Inconsistency in large pharmacogenomic studies.

Inconsistency in large pharmacogenomic studies.
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DOI:
10.1038/nature12831
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发表时间:
2013-12-19
期刊:
影响因子:
64.8
通讯作者:
--
中科院分区:
综合性期刊1区
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癌细胞系研究长期以来一直用于测试治疗药物的功效并探索预测反应的基因组因素。最近发表了两项大规模药物基因组学研究;每个项目都检测了数百个癌细胞系的基因表达、拷贝数、基因组序列以及对多种抗癌药物的药理学反应。由此产生的数据集提供了一个独特的机会来表征与药物反应相关的机制,其中检测了 471 个细胞系和 15 种药物。然而,虽然研究之间的基因表达具有良好的相关性,但测量到的药理学药物反应却高度不一致。这种不良的对应性令人惊讶,因为这两项研究都使用常见的估计量来评估药物反应:IC50(药物抑制最大细胞生长的 50% 时的浓度)和 AUC(测量剂量反应的活性曲线下面积)。对于两项研究中筛选的药物,只有一种药物的测量响应的 Spearman 相关系数大于 0.6。重要的是,这些结果也反映在基因组特征和药物反应之间不一致的关联上。尽管这两项对照良好的研究之间药物反应测量不一致的根源仍不确定,但这使得得出关于反应的确切结论非常困难,并且对于使用这些结果测量来评估基因-药物关系或根据其报告的结果选择潜在的抗癌药物具有潜在的影响。我们的研究结果表明,药物基因组学研究中反应测量方案的标准化至关重要,这样的研究才能兑现其承诺。
Cancer cell line studies have long been used to test efficacy of therapeutic agents and to explore genomic factors predictive of response. Two large-scale pharmacogenomic studies were published recently; each assayed a panel of several hundred cancer cell lines for gene expression, copy number, genome sequence, and pharmacological response to multiple anti-cancer drugs. The resulting datasets present a unique opportunity to characterize mechanisms associated with drug response, with 471 cell lines and 15 drugs assayed in both. However, while gene expression is well correlated between studies, the measured pharmacologic drugs response is highly discordant. This poor correspondence is surprising as both studies assessed drug response using common estimators: the IC50 (concentration at which the drug inhibited 50% of the maximal cellular growth), and the AUC (area under the activity curve measuring dose response). For drugs screened in both studies, only one had a Spearman correlation coefficient in measured response greater than 0.6. Importantly these results are also reflected in inconsistent associations between genomic features and drug response. Although the source of inconsistencies in drug response measures between these two well-controlled studies remains uncertain, it makes drawing firm conclusions about response very difficult and has potential implications for using these outcome measures to assess gene-drug relationships or select potential anti-cancer drugs based on their reported results. Our findings suggest standardization of response measurement protocols in pharmacogenomic studies is essential before such studies can live up to their promise.
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