Synthetic lethal screening with small molecule inhibitors provides a pathway to rational combination therapies for melanoma

Recent data demonstrate that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways. This network model suggests why inhibition of a single component of a canonical pathway, even when targeting a mutationally activated driver of cancer, has insufficiently dramatic effects on the treatment of cancer. The biological outcome of signals propagated through a network is inherently more robust and resistant to inhibition of a single network component due to compensatory and redundant signaling events. In this study, we performed a functional chemical genetic screen analogous to synthetic lethal screening in yeast genetics to identify novel interactions between signaling inhibitors that would not be predicted based on our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. Among the most robust and surprising results was synergy between sorafenib, a multi-kinase inhibitor with activity against Raf, and diclofenac, a non-steroidal anti-inflammatory drug (NSAID). This synergy did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. The NSAIDs celecoxib and ibuprofen could qualitatively substitute for diclofenac. Similarly, the MEK inhibitor PD325901 and the Raf inhibitor RAF265 could qualitatively substitute for sorafenib. These drug substitution experiments suggest that inhibition of cyclo-oxygenase and MAP kinase signaling are components of the observed synergistic cytotoxicity. Genome-wide expression profiling demonstrates synergy-specific down-regulation of survival-related genes. This study provides proof of principle that synthetic lethal screening can uncover novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype. RNA from VMM39, DM331, and SLM2 cells with/without mutations in Ras and/or Braf, treated with Sorafenib and/or Diclofenac.

最新研究数据表明，细胞外信号是通过蛋白质网络而非层级化信号通路进行传递的。该网络模型解释了为何即便针对突变激活的癌症驱动因子，抑制经典通路中的单一成分，也难以在癌症治疗中产生显著效果。由于存在代偿性与冗余性信号事件，通过网络传播的信号所产生的生物学效应本身具有更强的稳健性，且更能抵御单一网络成分的抑制作用。本研究开展了一项功能化学遗传学筛选，其原理类似于酵母遗传学中的合成致死（synthetic lethal）筛选，旨在识别基于当前信号网络认知无法预测的新型信号抑制剂间相互作用。我们在9种黑色素瘤细胞系中筛选了逾300种药物组合，已鉴定出多组具有协同细胞毒性的化合物对。在最具稳健性且最出乎意料的研究结果中，多激酶抑制剂索拉非尼（sorafenib，可靶向抑制Raf）与非甾体抗炎药（non-steroidal anti-inflammatory drug, NSAID）双氯芬酸（diclofenac）之间存在协同效应。该协同效应与黑色素瘤细胞系已知的RAS及BRAF突变状态并无关联。非甾体抗炎药塞来昔布（celecoxib）与布洛芬（ibuprofen）可在功能上替代双氯芬酸。同理，MEK抑制剂PD325901与RAF抑制剂RAF265也可在功能上替代索拉非尼。上述药物替代实验表明，环氧合酶与丝裂原活化蛋白激酶（mitogen-activated protein kinase, MAPK）信号通路的抑制作用是本次观测到的协同细胞毒性的组成部分。全基因组表达谱分析显示，存在协同效应特异性的存活相关基因下调现象。本研究验证了合成致死筛选可用于发现新型功能性药物组合的原理，并表明调控靶向治疗应答的潜在信号网络，会因细胞基因型中未被探索的成分而存在显著差异。本研究的样本为经索拉非尼和/或双氯芬酸处理的、携带或不携带Ras及/或Braf突变的VMM39、DM331与SLM2细胞的RNA。