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激酶组状态可预测胰腺癌肿瘤和癌相关成纤维细胞系的细胞活力。

Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines.

机构信息

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, United States of America.

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.

出版信息

PeerJ. 2024 Aug 28;12:e17797. doi: 10.7717/peerj.17797. eCollection 2024.

Abstract

Numerous aspects of cellular signaling are regulated by the kinome-the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC.

摘要

细胞信号转导的许多方面都受到激酶组的调控——激酶组是由超过 500 种蛋白激酶组成的网络,这些激酶指导和调节细胞内的信息传递。单个激酶和组成功能性子网络的激酶集合所起的关键作用导致激酶组失调,从而驱动许多疾病的发生,尤其是癌症。在胰腺导管腺癌 (PDAC) 的情况下,已经确定了多种激酶和相关信号通路,它们在疾病的发生和进展中起着关键作用。然而,新的相关治疗靶点的识别进展缓慢,并且肿瘤与周围肿瘤微环境之间的相互作用进一步使其复杂化。在这项工作中,我们试图将人类激酶组(激酶组型)的状态与经处理的、源自患者的 PDAC 肿瘤和癌相关成纤维细胞系中的细胞活力联系起来。我们将分类模型应用于独立的激酶组扰动和激酶抑制剂细胞筛选数据,发现细胞的推断激酶组型与细胞活力具有显著的预测关系。我们进一步发现,这些模型能够识别一组激酶,它们在受到扰动时的行为能够驱动这些细胞系中大多数的活力反应,其中包括研究较少的激酶 CSNK2A1/3、CAMKK2 和 PIP4K2C。我们接下来利用这些模型来预测新的、在初始数据集开发中不存在的临床激酶抑制剂的反应,并进行了验证筛选,证实了模型的准确性。这些结果表明,对人类蛋白激酶组的扰动状态进行特征描述为更好地理解信号转导行为和下游细胞表型提供了重要机会,并为 PDAC 的潜在治疗策略的更广泛设计提供了深入的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5c3/11365483/6096264e7b61/peerj-12-17797-g001.jpg

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