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

一种了解癌症和其他疾病中激酶通路布线、重布线和耐药性的新方法

• 批准号: 2753336
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753336
• 关键词: new; approach; understand; kinase; pathway

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.

磷酸化是重要的细胞调节机制。磷酸化的改变是疾病的主要贡献者，并且许多临床上重要的药物靶向激酶以治疗癌症和其他疾病。对这些疗法的获得性耐药性可通过激酶途径的重新布线而产生。因此，表征信号网络对于描述疾病的变化，改善药物疗效和患者对靶向治疗的反应的预测，了解耐药性以及鉴定生物标志物和诊断至关重要。为此，Kinomica提供了基于质谱和计算的服务，可以询问数千个磷酸化位点并阐明大型磷酸化蛋白质组学网络。尽管已知有数千个磷酸化位点，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，这意味着他们的磷酸化蛋白质组数据集的深度无法得到充分利用。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”）来鉴定特定磷酸化位点的激酶，该方法使用已经在近激酶组范围的蛋白激酶组上进行了分析的激酶抑制剂文库。每种激酶的抑制谱提供了一个“指纹”，允许识别作用于细胞提取物中靶磷酸位点的未知激酶。该方法具有明显的优势，在硅片和遗传筛选，我们已经验证了它对不同的激酶磷酸化位点对。它有可能帮助揭示疾病，特别是癌症中的激酶通路重新连接，并可能揭示个体患者的治疗靶点。然而，一个遗留的问题是KiPIK是否可以明确地识别具有相关共有基序的激酶的磷酸化位点的激酶。（例如嗜碱性激酶的靶基序; R/K-x-x-x-x-S/T、R/K-x-x-S/T和R/K-x-R/K-x-x-S/T），并测试KiPIK是否可以区分相关共有位点的激酶，从嗜碱性激酶Akt 1的靶点开始。他们将使用KiPIK来识别Kinomica数据集中常见的磷酸化位点的候选激酶，这些位点的相应激酶是未知的，并使用细胞生物学方法（如RNA干扰，CRISPR，抑制研究和体外激酶测定）对其进行验证。学生将在第一年内访问Kinomica，熟悉他们的技术并确定其他学习目标，然后在较长的时间内整合CASE项目的知识。成功将验证KiPIK作为基础和基于疾病的研究平台，允许阐明疾病中的激酶信号传导和通路重新布线，并将增加Kinomica技术提供的信息。