细胞信号传导的许多方面都受到激酶组的调节，激酶组是由 500 多种蛋白激酶组成的网络，可引导和调节整个细胞的信息传递。单个激酶和组织成功能子网的激酶组合所发挥的关键作用导致激酶组失调，从而导致许多疾病，特别是癌症。就胰腺导管腺癌 (PDAC) 而言，已确定多种激酶和相关信号通路在疾病发生及其进展中发挥关键作用。然而，其他相关治疗靶点的识别进展缓慢，并且因肿瘤与周围肿瘤微环境之间的相互作用而进一步混乱。在这项工作中，我们试图将人类激酶组或基因型的状态与经过治疗的患者来源的 PDAC 肿瘤和癌症相关成纤维细胞系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型与细胞活力具有显着的预测关系。我们进一步发现，模型能够识别一组激酶，这些激酶对扰动的反应驱动了这些细胞系中的大多数活力反应，包括尚未研究的激酶 CSNK2A1/3、CAMKK2 和 PIP4K2C。接下来，我们利用这些模型来预测模型开发的初始数据集中不存在的新的临床激酶抑制剂的反应，并进行了验证筛选以确认模型的准确性。这些结果表明，表征人类蛋白激酶组的扰动状态为更好地理解信号传导行为和下游细胞表型提供了重要的机会，并为 PDAC 潜在治疗策略的更广泛设计提供了见解。©2024 Berginski 等人。

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.©2024 Berginski et al.