table1_Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics.xlsx

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

前列腺癌（Prostate cancer, PRAD）是癌症相关死亡的主要诱因之一。当前单一疗法往往因快速出现的耐药性而疗效有限。联合疗法或可作为替代解决方案，通过增强治疗效果、降低细胞毒性、延缓耐药性的出现来解决该问题。然而，从数百万种潜在组合中筛选出具有协同作用的药物组合，实验方法成本高企且耗时耗力。因此，亟需探索其他高效策略以辅助实验研究。受此挑战启发，我们构建了基于转录组学（transcriptomics）和基于网络的（network-based）预测模型，以快速筛选前列腺癌的潜在药物组合，并通过体外实验（in vitro assays）进一步评估其预测性能。基于转录组学的方法筛选出9种潜在组合；而基于网络的方法至少对3组药物对给出了不一致的预测结果。进一步的实验结果显示，3种含多西他赛（docetaxel）的组合呈现剂量依赖性效应，并证实了基于转录组学模型预测的其余6种组合具有协同作用。对于基于网络的预测结果，体外实验对其中两组（即米托蒽醌（mitoxantrone）-赛庚啶（cyproheptadine）和卡巴他赛（cabazitaxel）-赛庚啶）得到了相反的结论。换言之，针对前列腺癌这类特定疾病，基于转录组学的方法优于基于网络的方法，这为药物组合筛选中的计算方法选择提供了指导依据。更重要的是，6种组合（3种含米托蒽醌和3种含卡巴他赛的组合）被发现是可协同攻克前列腺癌的极具潜力的候选方案。