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.

本研究提出了基于网络偏置树集成 (NetBiTE) 方法，利用先前的知识和基因表达数据，预测和鉴定癌症中的药物敏感性及其生物标志物。通过将高权重分配到药物靶点并在蛋白质相互作用网络 (如STRING) 中传播所分配的权重，定义了靶点周围的影响邻域，模拟药物给药后在细胞内的扰动传播。研究结果表明，NetBiTE 方法优于 RF 算法，仅适用于靶向膜受体途径 (MRPs) 的药物。本研究提出了对于抑制 MRPs 的药物，药物给药前的靶基因表达是预测 IC50 药物敏感性的生物标志物。经过对 PI3K / MTOR 签名通路抑制剂的对照研究和对 MRP 抑制药物进行虚拟靶点的测试，验证和加强了该命题。

用于癌症药物敏感性预测和药物敏感性生物标志物鉴定的基于网络的偏置树集成技术（NetBiTE）