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Updated: Jun 25, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Development and Validation of Biomarker Classifiers for Treatment Selection
1Richard Simon, D.Sc., Biometric Research Branch, National Cancer Institute, 9000 Rockville Pike, Bethesda MD 20892-7434, U.S.A. 301.496-0975 (tel), 301.402-0560 (fax), rsimon@mail.nih.gov.
Abstract:
Many syndromes traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This often leads to the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. Drugs that demonstrate effectiveness in such trials may subsequently be used broadly, resulting in ineffective treatment of many patients. New genomic and proteomic technologies provide powerful tools for the selection of patients likely to benefit from a therapeutic without unacceptable adverse events. In spite of the large literature on developing predictive biomarkers, there is considerable confusion about the development and validation of biomarker based diagnostic classifiers for treatment selection. In this paper we attempt to clarify some of these issues and to provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of pharmacogenomic classifiers.
Insights
Heterogeneous diseases require precise patient selection for effective treatment. This study guides the development and validation of pharmacogenomic classifiers for clinical trials, improving therapeutic outcomes.
Area of Science:
- Biomarker discovery
- Genomic medicine
- Clinical trial design
Background:
- Many diseases exhibit molecular heterogeneity, complicating treatment.
- Large clinical trials often yield small average benefits for diverse patient groups.
- Broad drug use can lead to ineffective treatments for many.
Purpose of the Study:
- To clarify issues in developing and validating biomarker-based diagnostic classifiers for treatment selection.
- To provide guidance on designing clinical trials for pharmacogenomic classifiers.
- To enhance patient stratification for targeted therapies.
Main Methods:
- Review of existing literature on predictive biomarker development.
- Analysis of challenges in biomarker-based diagnostic classifier validation.
- Framework development for clinical utility and robustness evaluation.
Main Results:
- Identification of confusion in current biomarker development and validation practices.
- Emphasis on the need for robust validation of pharmacogenomic classifiers.
- Proposal of guidelines for clinical trial design in pharmacogenomics.
Conclusions:
- Pharmacogenomic classifiers are crucial for personalized medicine.
- Standardized approaches are needed for classifier validation.
- Well-designed clinical trials are essential to demonstrate the utility of pharmacogenomic classifiers.
