The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

中文翻译：

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高内涵分析确定了非小细胞肺癌细胞系中 mTOR 抑制剂 INK128 和 HDAC 抑制剂之间的协同药物相互作用

耐药性的产生是癌症治疗失败的主要原因。为了抑制耐药性，通常将多种药物一起作为组合疗法进行治疗。特别是，协同药物组合可以在较低浓度下杀死癌细胞，保证癌症患者有更好的预后和更少的副作用。许多研究通过小规模基于功能的靶向生长测定或大规模非靶向生长测定来寻找协同组合，但由于技术问题（例如大量可能的测试组合），他们的发现始终具有挑战性。为了解决这个问题，我们在非小细胞肺癌细胞系中进行了中等规模的光学药物协同筛选，并通过高内涵图像分析进一步研究了组合药物反应中的个体药物相互作用。细胞反应的光学高内涵分析最近引起了药物发现、功能基因组学和毒理学领域的极大兴趣。在这里，我们采用了类似的方法来研究组合药物反应。通过检查 6 个不同药物类别中 12 种药物化合物的所有可能组合，例如 mTOR 抑制剂、HDAC 抑制剂、HSP90 抑制剂、MT 抑制剂、DNA 抑制剂和蛋白酶体抑制剂，我们成功确定了 mTOR 抑制剂 INK128 和 HDAC 抑制剂之间的协同作用，其他地方也有报道。我们的高内涵分析进一步表明，当 HDAC 抑制剂、HSP90 抑制剂和蛋白酶体抑制剂与 MT 抑制剂、DNA 抑制剂或 mTOR 抑制剂混合时，它们在组合药物反应中发挥主导作用，这表明隐性药物可能不那么优先作为成分多种药物鸡尾酒。总之，我们的光学药物筛选平台有效地识别了非小细胞肺癌细胞系中的协同药物组合，并且我们的高内涵分析进一步揭示了药物组合中的各个药物如何相互作用以产生组合药物反应。