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蛋白质组学分析确定了乳腺癌亚型特有的激酶分类。

Proteomic analysis defines kinase taxonomies specific for subtypes of breast cancer.

作者信息

Collins Kyla A L, Stuhlmiller Timothy J, Zawistowski Jon S, East Michael P, Pham Trang T, Hall Claire R, Goulet Daniel R, Bevill Samantha M, Angus Steven P, Velarde Sara H, Sciaky Noah, Oprea Tudor I, Graves Lee M, Johnson Gary L, Gomez Shawn M

机构信息

Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

出版信息

Oncotarget. 2018 Jan 29;9(21):15480-15497. doi: 10.18632/oncotarget.24337. eCollection 2018 Mar 20.

Abstract

Multiplexed small molecule inhibitors covalently bound to Sepharose beads (MIBs) were used to capture functional kinases in luminal, HER2-enriched and triple negative (basal-like and claudin-low) breast cancer cell lines and tumors. Kinase MIB-binding profiles at baseline without perturbation proteomically distinguished the four breast cancer subtypes. Understudied kinases, whose disease associations and pharmacology are generally unexplored, were highly represented in MIB-binding taxonomies and are integrated into signaling subnetworks with kinases that have been previously well characterized in breast cancer. Computationally it was possible to define subtypes using profiles of less than 50 of the more than 300 kinases bound to MIBs that included understudied as well as metabolic and lipid kinases. Furthermore, analysis of MIB-binding profiles established potential functional annotations for these understudied kinases. Thus, comprehensive MIBs-based capture of kinases provides a unique proteomics-based method for integration of poorly characterized kinases of the understudied kinome into functional subnetworks in breast cancer cells and tumors that is not possible using genomic strategies. The MIB-binding profiles readily defined subtype-selective differential adaptive kinome reprogramming in response to targeted kinase inhibition, demonstrating how MIB profiles can be used in determining dynamic kinome changes that result in subtype selective phenotypic state changes.

摘要

与琼脂糖珠共价结合的多重小分子抑制剂(MIBs)被用于捕获腔面型、HER2富集型和三阴性(基底样和Claudin低表达型)乳腺癌细胞系及肿瘤中的功能性激酶。在未受干扰的基线状态下,激酶MIB结合谱通过蛋白质组学方法区分了四种乳腺癌亚型。研究较少的激酶,其与疾病的关联及药理学特性通常未被探索,在MIB结合分类中高度富集,并与先前在乳腺癌中已得到充分表征的激酶整合到信号子网中。通过计算,可以使用与MIB结合的300多种激酶中不到50种激酶的谱来定义亚型,这些激酶包括研究较少的以及代谢和脂质激酶。此外,对MIB结合谱的分析为这些研究较少的激酶建立了潜在的功能注释。因此,基于MIB的激酶全面捕获提供了一种独特的基于蛋白质组学的方法,可将研究较少的激酶组中特征不明的激酶整合到乳腺癌细胞和肿瘤的功能子网中,而这是基因组策略无法做到的。MIB结合谱很容易定义了响应靶向激酶抑制的亚型选择性差异适应性激酶组重编程,展示了MIB谱如何用于确定导致亚型选择性表型状态变化的动态激酶组变化。

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