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. 2009 Aug 20;27(24):4027-34.
doi: 10.1200/JCO.2009.22.3701. Epub 2009 Jul 13.

Clinical Trial Designs for Predictive Biomarker Validation: Theoretical Considerations and Practical Challenges

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Free PMC article

Clinical Trial Designs for Predictive Biomarker Validation: Theoretical Considerations and Practical Challenges

Sumithra J Mandrekar et al. J Clin Oncol. .
Free PMC article

Abstract

Purpose: Biomarkers can add substantial value to current medical practice by providing an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient to guide patient-specific treatment selection. We discuss and evaluate various clinical trial designs for the validation of biomarker-guided therapy.

Methods: Designs for predictive marker validation are broadly classified as retrospective (ie, using data from previously well-conducted randomized controlled trials [RCTs]) versus prospective (enrichment, unselected, hybrid, or adaptive analysis). We discuss the salient features of each design in the context of real trials.

Results: Well-designed retrospective analysis from well-conducted prospective RCTs can bring forward effective treatments to marker-defined subgroups of patients in a timely manner (eg, KRAS and colorectal cancer). Enrichment designs are appropriate when preliminary evidence suggest that patients with or without that marker profile do not benefit from the treatments in question; however, this may sometimes leave questions unanswered (eg, trastuzumab and breast cancer). An unselected design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain (eg, epidermal growth factor receptor and lung cancer). Hybrid designs are appropriate when preliminary evidence demonstrate the efficacy of certain treatments for a marker-defined subgroup, making it unethical to randomly assign patients with that marker status to other treatments (eg, multigene assay and breast cancer). Adaptive analysis designs allow for prespecified marker-defined subgroup analyses of data from an RCT.

Conclusion: The implementation of these design strategies will lead to a more rapid clinical validation of biomarker-guided therapy.

Conflict of interest statement

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Figures

Fig 1.
Fig 1.
MARVEL (Marker Validation for Erlotinib in Lung Cancer) trial design. Each cycle of treatment is 21 days. Stratification factors: ECOG performance status, gender, smoking status, histology, best response to prior chemotherapy. EGFR, epidermal growth factor receptor; FISH, fluorescent in situ hybridization.
Fig 2.
Fig 2.
ECOG (Eastern Cooperative Oncology Group) 5202 trial design. Cycle of treatment, 2 days every 2 weeks, repeat for 12 cycles.
Fig 3.
Fig 3.
TAILORx (Trial Assigning Individualized Options for Treatment) trial design. RS, risk score.
Fig 4.
Fig 4.
MINDACT (Microarray in Node-Negative Disease May Avoid Chemotherapy) trial design.

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