Pevzner Kirill, Simchi Nitzan, Arad Gali, Seger Eran
Protai Bio, Ramat Gan, Israel.
Methods Mol Biol. 2025;2905:163-169. doi: 10.1007/978-1-0716-4418-8_10.
This chapter describes protein kinase (will be termed here kinase) activity estimation methods and their application to clinical cancer phosphoproteomics datasets, proposing a novel approach for identification of protein kinases as therapeutic targets. Despite significant advances in genomics-based target identification, clinical proteomics and phosphoproteomics remain underutilized. We highlight the growing availability of proteomics data from projects like Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Proteomics Identifications Database (PRIDE), and review key kinase activity estimation algorithms, including PTM-SEA, KSEA, Rokai, KStar, and Kinome Atlas. Applying these methods on clinical phosphoproteomic data, we demonstrate the identification of hyperactivated kinases in specific cancer indications and highlight HER2 and EGFR as benchmarks. Our description underscores the potential of integrating kinase activity estimation with clinical phosphoproteomics to uncover new therapeutic targets and develop precision oncology therapies.
本章描述了蛋白激酶（本文将其称为激酶）活性估计方法及其在临床癌症磷酸化蛋白质组学数据集中的应用，提出了一种将蛋白激酶鉴定为治疗靶点的新方法。尽管基于基因组学的靶点鉴定取得了重大进展，但临床蛋白质组学和磷酸化蛋白质组学仍未得到充分利用。我们强调了来自临床蛋白质组肿瘤分析联盟（CPTAC）和蛋白质组学鉴定数据库（PRIDE）等项目的蛋白质组学数据越来越容易获取，并回顾了关键的激酶活性估计算法，包括PTM-SEA、KSEA、Rokai、KStar和激酶组图谱。将这些方法应用于临床磷酸化蛋白质组数据，我们展示了在特定癌症适应症中鉴定过度激活的激酶，并强调HER2和EGFR作为基准。我们的描述强调了将激酶活性估计与临床磷酸化蛋白质组学相结合以发现新的治疗靶点和开发精准肿瘤治疗方法的潜力。
