🔬 WIA-BIO-003

Overview

The WIA-BIO-003 standard establishes comprehensive protocols for biomarker discovery, validation, and clinical implementation across genomic, proteomic, metabolomic, and imaging modalities. This standard enables reproducible biomarker development and regulatory approval.

Key Features

Biomarker Classification

Discovery & Validation Pipeline

Phase 1: Discovery

1. Hypothesis Generation: Literature review, pathway analysis, preliminary data
2. Sample Collection: Retrospective cohorts (n=50-200), well-characterized phenotypes
3. Unbiased Screening: NGS, mass spectrometry, microarray, metabolomics
4. Statistical Analysis: Differential expression, ROC curves, multiple testing correction (FDR <0.05)
5. Candidate Selection: Effect size, biological plausibility, technical feasibility

Phase 2: Analytical Validation

Phase 3: Clinical Validation

• Study Design: Prospective cohort or case-control (n=200-500+)
• Clinical Sensitivity: TP / (TP + FN) ≥80% for diagnostics
• Clinical Specificity: TN / (TN + FP) ≥90% for diagnostics
• PPV (Positive Predictive Value): Context-dependent on prevalence
• NPV (Negative Predictive Value): Critical for rule-out tests
• AUC (Area Under Curve): >0.8 excellent, >0.7 acceptable
• Clinical Utility: Demonstrate impact on patient management

Clinical Use Cases

Regulatory Pathways

FDA Biomarker Qualification Program

1. Letter of Intent (LOI): Describe biomarker, context of use (COU), development plan
2. Qualification Plan: FDA feedback on study design, validation approach
3. Full Qualification Package: Submit analytical and clinical validation data
4. FDA Review: 6-12 month review, possible Advisory Committee meeting
5. Qualification Letter: Biomarker qualified for specific COU, can be used across sponsors

In Vitro Diagnostic (IVD) Approval

Implementation Example

import { BiomarkerData, Validator, ClinicalUtility } from '@wia/bio-biomarker';

// Define biomarker
const biomarker = new BiomarkerData({
  name: 'Oncotype DX',
  type: 'prognostic',
  context: 'early_stage_breast_cancer',
  analytes: [
    { gene: 'ESR1', weight: 0.8 },
    { gene: 'PGR', weight: 0.5 },
    { gene: 'BCL2', weight: 0.3 },
    { gene: 'SCUBE2', weight: -0.1 },
    { gene: 'MKI67', weight: 1.0 },
    // ... 16 more genes
  ],
  referenceGenes: ['ACTB', 'GAPDH', 'RPLP0', 'GUS', 'TFRC']
});

// Analytical validation
const analyticalValidation = await Validator.analytical({
  biomarker: biomarker,
  samples: {
    LOD: 20,  // replicates
    LOQ: 20,
    precision_within: 10,
    precision_between: 20,
    linearity: 7,  // concentration levels
    specificity: ['related_genes', 'degraded_RNA']
  }
});

console.log(`LOD: ${analyticalValidation.LOD.value} ng RNA`);
console.log(`Precision (within-run): CV = ${analyticalValidation.precision.within}%`);
console.log(`Linearity: R² = ${analyticalValidation.linearity.r2}`);

// Clinical validation
const clinicalValidation = await Validator.clinical({
  biomarker: biomarker,
  cohort: {
    size: 500,
    design: 'retrospective',
    endpoint: 'distant_recurrence_10yr'
  },
  stratification: {
    low: { score: [0, 17], risk: '<10%' },
    intermediate: { score: [18, 30], risk: '10-20%' },
    high: { score: [31, 100], risk: '>20%' }
  }
});

console.log(`Sensitivity (high-risk detection): ${clinicalValidation.sensitivity}%`);
console.log(`Specificity (low-risk identification): ${clinicalValidation.specificity}%`);
console.log(`AUC: ${clinicalValidation.auc}`);

// Clinical utility assessment
const utility = new ClinicalUtility(biomarker);
const impact = await utility.assessDecisionImpact({
  patients: 1000,
  baseline_chemotherapy_rate: 0.85,
  post_biomarker_chemotherapy_rate: 0.30
});

console.log(`Patients avoiding chemotherapy: ${impact.avoided_treatment}`);
console.log(`Estimated cost savings: $${impact.cost_savings}`);
console.log(`Quality-adjusted life years (QALYs) gained: ${impact.qaly_gain}`);