Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics.
Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics.
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DOI：
10.1158/1541-7786.mcr-15-0189
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Costello JC
中科院分区：
文献类型：
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作者：
Goodspeed A;Heiser LM;Gray JW;Costello JC
与正常细胞相比，肿瘤细胞经历了一系列遗传和表观遗传改变。通常，这些变化是癌症发展、进展和耐药性的基础，因此模型系统的效用取决于它们概括原发性肿瘤中观察到的基因组畸变的能力。肿瘤来源的细胞系长期以来一直用于研究癌症中的潜在生物学过程，以及用于发现和评估抗癌疗法的功效的筛选平台。随着高通量技术和多组合作项目的进步，已经产生了超过一千种癌细胞系的多组学测量。这些数据补充了大型国际癌症基因组测序工作，以表征患者肿瘤，如癌症基因组图谱（TCGA）和国际癌症基因组联盟（ICGC）。鉴于已经产生的数据的范围和规模，研究人员现在能够评估细胞系和患者样本之间基因组特征的相似性和差异。作为药物基因组学模型，细胞系提供了易于生长、相对便宜和适于治疗剂的高通量测试的优点。然后，从细胞系产生的数据可以用于将细胞药物反应与基因组特征联系起来，最终目标是建立患者结果的预测特征。这篇综述强调了最近的工作，比较了组学概况的细胞系与原发性肿瘤，并讨论了癌症细胞系作为抗癌治疗的药物基因组学模型的优点和缺点。
Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies.