癌症基因组图谱 (TCGA) 和癌细胞系百科全书 (CCLE) 是癌症研究的基础资源，提供广泛的分子和表型数据。然而，这些队列的各种癌症类型的大规模蛋白质组数据仍然有限。在这里，我们扩展了之前的工作，使用反相蛋白芯片为大约 8,000 个 TCGA 患者样本和大约 900 个 CCLE 细胞系样本生成高质量的蛋白表达数据，涵盖 447 种临床相关蛋白。这些蛋白质表达谱为肿瘤间异质性和癌症依赖性提供了深刻的见解，并可作为体细胞改变的敏感功能读数。我们开发了一种以蛋白质为中心的系统策略，用于识别合成致死对，并通过实验验证蛋白激酶 A 亚基 α 和表皮生长因子受体之间的相互作用。我们还鉴定了具有临床相关性的转移相关蛋白标记物。该数据集为增进我们对癌症机制的理解、发现蛋白质生物标志物和开发创新治疗策略提供了宝贵的资源。© 2024。作者获得 Springer Nature America, Inc. 的独家许可。

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.