tooluniverse-precision-medicine-stratification

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:

1. 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).
2. 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.
3. 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:

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