Comparison and validation of genomic predictors for anticancer drug sensitivity
Comparison and validation of genomic predictors for anticancer drug sensitivity
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DOI：
10.1136/amiajnl-2012-001442
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发表时间：
2013-07-01
影响因子：
6.4
通讯作者：
Haibe-Kains, Benjamin
中科院分区：
文献类型：
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作者：
Papillon-Cavanagh, Simon;De Jay, Nicolas;Haibe-Kains, Benjamin
个性化医疗的一个持久挑战在于为每个患者选择正确的药物。虽然在大型试验中对患者进行药物测试是评估其临床疗效和毒性的唯一方法，但我们严重缺乏资源来测试目前正在开发的数百种药物。因此，临床前模型系统的使用已得到深入研究，因为这种方法可以在多个细胞系中并行测试数百种药物的反应。方法最近进行了两项大规模药物基因组学研究，在超过1000个细胞系中筛选了多种抗癌药物。我们建议将这些数据集联合收割机来构建和稳健地验证药物反应的基因组预测因子。我们比较了五种不同的方法来构建越来越复杂的预测。我们评估了他们的交叉验证和两个大的验证集的性能，一个包含相同的细胞系存在于训练集和另一个数据集的细胞系，从来没有被使用在training phase.Results十六种药物被发现在数据集之间的共同点。我们能够验证16种测试药物中3种的多变量预测因子，即伊立替康、PD-0325901和PLX 4720。此外，我们还观察到通过单个基因NQO 1的表达水平可以有效地预测对Hsp 90抑制剂17-AAG的反应。结论这些结果表明，基因组预测因子可以用于特异性药物的预测。如果在患者的肿瘤细胞中成功验证，并随后在临床试验中验证，它们可以作为相应药物的伴随试验，并在个性化医疗中发挥重要作用。
Background An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel.Methods Two large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase.Results Sixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1.Conclusion These results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.