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We present the Network-based Biased Tree Ensembles (NetBiTE) method for drugsensitivity prediction and drug sensitivity biomarker identification in cancerusing a combination of prior knowledge and gene expression data. Our devisedmethod consists of a biased tree ensemble that is built according to aprobabilistic bias weight distribution. The bias weight distribution isobtained from the assignment of high weights to the drug targets andpropagating the assigned weights over a protein-protein interaction networksuch as STRING. The propagation of weights, defines neighborhoods of influencearound the drug targets and as such simulates the spread of perturbationswithin the cell, following drug administration. Using a synthetic dataset, weshowcase how application of biased tree ensembles (BiTE) results in significantaccuracy gains at a much lower computational cost compared to the unbiasedrandom forests (RF) algorithm. We then apply NetBiTE to the Genomics of DrugSensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperformsRF in predicting IC50 drug sensitivity, only for drugs that target membranereceptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We proposebased on the NetBiTE results, that for drugs that inhibit MRPs, the expressionof target genes prior to drug administration is a biomarker for IC50 drugsensitivity following drug administration. We further verify and reinforce thisproposition through control studies on, PI3K/MTOR signaling pathway inhibitors,a drug category that does not target MRPs, and through assignment of dummytargets to MRP inhibiting drugs and investigating the variation in NetBiTEaccuracy.

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer