PDF(Pubmed)
Abstract：
Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.
摘要：
缺乏疗效或药物不良反应是药物治疗中常见的现象，会导致相当大的发病率和死亡率。据估计，药物反应中这种变异性的20-30％源于编码药物靶标的基因或涉及药物处置的因子的变异。利用这种药物基因组学信息来预先识别将从剂量调整或替代药物中受益的患者，因此构成了精准医学的重要前沿。计算方法可用于预测未知意义的变体的功能效应。然而,他们在药物基因组变异数据上的表现一直平淡无奇。为了克服这个限制,我们以前开发了一个集成分类器，称为APF，专门设计用于药物基因组变异预测。这里,我们旨在通过利用基于深度神经网络的蛋白质折叠预测的最新进展来进一步改善预测。对530个药物遗传学错义变体的28个变体效应预测因子进行基准测试显示，使用AlphaMissense的结构预测是最具体的，而APF表现出最平衡的表现。然后我们开发了一个新工具，APF2，通过优化用于药物基因组变异的顶级执行算法的算法参数化，并将其预测汇总为统一的整体评分。重要的是,APF2提供了与实验结果（R2=0.91，p=0.003）良好相关的定量变体效应估计，并比以前的方法更准确地预测药物基因组变体的功能影响。特别是对于具有可操作的药物基因组指南的临床相关变异。此外,我们在61个未用于模型训练或验证的药源的146个变体的独立测试集上证明了更好的性能(92%的准确度)。将APF2应用于来自800,000多个个体的人口规模测序数据显示出巨大的种族地理差异，对药物治疗具有重要意义。因此，我们认为APF2具有改善遗传信息转化为药物遗传学建议的潜力，从而促进将下一代测序数据用于分层医学。
