目次

Section 1: Introduction1. Scope2. Biomarker is a biomarker is a biomarker3. Omics4. Precision medicineSection 2: Biomarker Development Path5. Overview - clinical development path (analogy to Therapeutics) - Fit for purpose6. Analytical Validation7. Clinical Validation8. Clinical UtilitySection 3: Statistical Considerations9. Introduction10. Discovery Informatics / Machine Learning11. Statistical Issues for Biomarker Studies (confounding, statistical tests, multiplicity/overfitting, FDR)12. Statistical versus clinical significance13. Biomarker study design (RCT, Biomarker by Treatment Interaction Design, Adaptive Designs)Section 4: Molecular ProfilingThe Sample: Diagnostic Matrices14. FFPE15. Liquid Biopsies16. Extracellular vesiclesThe method: Technologies17. PCR versus Sequencing: The Heisenberg UncertaintyThe test18. From Central Reference Labs over IVD kits to Sample to Answers and Mobile PlatformsSection 5: Clinical BiomarkersGenetic19. Lessons learned 120. Lessons learned 221. Lessons learned 322. Whole genome-exome diagnosticsEpigenetic23. Lessons learned 124. Lessons learned 225. Whole epigenome / Methylation atlasExpression (RNA, miRNA, lncRNA)26. Lessons learned 127. Lessons learned 228. TranscriptomicsTranslation (Protein)29. Lessons learned 130. Lessons learned 231. Proteomics32. PeptidomicsSection 6: Clinico-Genomic33. Best of both worldsSection 7: The Economics of Molecular Diagnostics34. HTA/Economic evaluation35. Reimbursement36. IP - Intellectual Property37. Commercialization - Business Models - Translating biomarkers successfully to the clinicSection 8: Biomarkers and ELSI (Ethical, Legal and Social Implication)38. Legal - Regulations39. Ethical and Social ImplicationsSection 9: Biomarkers and Patient Empowerment40. Risk Management, A Review of Shared Decision-Making and/or Evidence-based advocacySection 10: Best Practices41. Keys to success/lessons learned