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综合分析多平台反相蛋白芯片数据以评估对靶向治疗的反应的药效学。

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies.

机构信息

Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK.

Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

出版信息

Sci Rep. 2020 Dec 15;10(1):21985. doi: 10.1038/s41598-020-77335-0.

Abstract

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.

摘要

反相蛋白质阵列(RPPA)技术使用高特异性抗体的组合来测量细胞和组织中的蛋白质和蛋白质翻译后修饰。该方法提供了大量样本的敏感和精确的定量,因此已在临床和临床前样本分析中得到了应用。为了有效地整合到药物开发和临床实践中,具有一致结果的稳健检测至关重要。利用协作 RPPA 模型,我们着手使用不同的仪器设置和工作流程来评估三种不同 RPPA 平台之间的可变性。我们使用多种基于 RPPA 的方法在不同的实验室中运行,对一系列人类乳腺癌细胞及其对两种临床相关癌症药物的蛋白质水平反应进行了表征。我们整合了多平台 RPPA 数据,并使用无监督学习来识别不依赖于 RPPA 平台和分析工作流程的蛋白质表达和磷酸化特征。我们的研究结果表明,使用不同的 RPPA 平台对癌细胞系进行蛋白质组学分析可以识别对药物抑制的反应的一致图谱,包括使用不同的抗体来测量相同的靶抗原时也是如此。这些结果突出了 RPPA 技术的稳健性和可重复性,以及其识别疾病或对治疗反应的蛋白质标志物的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d617/7738515/3faba00c02a8/41598_2020_77335_Fig1_HTML.jpg

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