我们对60种源于人类的乳腺癌细胞系模型进行了定量蛋白质组学研究，深度达到了约13,000种蛋白质。对所得到的高通量数据集进行了质量和重复性评估。我们利用这些数据集识别和表征了乳腺癌的亚型，并展示了它们与已知的转录亚型相一致，揭示了即使在样本不足的蛋白质特征集中，分子亚型仍然保持不变。所有数据集都作为公共资源在LINCS门户上免费提供。我们预计，这些数据集不管是独立使用还是与基因组学、转录组学和磷酸蛋白质组学等互补测量相结合，可以用于预测药物反应、在信号通路模型中推断细胞系特异性环境，以及识别对治疗药物敏感性或耐受性的标记物。© 2023年 Springer Nature Limited.

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.© 2023. Springer Nature Limited.