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高通量转录谱分析揭示了抗癌药物的常见和细胞类型特异性反应。

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

机构信息

HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.

Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.

出版信息

Nat Commun. 2017 Oct 30;8(1):1186. doi: 10.1038/s41467-017-01383-w.

Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

摘要

更有效地利用靶向抗癌药物取决于阐明药物治疗引起的分子状态与这些状态所控制的细胞表型(如细胞静止和死亡)之间的联系。当单个基因突变不足以作为药物反应的预测因子时,这一点尤其正确。本文描述了一个约 600 对药物细胞系对的数据集,这些数据集是作为 NIH LINCS 计划(http://www.lincsproject.org/)的一部分收集的,其中分子数据(降低维度的转录 L1000 谱)在剂量和时间上与细胞生长抑制和细胞毒性的表型数据平行记录。我们报告说,转录和表型反应通常相互关联,但伴侣蛋白和细胞周期激酶抑制剂抑制细胞系中的转录变化相似,而抑制细胞内信号激酶的药物诱导的变化是细胞类型特异性的。在一些药物/细胞系对中,观察到转录显著变化而细胞生长或存活没有变化;对这种对的分析确定了药物等效类,在一种情况下,还确定了协同药物相互作用。在这种情况下,协同作用涉及细胞类型特异性抑制适应性药物反应。

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