ABSTRACT

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.

Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.

Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

摘要

简介：癌细胞系 (CCL) 一直是癌症研究的主要资源。在过去的几十年里，它们在组学分析方法的开发中发挥了重要作用，并作为模型系统在细胞和癌症生物学方面产生了新知识。最近，随着数百个 CCL 中生成的基因组、转录组和蛋白质组数据数量不断增加，进行综合蛋白质组数据分析的潜力越来越大。

涵盖的领域：在这篇综述中，我们首先描述了 CCL 中最常用的蛋白质组分析方法。然后，我们讨论如何将这些蛋白质组学数据与基因组学数据整合起来进行蛋白质组学分析。最后，我们强调了一些最近从 CCL 的蛋白质组学分析中产生的生物学发现。

专家意见：迄今为止，CCL 的蛋白质基因组学分析已经使新蛋白质和蛋白质形式的发现成为可能。它还提高了我们对生物学过程的理解，包括蛋白质丰度的转录后调节和主要组织相容性复合物等位基因的抗原呈递。随着未来几年将在数百到数千个 CCL 中生成蛋白质组学数据，大规模蛋白质组学分析和与表型特征良好的 CCL 的数据集成将具有进一步的潜力。