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tooluniverse-precision-medicine-stratification Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
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tooluniverse-multiomic-disease-characterization Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
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7 arquivos SCORING_REFERENCE.md 9.2 KB name tooluniverse-precision-medicine-stratification description Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
Precision Medicine Patient Stratification
Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies.
Reasoning Before Searching
Stratification means splitting patients into groups that respond differently to a treatment or have different prognoses. Ask these questions before running any tools:
What molecular feature predicts response? Candidates: somatic mutation (e.g., EGFR L858R), germline variant (e.g., BRCA1 LoF), expression level (e.g., HER2 overexpression), germline pharmacogenomic variant (e.g., CYP2C19 PM), or composite biomarker (e.g., TMB-H + MSI-H).Is the predictive feature actionable? Knowing it must change treatment — either the drug choice, dose, or monitoring plan. A variant with prognostic value but no therapeutic consequence is not a stratification biomarker.What is the evidence level for the stratifier? FDA-approved companion diagnostic (T1) vs. exploratory (T4) changes how much weight to place on the finding.
Route to the correct Phase 3 path BEFORE running Phase 2 tools — cancer, metabolic, CVD, rare disease, and autoimmune pipelines require different stratifiers.
LOOK UP DON'T GUESS : Never assume a variant is pathogenic, never assume a gene is relevant to a disease, never assign metabolizer status without PharmGKB or CPIC evidence.
KEY PRINCIPLES :
Report-first - Create report file FIRST, then populate progressivelyDisease-specific logic - Cancer vs metabolic vs rare disease pipelines diverge at Phase 3
Multi-level integration - Germline + somatic + expression + clinical data layers
Evidence-graded - Every finding has an evidence tier (T1-T4)
Quantitative output - Precision Medicine Risk Score (0-100)
Source-referenced - Every statement cites the tool/database source
English-first queries - Always use English terms in tool callsReference files (same directory):
TOOLS_REFERENCE.md - Tool parameters, response formats, phase-by-phase tool listsSCORING_REFERENCE.md - Scoring matrices, risk tiers, pathogenicity tables, PGx tablesREPORT_TEMPLATE.md - Output report template, treatment algorithms, completeness requirementsEXAMPLES.md - Six worked examples (cancer, metabolic, NSCLC, CVD, rare, neuro)QUICK_START.md - Sample prompts and output summary
COMPUTE, DON'T DESCRIBE When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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 nameAt 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):
Category Examples CANCER Breast, lung, colorectal, melanoma METABOLIC Type 2 diabetes, obesity, NAFLD CARDIOVASCULAR CAD, heart failure, AF NEUROLOGICAL Alzheimer, Parkinson, epilepsy RARE/MONOGENIC Marfan, CF, sickle cell, Huntington AUTOIMMUNE RA, lupus, MS, Crohn's
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, limitcBioPortal_get_mutations : gene_list is a STRING (space-separated), not arrayPubMed_search_articles : Returns a plain list of dicts, NOT {articles: [...]}fda_pharmacogenomic_biomarkers : Use limit=1000 for all resultsgnomAD : May return "Service overloaded" - skip gracefullyOpenTargets : Always nested {data: {entity: {field: ...}}} structure
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
Resolve disease to EFO ID using OpenTargets_get_disease_id_description_by_nameClassify disease type (CANCER/METABOLIC/CVD/NEUROLOGICAL/RARE/AUTOIMMUNE)Parse genomic data into structured format (gene, variant, type)Resolve gene IDs using MyGene_query_genes to get Ensembl/Entrez IDs
Phase 2: Genetic Risk Assessment
Germline variant pathogenicity : ClinVar_search_variants, EnsemblVEP_annotate_rsid/_hgvsGene-disease association : OpenTargets_target_disease_evidenceGWAS polygenic risk : gwas_get_associations_for_trait, OpenTargets_search_gwas_studies_by_diseasePopulation frequency : gnomad_get_variantGene 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
Molecular subtyping : cBioPortal_get_mutations, HPA_get_cancer_prognostics_by_geneTMB/MSI/HRD : fda_pharmacogenomic_biomarkers for FDA cutoffsPrognostic stratification : Combine stage + molecular features
METABOLIC PATH
Genetic risk integration : GWAS_search_associations_by_gene, OpenTargets_target_disease_evidenceComplication risk : Based on HbA1c, duration, existing complications
CVD PATH
FH gene check : ClinVar_search_variants for LDLR, APOB, PCSK9Statin PGx : PharmGKB_get_clinical_annotations for SLCO1B1
RARE DISEASE PATH
Causal variant identification : ClinVar_search_variantsGenotype-phenotype : UniProt_get_disease_variants_by_accession Scoring: See SCORING_REFERENCE.md for disease-specific tables.
Phase 4: Pharmacogenomic Profiling
Drug-metabolizing enzymes : PharmGKB_get_clinical_annotations, PharmGKB_get_dosing_guidelinesFDA PGx biomarkers : fda_pharmacogenomic_biomarkers (use limit=1000)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
Disease overlap : OpenTargets_get_associated_targets_by_disease_efoIdDDI check : drugbank_get_drug_interactions_by_drug_name_or_id, FDA_get_drug_interactions_by_drug_namePGx-amplified DDI : If PM genotype + CYP inhibitor, flag compounded risk
Phase 6: Molecular Pathway Analysis
Pathway enrichment : enrichr_gene_enrichment_analysis (libs: KEGG_2021_Human, Reactome_2022, GO_Biological_Process_2023)Reactome mapping : ReactomeAnalysis_pathway_enrichment, Reactome_map_uniprot_to_pathwaysNetwork analysis : STRING_get_interaction_partners, STRING_functional_enrichmentDruggable targets : OpenTargets_get_target_tractability_by_ensemblID
Phase 7: Clinical Evidence & Guidelines
Guidelines search : PubMed_Guidelines_Search (fallback: PubMed_search_articles)FDA-approved therapies : OpenTargets_get_associated_drugs_by_disease_efoId, FDA_get_indications_by_drug_nameBiomarker-drug evidence : civic_search_evidence_items, civic_search_assertions
Phase 8: Clinical Trial Matching
Biomarker-driven trials : search_clinical_trials with condition + interventionPrecision 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 + PRSClinical Risk (0-30): Stage/biomarkers/comorbiditiesMolecular Features (0-25): Driver mutations, subtypes, actionable targetsPharmacogenomic Risk (0-10): Metabolizer status, HLA alleles
Risk Tiers Score Tier Management 75-100 VERY HIGH Intensive treatment, subspecialty referral, clinical trial 50-74 HIGH Aggressive treatment, close monitoring 25-49 INTERMEDIATE Standard guideline-based care, PGx-guided dosing 0-24 LOW Surveillance, prevention, risk factor modification
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:
Cancer + actionable mutation : Breast cancer, BRCA1, ER+/HER2- -> Score ~55-65 (HIGH)Metabolic + PGx concern : T2D, CYP2C19 PM on clopidogrel -> Score ~55-65 (HIGH)NSCLC comprehensive : EGFR L858R, TMB 25, PD-L1 80% -> Score ~75-85 (VERY HIGH)CVD risk : LDL 190, SLCO1B1*5, family hx MI -> Score ~50-60 (HIGH)Rare disease : Marfan, FBN1 variant -> Score ~55-65 (HIGH)Neurological risk : APOE e4/e4, family hx Alzheimer's -> Score ~60-72 (HIGH) Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
