  We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug
sensitivity prediction and drug sensitivity biomarker identification in cancer
using a combination of prior knowledge and gene expression data. Our devised
method consists of a biased tree ensemble that is built according to a
probabilistic bias weight distribution. The bias weight distribution is
obtained from the assignment of high weights to the drug targets and
propagating the assigned weights over a protein-protein interaction network
such as STRING. The propagation of weights, defines neighborhoods of influence
around the drug targets and as such simulates the spread of perturbations
within the cell, following drug administration. Using a synthetic dataset, we
showcase how application of biased tree ensembles (BiTE) results in significant
accuracy gains at a much lower computational cost compared to the unbiased
random forests (RF) algorithm. We then apply NetBiTE to the Genomics of Drug
Sensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperforms
RF in predicting IC50 drug sensitivity, only for drugs that target membrane
receptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We propose
based on the NetBiTE results, that for drugs that inhibit MRPs, the expression
of target genes prior to drug administration is a biomarker for IC50 drug
sensitivity following drug administration. We further verify and reinforce this
proposition through control studies on, PI3K/MTOR signaling pathway inhibitors,
a drug category that does not target MRPs, and through assignment of dummy
targets to MRP inhibiting drugs and investigating the variation in NetBiTE
accuracy.
