification : Combine stage + molecular features

METABOLIC PATH

1. Genetic risk integration : GWAS_search_associations_by_gene, OpenTargets_target_disease_evidence
2. Complication risk : Based on HbA1c, duration, existing complications

CVD PATH

1. FH gene check : clinvar_search_variants for LDLR, APOB, PCSK9
2. Statin PGx : PharmGKB_get_clinical_annotations for SLCO1B1

RARE DISEASE PATH

1. Causal variant identification : clinvar_search_variants
2. Genotype-phenotype : UniProt_get_disease_variants_by_accession

Scoring: See SCORING_REFERENCE.md for disease-specific tables.

Phase 4: Pharmacogenomic Profiling

1. Drug-metabolizing enzymes : PharmGKB_get_clinical_annotations, PharmGKB_get_dosing_guidelines
2. FDA PGx biomarkers : fda_pharmacogenomic_biomarkers (use limit=1000)
3. Treatment-specific PGx : PharmGKB_get_drug_details

Scoring: See SCORING_REFERENCE.md for PGx risk score (0-10 points).

Phase 5: Comorbidity & Drug Interaction Risk

1. Disease overlap : OpenTargets_get_associated_targets_by_disease_efoId
2. DDI check : drugbank_get_drug_interactions_by_drug_name_or_id, FDA_get_drug_interactions_by_drug_name
3. PGx-amplified DDI : If PM genotype + CYP inhibitor, flag compounded risk

Phase 6: Molecular Pathway Analysis

1. Pathway enrichment : enrichr_gene_enrichment_analysis (libs: KEGG_2021_Human, Reactome_2022, GO_Biological_Process_2023)
2. Reactome mapping : ReactomeAnalysis_pathway_enrichment, Reactome_map_uniprot_to_pathways
3. Network analysis : STRING_get_interaction_partners, STRING_functional_enrichment
4. Druggable targets : OpenTargets_get_target_tractability_by_ensemblID

Phase 7: Clinical Evidence & Guidelines

1. Guidelines search : PubMed_Guidelines_Search (fallback: PubMed_search_articles)
2. FDA-approved therapies : OpenTargets_get_associated_drugs_by_disease_efoId, FDA_get_indications_by_drug_name
3. Biomarker-drug evidence : civic_search_evidence_items, civic_search_assertions

Phase 8: Clinical Trial Matching

1. Biomarker-driven trials : clinical_trials_search with condition + intervention
2. Precision medicine trials : search_clinical_trials for basket/umbrella trials

Phase 9: Integrated Scoring & Recommendations

Score Components (total 0-100)

• Genetic Risk (0-35): Pathogenicity + gene-disease association + PRS
• Clinical Risk (0-30): Stage/biomarkers/comorbidities
• Molecular Features (0-25): Driver mutations, subtypes, actionable targets
• Pharmacogenomic Risk (0-10): Metabolizer status, HLA alleles

Risk Tiers

Output

Generate report per REPORT_TEMPLATE.md. See SCORING_REFERENCE.md for detailed scoring matrices.

Common Use Patterns

See EXAMPLES.md for six detailed worked examples:

1. Cancer + actionable mutation : Breast cancer, BRCA1, ER+/HER2- -> Score ~55-65 (HIGH)
2. Metabolic + PGx concern : T2D, CYP2C19 PM on clopidogrel -> Score ~55-65 (HIGH)
3. NSCLC comprehensive : EGFR L858R, TMB 25, PD-L1 80% -> Score ~75-85 (VERY HIGH)
4. CVD risk : LDL 190, SLCO1B1*5, family hx MI -> Score ~50-60 (HIGH)
5. Rare disease : Marfan, FBN1 variant -> Score ~55-65 (HIGH)
6. Neurological risk : APOE e4/e4, family hx Alzheimer's -> Score ~60-72 (HIGH)

Weekly Installs

125

Repository

mims-harvard/to…universe

GitHub Stars

1.2K

First Seen

Feb 19, 2026

Security Audits

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Installed on

codex122

opencode121

gemini-cli121

github-copilot120

cursor118

kimi-cli117