SynBioC Research Group, Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Laboratory of Experimental Cancer Research, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Int J Cancer. 2019 Aug 1;145(3):735-747. doi: 10.1002/ijc.32169. Epub 2019 Feb 20.
Our study investigates the biochemical and functional impact of selective histone deacetylase 6 (HDAC6) inhibitors, a promising class of novel therapeutics, in several cancer models. Selective HDAC6 inhibitors (Tubathian A, Tubastatin A, Tubacin and Ricolinostat) and a non-selective HDAC inhibitor (Vorinostat) were evaluated on cancer cell lines derived from multiple tumour types in both an in vitro and in vivo setting as potential cancer therapeutics. Selective HDAC6 inhibitors resulted in α-tubulin acetylation with no impact on histone acetylation but failed to show any anti-cancer properties. Only the use of high concentrations of selective HDAC6 inhibitors resulted in co-inhibition of other HDAC enzymes and consequently in reduced growth, migratory and/or invasive activity of cancer cells in vitro as well as in vivo. The specificity of HDAC6 inhibition was confirmed using a CRISPR/Cas9 knockout cell line. Our results suggest that selective HDAC6 inhibitors may fall short as potential single agent anti-cancer drugs and prove that many previous data regarding this promising class of compounds need to be interpreted with great care due to their use in high concentrations resulting in low selectivity and potential off-target effects.
我们的研究调查了选择性组蛋白去乙酰化酶 6(HDAC6)抑制剂在几种癌症模型中的生化和功能影响,这些抑制剂是一类很有前途的新型治疗药物。我们评估了几种选择性 HDAC6 抑制剂(Tubathian A、Tubastatin A、Tubacin 和 Ricolinostat)和一种非选择性 HDAC 抑制剂(Vorinostat),作为潜在的癌症治疗药物,在源自多种肿瘤类型的癌细胞系中进行了体外和体内研究。选择性 HDAC6 抑制剂导致α-微管蛋白乙酰化,而对组蛋白乙酰化没有影响,但未能显示出任何抗癌特性。只有使用高浓度的选择性 HDAC6 抑制剂才能抑制其他 HDAC 酶,从而导致体外和体内癌细胞的生长、迁移和/或侵袭活性降低。使用 CRISPR/Cas9 敲除细胞系证实了 HDAC6 抑制的特异性。我们的研究结果表明,选择性 HDAC6 抑制剂可能不能作为单一的抗癌药物,并且证明许多之前关于这一有前途的化合物类别的数据需要谨慎解释,因为它们在高浓度下使用会导致低选择性和潜在的脱靶效应。