Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.
Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.
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DOI：
10.1016/j.mcpro.2023.100602
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发表时间：
2023-08
影响因子：
7
通讯作者：
Guo, Tiannan
中科院分区：
文献类型：
--
作者：
Sun, Rui;Ge, Weigang;Zhu, Yi;Sayad, Azin;Luna, Augustin;Lyu, Mengge;Liang, Shuang;Tobalina, Luis;Rajapakse, Vinodh N.;Yu, Chenhuan;Zhang, Huanhuan;Fang, Jie;Wu, Fang;Xie, Hui;Saez-Rodriguez, Julio;Ying, Huazhong;Reinhold, William C.;Sander, Chris;Pommier, Yves;Neel, Benjamin G.;Aebersold, Ruedi;Guo, Tiannan
乳腺癌（BC）的治疗和相关目标仍然有限，尤其是三阴性乳腺癌（TNBC）。我们使用数据独立采集（DIA）鉴定了76个人BC细胞系的6091个蛋白。将我们的蛋白质组学研究结果与先前的多组学数据集相结合，我们发现，包括蛋白质组学数据可以改善药物敏感性预测，并提供对作用机制的见解。随后，我们分析了用EGFR/AKT/mTOR抑制剂处理的九种细胞系（五种TNBC和四种非TNBC）的蛋白质组变化。在TNBC中，EGFR/mTOR抑制剂治疗后代谢途径失调，而AKT抑制剂影响RNA修饰和细胞周期途径。这种系统的多组学和对BC细胞蛋白质组的深入分析可以帮助优先考虑潜在的治疗靶点，并为TNBC的适应性耐药性提供见解。76个乳腺癌细胞系和9个干扰细胞系的蛋白质组学分析。基于机器学习的多组学预测乳腺癌细胞对90种药物的敏感性纵向蛋白质组分析提供了对耐药性的见解。在这里，我们提出了76人乳腺癌细胞系的深层蛋白质组资源。每个细胞系包括四个重复。然后，我们使用这些多组学数据建立了机器学习模型，以预测BC细胞对90种药物的敏感性。我们进一步进行了纵向蛋白质组学分析的9个细胞系扰动三种药物。总之，这项研究提出了迄今为止对BC细胞系最全面的蛋白质组学研究，并揭示了TNBC细胞的新分子见解。
Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC. Proteomic analysis of 76 breast cancer cell lines and 9 lines with perturbation. Machine learning-based multi-omics predicts breast cancer cells’ sensitivity to 90 drugs. Longitudinal proteomic profiling provides insights into drug resistance. Here we present a deep proteome resource of 76 human breast cancer cell lines. Each cell line included four replicates. Then we built machine learning models using these multi-omics data to predict BC cells’ sensitivity to 90 drugs. We further performed longitudinal proteomic profiling of 9 cell lines perturbed by three drugs. Together, this study presents the so far most comprehensive proteomic investigation of BC cell lines and uncovered novel molecular insights for TNBC cells.
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影响因子：
8
作者：
Xiang, T.;Jia, Y.;Sherris, D.;Li, S.;Wang, H.;Lu, D.;Yang, Q.
通讯作者：
Yang, Q.
影响因子：
56.9
作者：
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通讯作者：
Krogan, Nevan J.
影响因子：
82.9
作者：
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影响因子：
16.6
作者：
Asleh K;Negri GL;Spencer Miko SE;Colborne S;Hughes CS;Wang XQ;Gao D;Gilks CB;Chia SKL;Nielsen TO;Morin GB
通讯作者：
Morin GB
使用 iRT（一种标准化保留时间）可以更有针对性地测量肽。
影响因子：
3.4
作者：
Escher, Claudia;Reiter, Lukas;MacLean, Brendan;Ossola, Reto;Herzog, Franz;Chilton, John;MacCoss, Michael J.;Rinner, Oliver
通讯作者：
Rinner, Oliver