Biomarker classifiers for identifying susceptible subpopulations for treatment decisions

A main goal of pharmacogenomics is to develop genomic signatures to predict patients' responses to a drug or therapy for treatment decisions. Identification of patients who would have no beneficial effect or have the risk of developing adverse effects from an unnecessary treatment could save enormous cost in the healthcare system and clinical trials. This article presents an approach for developing a biomarker classifier for identifying a fraction of susceptible patients, who should be spared unnecessary treatment prior to treatment.

The identification of susceptible patients involves two steps. The first step is to identify 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 nonsusceptible patients.

Selection of the biomarkers of susceptibility is essential to achieve good prediction accuracy. The ensemble algorithm significantly improves the prediction accuracy compared with the standard classifiers.

The study shows that classifiers developed based on the biomarkers obtained by comparing the genomic profiles of responders to those of nonresponders may lead to a high misclassification error rate. Classifiers to identify a small fraction of the subpopulation should take imbalanced class sizes into consideration. A large sample size may be needed in order to ensure detection of a sufficient number of biomarkers and a sufficient number of susceptible subjects for classifier development and validation.