A new approach to understand kinase pathway wiring, rewiring, and drug resistance in cancer and other diseases

磷酸化是一种重要的细胞调控机制。磷酸化的改变是疾病的主要因素，许多临床上重要的药物以激酶为靶点治疗癌症和其他疾病。对这些疗法的获得性耐药可能是通过重新连接激酶通路而产生的。因此，确定信号网络的特征对于描述疾病的变化、提高对药物疗效和患者对靶向治疗的反应的预测、了解耐药性以及识别生物标记物和诊断至关重要。为此，Kinomica提供基于质谱学和计算的服务，询问数千个磷酸化位点并阐明大型磷酸蛋白质组网络。尽管已知数千个磷酸化位点，但识别特定磷酸化事件的激酶是一个主要障碍。这阻碍了对疾病信号变化的理解，对于Kinomica来说，这意味着他们的磷蛋白质组数据集的深度不能被充分利用。为了解决这些问题，我们最近开发了一种普遍适用的方法(“KiPIK”)来识别特定磷酸化位点的激酶，该方法使用已在近动态组范围的蛋白激酶面板上描述的激酶抑制物库。每一种激酶的抑制图谱提供了一个“指纹”，允许识别作用于细胞提取物中的目标亚磷酸盐上的未知激酶。该方法在计算机和基因筛查方面具有明显的优势，我们已经在不同的激酶-亚磷酸盐对上进行了验证。它有可能帮助揭示疾病，特别是癌症中激酶通路的重新连接，并可能揭示个别患者的治疗靶点。在这个项目中，学生将确定合适的已知磷酸化位点(例如，碱性激酶的目标基序；R/K-x-S/T、R/K-x-x-S/T和R/K-x-R/K-x-x-S/T)，并测试KipIK是否能够从碱性激酶Akt1的目标开始区分相关共识位点的激酶。他们将使用KIPIK来确定Kinomica数据集中常见的、对应的激酶(S)未知的磷酸化位点的候选激酶，并使用细胞生物学方法验证它们，如RNA干扰、CRISPR、抑制研究和体外激酶分析。学生将在一年级内访问Kinomica，熟悉他们的技术并确定其他学习目标，然后在更长的一段时间内整合案例项目中的知识。KiPIK的成功将验证KiPIK作为基础和基于疾病研究的平台，允许阐明疾病中的激酶信号和途径重新布线，并将增加Kinomica技术提供的信息。

Phosphorylation is a vital cellular regulatory mechanism. Alterations in phosphorylation are major contributors to disease, and numerous clinically-important drugs target kinases to treat cancer and other disorders. Acquired resistance to these therapies can arise by rewiring of kinase pathways. Characterising signalling networks is therefore crucial to delineate changes in disease, to improve predictions of drug efficacy and patient response to targeted therapies, to understand resistance, and to identify biomarkers and diagnostics. To this end, Kinomica offers mass-spectrometry-based and computational services that interrogate thousands of phosphorylation sites and elucidate large phosphoproteomic networks.Although thousands of phosphorylation sites are known, identifying the kinases for particular phosphorylation events is a major roadblock. This hinders understanding of signalling changes in disease and, for Kinomica, means that the depth of their phosphoproteomic datasets cannot be fully exploited. To address these problems, we recently developed a generally-applicable method ("KiPIK") to identify kinases for specific phosphorylation sites that uses libraries of kinase inhibitors that have been profiled on near-kinome-wide panels of protein kinases. The inhibition profile for each kinase provides a 'fingerprint' that allows unknown kinases acting on target phosphosites in cell extracts to be identified. The method has clear advantages over in silico and genetic screening, and we have validated it on diverse kinase-phosphosite pairs. It has potential to help reveal kinase pathway rewiring in disease, particularly cancer, and perhaps to reveal therapeutic targets in individual patients. However, a remaining question is whether KiPIK can unambiguously identify kinases for phosphorylation sites of kinases with related consensus motifs.In this project, the student will identify suitable known phosphorylation sites (eg target motifs of basophilic kinases; R/K-x-x-x-x-S/T, R/K-x-x-S/T and R/K-x-R/K-x-x-S/T) and test whether KiPIK can distinguish kinases for related consensus sites, beginning with targets of the basophilic kinase Akt1. They will use KiPIK to identify candidate kinases for phosphorylation sites commonly present in Kinomica's datasets for which the corresponding kinase(s) are unknown, and validate them using cell biological approaches such as RNA interference, CRISPR, inhibition studies, and in vitro kinase assays. The student will visit Kinomica within year 1 to familiarise themselves with their technology and identify additional targets for study, and then later for an extended period to integrate knowledge from the CASE project. Success would validate KiPIK as a platform for fundamental and disease-based research, allowing the elucidation kinase signalling and pathway rewiring in disease, and would increase the information provided by Kinomica's technologies.