Biomarker Classifiers for Identifying Susceptible Subpopulations for Treatment Decisions

• Activity Number: 516
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Wei-Jiun Lin*+ and James J. Chen
• Companies: Feng Chia University and FDA
• Keywords: biomarkers of susceptibility ; class prediction ; imbalanced class sizes ; personalized medicine ; susceptible subpopulations

Abstract

A main goal of pharmacogenomics is to develop genomic signatures to predict patients' responses to drug or therapy for treatment decisions. This article presents an approach to developing a biomarker classifier for identifying a fraction of susceptible patients who should be spared unnecessary treatment. The approach involves two steps. The first step is to identify a set of biomarkers of susceptibility from a mixture of biomarkers of susceptibility and biomarkers of response; the second step is to develop a classifier using an ensemble classification algorithm, as the number of susceptible patients is generally much smaller than the number of non-susceptible patients. The result shows that selection of the biomarkers of susceptibility is essential to achieve good prediction accuracy. The ensemble algorithm improves the prediction accuracy significantly compared with the standard classifiers. Classifiers developed based on the biomarkers obtained by comparing the genomic profiles of responders to those of non-responders may lead to a high misclassification error rate.