Bioinformatics and Computational Genomics Laboratory, Institut de recherches cliniques de Montréal, University of Montreal, Montreal, Quebec, Canada.
J Am Med Inform Assoc. 2013 Jul-Aug;20(4):597-602. doi: 10.1136/amiajnl-2012-001442. Epub 2013 Jan 26.
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.
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.
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.
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.
个性化医学中的一个持久挑战在于为每个个体患者选择合适的药物。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，人们已经深入研究了使用临床前模型系统，因为这种方法能够使数百种药物在多个细胞系中同时进行平行测试。
最近两项大型药物基因组学研究对超过 1000 个细胞系进行了多种抗癌药物的筛选。我们建议将这些数据集结合起来，构建和稳健地验证药物反应的基因组预测因子。我们比较了构建预测因子的五种不同方法，这些方法的复杂性逐渐增加。我们在交叉验证和两个大型验证集中评估了它们的性能，一个包含了训练集中存在的相同细胞系，另一个数据集由在训练阶段从未使用过的细胞系组成。
在两个数据集中发现了 16 种共同的药物。我们能够验证三种已测试药物（伊立替康、PD-0325901 和 PLX4720）的多元预测因子。此外，我们观察到，Hsp90 抑制剂 17-AAG 的反应可以通过单个基因 NQO1 的表达水平有效地预测。
这些结果表明，基因组预测因子可以针对特定药物进行稳健验证。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测试，并在个性化医学中发挥重要作用。
