Committee on Cancer Biology, University of Chicago, Chicago, IL, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States.
Committee on Cancer Biology, University of Chicago, Chicago, IL, United States; Ben May Department for Cancer Research, University of Chicago, Chicago, IL, United States.
Pharmacol Ther. 2018 Nov;191:178-189. doi: 10.1016/j.pharmthera.2018.06.014. Epub 2018 Jun 25.
High-throughput screens in cancer cell lines (CCLs) have been used for decades to help researchers identify compounds with the potential to improve the treatment of cancer and, more recently, to identify genomic susceptibilities in cancer via genome-wide shRNA and CRISPR/Cas9 screens. Additionally, rich genomic and transcriptomic data of these CCLs has allowed researchers to pair this screening data with biological features, enabling efforts to identify biomarkers of treatment response and gene dependencies. In this paper, we review the major CCL screening efforts and the large datasets these screens have made available. We also assess the CCL screens collectively and include a resource with harmonized CCL and compound identifiers to facilitate comparisons across screens. The CCLs in these screens were found to represent a wide range of cancer types, with a strong correlation between the representation of a cancer type and its associated mortality. Patient ages and gender distributions of CCLs were generally as expected, with some notable exceptions of female underrepresentation in certain disease types. Also, ethnicity information, while largely incomplete, suggests that African American and Hispanic patients may be severely underrepresented in these screens. Nearly all genes were targeted in the genetic perturbations screens, but the compounds used for the drug screens target less than half of known cancer drivers, likely reflecting known limitations in our drug design capabilities. Finally, we discuss recent developments in the field and the promise they hold for enabling future screens to overcome previous limitations and lead to new breakthroughs in cancer treatment.
几十年来,高通量筛选已在癌细胞系(CCLs)中得到广泛应用,以帮助研究人员识别具有改善癌症治疗潜力的化合物,最近,通过全基因组 shRNA 和 CRISPR/Cas9 筛选来鉴定癌症的基因组易感性。此外,这些 CCLs 的丰富基因组和转录组数据使研究人员能够将这些筛选数据与生物学特征相结合,从而努力识别治疗反应和基因依赖性的生物标志物。在本文中,我们回顾了主要的 CCL 筛选工作以及这些筛选产生的大量数据集。我们还对 CCL 筛选进行了全面评估,并包含一个协调的 CCL 和化合物标识符资源,以促进跨筛选的比较。这些筛选中的 CCL 代表了广泛的癌症类型,癌症类型的代表性与相关死亡率之间存在很强的相关性。CCL 的患者年龄和性别分布通常与预期相符,但在某些疾病类型中女性代表性不足是一些明显的例外。此外,尽管种族信息在很大程度上不完整,但表明非裔美国人和西班牙裔患者在这些筛选中可能严重代表性不足。几乎所有基因都在遗传扰动筛选中被靶向,但用于药物筛选的化合物仅靶向已知癌症驱动基因的一半以下,这可能反映了我们药物设计能力的已知局限性。最后,我们讨论了该领域的最新进展及其为未来筛选提供的潜力,以克服以前的局限性并在癌症治疗方面取得新的突破。