Inferring Kinase Activity from Tumor Phosphoproteomic Data

项目摘要 激酶是用于通过调节蛋白质和蛋白质代谢来调节细胞生理学的基本上重要的酶。 通过磷酸化酪氨酸、丝氨酸和苏氨酸残基的蛋白质相互作用。激酶失调通常是一种 这就是为什么激酶抑制剂是FDA批准的最大类别之一， 肿瘤药物然而，在为PA提供基于精确度的激酶治疗方面仍然存在许多挑战。 治疗失败和通过各种手段对治疗产生耐药性。 该项目旨在推进一种有前途的新方法（称为KSTAR），用于了解激酶失调 通过基于磷酸化蛋白质组学特征推断肿瘤活检组织中激酶的活性来研究癌症。科士达 是一个一流的算法，可以对任何类型的磷酸蛋白质组学数据进行操作，不需要配对的定量， 有效的比较组织，并且比其他可用的方法显著更稳健。KSTAR被证明 通过识别对治疗无效和患者分类错误来补充临床护理标准 作为HER 2阳性或阴性，其偏离HER 2活性。与一系列合作者合作， 我们寻求进一步KSTAR的能力，以帮助研究人员和临床医生更好地匹配激酶抑制剂 根据患者的分子激酶活性特征进行治疗。关键的算法改进将被执行， 例如：将方法扩展到覆盖所有人类激酶，从免疫信号的去卷积， 间质成分的实体瘤活检，并增加速度。这项工作将推进和硬化dissem- 跨各种平台的KSTAR，这将允许其他程序员的最大灵活性，但也 基于Web的界面，无需编程即可与患者和细胞激酶配置文件进行交互。

Project Summary Kinases are fundamentally important enzymes for regulating cell physiology through regulation of proteins and protein interactions by phosphorylating tyrosine, serine, and threonine residues. Kinase dysregulation is often a contributor to cancer progression, which is why kinase inhibitors are one of the largest classes of FDA-approved drugs for oncology. However, many challenges still remain in providing precision-based kinase therapy to pa- tients, such as failure to respond to therapy and the development of resistance to therapy through diverse means. This project seeks to advance a promising new approach (called KSTAR) for understanding kinase dysregulation in cancer by inferring the activity of kinases in tumor biopsies, based on their phosphoproteomic profiles. KSTAR is a first-in class algorithm that can operate on any type of phosphoproteomic data, not requiring paired quantita- tive comparison tissues, and is significantly more robust than other available approaches. KSTAR was shown to compliment clinical standard of care by identifying failure to respond to therapy and misclassification of patients as HER2-positive or negative, which departed from HER2-activity. Working with collaborators across a range of solid cancers, we seek to further KSTAR's ability to help researchers and clinicians better match kinase inhibitor therapies, based on patient molecular kinase activity profiles. Key algorithmic improvements will be performed, such as: expansion of the approach to cover all human kinases, deconvolution of signaling from immune and stroma components of a solid tumor biopsies, and increasing speed. This work will advance and harden dissem- ination of KSTAR across a variety of platforms that will allow maximum flexibility for other programmers, but also web-based interfaces that require no programming to interact with patient and cell kinase profiles.