Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.
Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.
Mass Spectrom Rev. 2024 Nov-Dec;43(6):1255-1269. doi: 10.1002/mas.21827. Epub 2022 Dec 10.
Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.
近年来，基于质谱（MS）的蛋白质组学技术的最新进展加速了其在更大数量的人类肿瘤标本研究中的应用。在过去的几年中，临床蛋白质组肿瘤分析联盟、国际癌症蛋白质基因组学联盟和其他组织已经生成了基于 MS 的蛋白质组学分析数据，并结合了数千个人类肿瘤的相应多组学数据。目前，公共领域的蛋白质组数据集可以由其他研究人员重新检查，这些研究人员关注的问题与原始研究探索的问题不同。在这篇综述中，我们研究了蛋白质组学在癌症研究中的作用不断增加，以及先前的研究及其相关数据集如何有助于改善癌症的临床诊断和治疗。我们还探索了来自癌症细胞系模型的公开可用的蛋白质组学和多组学数据，以展示此类数据如何帮助确定癌症亚组的治疗策略。
