Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK.
Nat Commun. 2023 Jul 18;14(1):4308. doi: 10.1038/s41467-023-39765-y.
A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
全面描述癌症患者的血液蛋白质组谱特征有助于更好地了解疾病的病因,从而实现更早的诊断、风险分层以及对不同癌症亚型的更好监测。在这里,我们描述了下一代蛋白质组学在探索多种主要癌症类型患者血液中蛋白质组特征方面的应用。在诊断和治疗前,从 1400 多名癌症患者的少量血液中测量了 1463 种蛋白质的血浆谱。疾病血液图谱资源允许对从个体癌症患者采集的血液中的个体蛋白质谱进行探索。我们还展示了一些研究,其中基于机器学习的分类模型已被用于识别与分析的每种癌症相关的一组蛋白质。讨论了下一代血浆分析在癌症精准医学中的应用。