通过将基于质谱的蛋白质组学和磷酸蛋白质组学与基因组学、表观基因组学和转录组学相结合，蛋白组学基因组学提供了癌症的综合分子特征化。利用这种方法，临床蛋白质肿瘤分析联盟（CPTAC）已对10种癌症类型的1000多个原发性肿瘤进行了特征化研究，其中许多肿瘤有相匹配的正常组织。在这里，我们介绍了LinkedOmicsKB，这是一个蛋白组学基因组学数据驱动的知识库，通过约40,000个基于基因、蛋白质、突变和表型的网页，向公众提供了一致处理和系统预计算的CPTAC全癌种蛋白组学数据。可视化技术能够有效地探索和推理复杂互相关联的数据。通过三个案例研究，我们展示了LinkedOmicsKB在提供新的思路方面的实际实用性，包括基因、磷酸化位点、体细胞突变和癌症表型等。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，为加速蛋白组学基因组学数据驱动的发现提供了全面的资源，以改善我们对人类癌症的理解和治疗。此论文的透明同行评审过程的记录已包含在附属信息中。版权所有©2023 Elsevier Inc. 保留所有权利。

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.Copyright © 2023 Elsevier Inc. All rights reserved.