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一个基于人类癌症的蛋白质基因组学数据驱动的知识库。

A proteogenomics data-driven knowledge base of human cancer.

机构信息

Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

出版信息

Cell Syst. 2023 Sep 20;14(9):777-787.e5. doi: 10.1016/j.cels.2023.07.007. Epub 2023 Aug 23.

Abstract

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

摘要

通过将基于质谱的蛋白质组学和磷酸化蛋白质组学与基因组学、表观基因组学和转录组学相结合,蛋白质基因组学为癌症提供了全面的分子特征描述。利用这种方法,临床蛋白质组肿瘤分析联盟(CPTAC)已经对 10 种癌症类型的 1000 多个原发性肿瘤进行了特征描述,其中许多都有匹配的正常组织。在这里,我们展示了 LinkedOmicsKB,这是一个蛋白质基因组学数据驱动的知识库,通过大约 40000 个以基因、蛋白质、突变和表型为中心的网页向公众提供一致处理和系统预计算的 CPTAC 泛癌症蛋白质基因组学数据。可视化技术有助于高效探索和推理复杂的、相互关联的数据。通过三个案例研究,我们说明了 LinkedOmicsKB 在提供有关基因、磷酸化位点、体细胞突变和癌症表型的新见解方面的实际效用。LinkedOmicsKB 预先计算了 19701 个编码基因、125969 个磷酸化位点和 256 个基因型和表型的结果,提供了一个全面的资源,以加速蛋白质基因组学数据驱动的发现,从而提高我们对人类癌症的理解和治疗水平。本文透明同行评审过程的记录包含在补充信息中。

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