Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.
Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.
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DOI：
10.1093/bioinformatics/btv529
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发表时间：
2016-01-01
期刊：
影响因子：
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通讯作者：
Malliavin TE
中科院分区：
文献类型：
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作者：
Cortés-Ciriano I;van Westen GJ;Bouvier G;Nilges M;Overington JP;Bender A;Malliavin TE
动机：最近的大规模组学倡议已经编目了癌细胞系面板的体细胞变化以及它们对数百种化合物的药理学反应。在这项研究中，我们探索了这些数据，以推进计算方法，使当前和未来的抗癌治疗能够更有效和有针对性地使用。结果：通过整合化学和生物(细胞系)信息，我们建立了17个 -142化合物对59个癌细胞株的半数生长抑制终点(GI50)模型(941个 831个数据点，93.08%完成)。我们确定，蛋白质、基因转录物和miRNA丰度在模拟GI50终点时提供了最高的预测信号，显著优于DNA拷贝数变异或外显子组测序数据(Tukey的诚实显著差异，P<；0.05)。我们证明，在数据的限制范围内，我们的方法显示了对新的细胞系和组织以及不同化合物的生物活性进行内插和外推的能力。此外，我们的方法比以前在GDSC数据集上生成的模型性能更好。最后，我们确定在更详细调查的情况下，预测的药物途径关联和生长抑制模式与实验数据基本一致，这也表明在新的细胞系上识别新化合物药物敏感性的基因组标记的可能性。联系人：terez@pasteur.fr；ab454@ac.cam.uk补充信息：补充数据可从BioInformation Online获得。
Motivation: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics. Results: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17 142 compounds screened against 59 cancer cell lines from the NCI60 panel (941 831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey’s Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines. Contact: terez@pasteur.fr; ab454@ac.cam.uk Supplementary information: Supplementary data are available at Bioinformatics online.