Riddick G, Song H, Holbeck S L, Kopp W, Walling J, Ahn S, Zhang W, Fine H A
Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Developmental Therapeutics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Pharmacogenomics J. 2015 Aug;15(4):347-53. doi: 10.1038/tpj.2014.61. Epub 2014 Dec 2.
Cancer stem cells (CSCs) are thought to promote resistance to chemotherapeutic drugs in glioblastoma, the most common and aggressive primary brain tumor. However, the use of high-throughput drug screens to discover novel small-molecule inhibitors for CSC has been hampered by their instability in long-term cell culture. We asked whether predictive models of drug response could be developed from gene expression signatures of established cell lines and applied to predict drug response in glioblastoma stem cells. Predictions for active compounds were confirmed both for 185 compounds in seven established glioma cell lines and 21 compounds in three glioblastoma stem cells. The use of established cell lines as a surrogate for drug response in CSC lines may enable the large-scale virtual screening of drug candidates that would otherwise be difficult or impossible to test directly in CSCs.
癌症干细胞（CSCs）被认为会促进胶质母细胞瘤（最常见且侵袭性最强的原发性脑肿瘤）对化疗药物产生耐药性。然而，利用高通量药物筛选来发现针对癌症干细胞的新型小分子抑制剂，却因它们在长期细胞培养中的不稳定性而受到阻碍。我们探讨了能否从已建立细胞系的基因表达特征开发出药物反应预测模型，并将其应用于预测胶质母细胞瘤干细胞中的药物反应。对于七种已建立的胶质瘤细胞系中的185种化合物以及三种胶质母细胞瘤干细胞中的21种化合物，活性化合物的预测均得到了证实。将已建立的细胞系用作癌症干细胞系中药物反应的替代物，可能会实现对药物候选物的大规模虚拟筛选，否则这些候选物直接在癌症干细胞中进行测试将很困难或不可能。
