Cortés-Ciriano Isidro, van Westen Gerard J P, Bouvier Guillaume, Nilges Michael, Overington John P, Bender Andreas, Malliavin Thérèse E
Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75 724 Paris, France.
Medicinal Chemistry, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333CC, Leiden.
Bioinformatics. 2016 Jan 1;32(1):85-95. doi: 10.1093/bioinformatics/btv529. Epub 2015 Sep 8.
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.
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.
terez@pasteur.fr; ab454@ac.cam.uk
Supplementary data are available at Bioinformatics online.
近期的大规模组学计划已对癌细胞系面板的体细胞改变及其对数百种化合物的药理反应进行了编目。在本研究中，我们探索了这些数据，以推进计算方法，从而更有效且有针对性地使用当前及未来的抗癌治疗药物。
我们通过整合化学和生物学（细胞系）信息，对针对NCI60面板中59种癌细胞系筛选的17142种化合物的50%生长抑制生物测定终点（GI50）进行了建模（941831个数据点，矩阵完整性为93.08%）。我们确定，在对GI50终点进行建模时，蛋白质、基因转录本和miRNA丰度提供了最高的预测信号，其显著优于DNA拷贝数变异或外显子测序数据（Tukey真实显著差异，P<0.05）。我们证明，在数据范围内，我们的方法能够将化合物生物活性内插和外推到新的细胞系和组织，并且在较小程度上也能外推到不同的化合物。此外，我们的方法优于基于GDSC数据集生成的先前模型。最后，我们确定，在更详细研究的案例中，预测的药物-通路关联和生长抑制模式大多与实验数据一致，这也表明有可能在新的细胞系上识别新型化合物的药物敏感性基因组标记。
terez@pasteur.fr；ab454@ac.cam.uk
补充数据可在《生物信息学》在线获取。
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