Genomic Biomarkers for a Binary Clinical Outcome in Early Drug Development Microarray Experiments

Abstract

In this article, we discuss methods to select three different types of genes (treatment related, response related, or both) and investigate whether they can serve as biomarkers for a binary outcome variable. We consider an extension of the joint model introduced by Lin et al. (2010) and Tilahun et al. (2010) for a continuous response. As the model has certain drawbacks in a binary setting, we also present a way to use classical selection methods to identify subgroups of genes, which are treatment and/or response related. We evaluate their potential to serve as biomarkers by applying DLDA to predict the response level.

Key Words:

• Biomarkers
• BW ratio
• Categorical data
• Joint modeling
• Microarrays

ACKNOWLEDGMENT

We gratefully acknowledge support of the IAP research network P6/03 of the Belgian Government (Belgian Science Policy).

Notes

Note. The upper part of the table contains the results for therapeutic and/or prognostic biomarkers obtained by the joint modeling approach, while the lower part presents the results for BW _Resp , BW _Treat , and BW _Treat/Resp . CV: cross-validation, LOOCV: leave-one-out cross-validation, BCV: bootstrap cross-validation.

Note. The upper part of the table contains the results for therapeutic (comparison between T1 and T3) and/or prognostic biomarkers obtained by the joint modeling approach, while the lower part presents the results for BW _Resp , BW _treat , and BW _Treat/Resp . CV: cross-validation, LOOCV: leave-one-out cross-validation, BCV: bootstrap cross-validation.