Lawrence Robert T, Perez Elizabeth M, Hernández Daniel, Miller Chris P, Haas Kelsey M, Irie Hanna Y, Lee Su-In, Blau C Anthony, Villén Judit
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Center for Cancer Innovation, University of Washington, Seattle, WA 98109, USA; Department of Medicine, Division of Hematology, University of Washington, Seattle, WA 98195, USA.
Cell Rep. 2015 Apr 28;11(4):630-44. doi: 10.1016/j.celrep.2015.03.050. Epub 2015 Apr 16.
Triple-negative breast cancer is a heterogeneous disease characterized by poor clinical outcomes and a shortage of targeted treatment options. To discover molecular features of triple-negative breast cancer, we performed quantitative proteomics analysis of twenty human-derived breast cell lines and four primary breast tumors to a depth of more than 12,000 distinct proteins. We used this data to identify breast cancer subtypes at the protein level and demonstrate the precise quantification of biomarkers, signaling proteins, and biological pathways by mass spectrometry. We integrated proteomics data with exome sequence resources to identify genomic aberrations that affect protein expression. We performed a high-throughput drug screen to identify protein markers of drug sensitivity and understand the mechanisms of drug resistance. The genome and proteome provide complementary information that, when combined, yield a powerful engine for therapeutic discovery. This resource is available to the cancer research community to catalyze further analysis and investigation.
三阴性乳腺癌是一种异质性疾病,其临床预后较差且缺乏靶向治疗选择。为了发现三阴性乳腺癌的分子特征,我们对20个人源乳腺癌细胞系和4个原发性乳腺肿瘤进行了定量蛋白质组学分析,深度覆盖超过12,000种不同蛋白质。我们利用这些数据在蛋白质水平上鉴定乳腺癌亚型,并通过质谱法证明生物标志物、信号蛋白和生物途径的精确量化。我们将蛋白质组学数据与外显子序列资源整合,以识别影响蛋白质表达的基因组畸变。我们进行了高通量药物筛选,以识别药物敏感性的蛋白质标志物并了解耐药机制。基因组和蛋白质组提供互补信息,结合起来可产生用于治疗发现的强大引擎。该资源可供癌症研究界使用,以促进进一步的分析和研究。