Inferring Kinase Activity from Tumor Phosphoproteomic Data

项目摘要 激酶是通过调节蛋白质和蛋白质来调节细胞生理的基本重要酶 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 促进癌症进展，这就是为什么激酶抑制剂是FDA批准的最大类别之一 肿瘤学的药物。然而，在为患者提供基于精确的激酶治疗方面仍然存在许多挑战。 例如，对治疗无效和通过各种手段对治疗产生抵抗力。 这个项目试图推进一种有希望的新方法(称为KSTAR)来理解激酶失调 通过推断肿瘤活检组织中的激酶活性，基于它们的磷酸蛋白质组原fiLES。Kstar 是一种fi第一类算法，可以对任何类型的磷酸蛋白质组数据进行操作，不需要配对的数量- 与组织比较，fi明显比其他可用的方法更稳健。Kstar被展示给 通过识别患者对治疗无效和fi分类错误来赞扬临床护理标准 HER2阳性或阴性，与HER2活性背离。与一系列领域的合作者合作 对于实体癌症，我们寻求进一步提高KSTAR帮助研究人员和临床医生更好地匹配激酶抑制剂的能力 治疗，基于患者分子激酶活性PROfiLES。将执行关键的算法改进， 例如：将该方法扩展到覆盖所有人类激酶，从免疫和免疫信号的去卷积 间质成分的实体瘤活检，而且速度越来越快。这项工作将推进和强化分裂- KSTAR在各种平台上的集成，将允许其他程序员获得最大的fl灵活性，但也 基于网络的界面，无需编程即可与患者和细胞激酶专业fiLES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.