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Predictive biomarker validation in practice: lessons from real trials.

Sumithra J Mandrekar1, Daniel J Sargent

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA. mandrekar.sumithra@mayo.edu

Clinical Trials (London, England)
|April 16, 2010
PubMed
Summary
This summary is machine-generated.

Biomarker validation is crucial for personalized medicine. Different clinical trial designs, including retrospective and prospective approaches, offer unique advantages and limitations for validating predictive biomarkers.

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Published on: June 26, 2013

Area of Science:

  • Clinical trial design
  • Biomarker validation
  • Personalized medicine

Background:

  • Biomarkers are essential for individualizing therapy in the era of targeted treatments.
  • Validation of predictive biomarkers is critical due to evolving treatment landscapes and mechanisms of action.
  • Biomarker-driven prediction integrates disease genetics and patient genotype for tailored therapies.

Purpose of the Study:

  • To review the strengths and weaknesses of various clinical trial designs for predictive biomarker validation.
  • To illustrate these designs using examples from completed and ongoing clinical trials.

Main Methods:

  • Classification of trial designs into retrospective (using existing randomized controlled trials - RCTs) and prospective (enrichment, targeted, all-comers, hybrid, adaptive analysis).
  • Analysis of specific trial designs in the context of real-world clinical studies.

Main Results:

  • Retrospective analysis of RCTs can expedite treatment delivery to biomarker-defined subgroups (e.g., KRAS in colorectal cancer).
  • Prospective enrichment designs are suitable when preliminary evidence suggests variable patient benefit, but may leave questions unanswered (e.g., HER2 in breast cancer).
  • All-comers designs are optimal for uncertain treatment benefits or assay reproducibility (e.g., EGFR in lung cancer) or for comparing multiple regimens (e.g., breast cancer chemotherapy).

Conclusions:

  • Successful biomarker validation relies on established technical feasibility, assay performance, and specimen logistics, with promising initial predictive results.
  • The selection of an appropriate clinical trial design involves scientific, clinical, statistical, and ethical considerations.
  • There is no universal approach to predictive biomarker validation; design choice is context-dependent.