Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.
Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.
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DOI：
10.1002/em.21996
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发表时间：
2016-04
影响因子：
2.8
通讯作者：
Dertinger SD
中科院分区：
文献类型：
--
作者：
Bryce SM;Bernacki DT;Bemis JC;Dertinger SD
与细胞对DNA损伤的反应相关的几个终点以及明显的细胞毒性被复合到一个小型化的“添加和读取”型流式细胞术试验中。试剂包括释放细胞核的清洁剂、碘化丙啶和作为泛dna染料的RNase、抗γ - h2ax、磷酸组蛋白H3和p53的荧光抗体，以及用于细胞核绝对计数的荧光微球。该实验应用于TK6细胞和67种不同的参考化学物质作为训练集。在96孔板中暴露24小时，除非沉淀或细胞毒性的预先提示，否则最高浓度为1 mM。在4小时和24小时时，等份去除并添加到含有该试剂混合物的微滴板中。在短暂的潜伏期之后，机器人采样促进了现场数据采集。单变量分析确定了生物标记物和时间点，这些标记物和时间点对于将药物分为三组有价值：致裂性、非优生性或非遗传毒性。通过考虑Wald检验p值、接收算子特征曲线和伪R2值等因素的前进过程，对这些动作模式预测进行了优化。一个特别高效的多项逻辑回归模型由四个因素组成：4hr γH2AX和磷酸化组蛋白H3值，以及24 hr p53和多倍体值。对于训练集化学品，四因素模型与我们的先验分类有94%的一致性。交叉验证通过留一法进行，在这种情况下，观察到91%的一致性。对17种未用于构建模型的化学物质的测试集进行了评估，其中一些在代谢激活系统存在的情况下使用了短期治疗，并且在16种情况下的作用模式被正确预测。这些初步结果令人鼓舞，因为它们表明机器学习策略可用于根据高效且高度可扩展的多路试验数据快速可靠地预测新化学品的基因毒性作用模式。
Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, “add and read” type flow cytometric assay. Reagents included a detergent to liberate nuclei, propidium iodide and RNase to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96 well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4 and 24 hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R2 values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals’ genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.