Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.
Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, United States.
Elife. 2020 Sep 18;9:e52707. doi: 10.7554/eLife.52707.
Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.
我们发现有效药物组合的能力有限，部分原因是我们对两种单药治疗的转录反应如何导致其组合的转录反应了解不足。我们分析了用单药和联合用药处理的细胞的匹配时间过程 RNAseq 分析，发现协同组合的转录特征相对于单一药物治疗是独特的。在基因调控网络中，转录因子的时间顺序激活被牵连其中。这种转录级联的性质表明，当两种不相关的个体药物引起的转录反应相关时，可能会出现药物协同作用。我们将这些结果用作一个简单预测算法的基础，该算法在独立数据集的协同药物组合预测中达到了 0.77 的 AUROC。
