Robust predictors for drug response of patients with acute myeloid leukemia

Abstract

The significant heterogeneity in treatment responses among patients with acute myeloid leukemia (AML) underscores the critical need for accurate drug response prediction. We developed k-Top Scoring Pairs (kTSP) classifiers, ensemble methods that aggregate the relative expression of gene pairs. We compared their accuracy with that of state-of-the-art machine learning methods, linear and radial basis function support vector machines, random forest and elastic net regression classifiers for drug response prediction of patients with AML. Our results demonstrate that kTSP particularly outperforms other methods when the number of sensitive and resistant patients is imbalanced, a common challenge in clinical studies. Our approach is inherently robust to batch effects and uniquely suited for single-patient classification due to its rank-based methodology.