Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Genetics & Genome Biology, SickKids Research Institute, Toronto, ON, Canada.
Bioinformatics. 2019 Oct 1;35(19):3743-3751. doi: 10.1093/bioinformatics/btz158.
Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet.
We introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity.
Processed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https://github.com/rampasek/DrVAE.
Supplementary data are available at Bioinformatics online.
个体化药物反应预测是癌症个性化医学的一个基本组成部分。人们已经付出了巨大的努力来发现生物标志物或开发机器学习方法，以实现癌症中药物反应的准确预测。将生物系统的先验知识纳入这些方法是提高预测性能的一个有前途的途径。药物诱导的转录组扰动效应的高通量细胞系测定是一种先验知识，但尚未完全纳入药物反应预测模型。
我们引入了一种统一的概率方法，即药物反应变分自动编码器（Dr.VAE），它同时对生存能力和转录组扰动两个方面的药物反应进行建模。Dr.VAE 是一种基于变分自动编码器的深度生成模型。我们的实验结果表明，在 26 种经过测试的美国食品和药物管理局批准的药物中，Dr.VAE 在 23 种药物中的表现与标准分类方法一样好，甚至更好。在一系列消融实验中，我们表明，Dr.VAE 的观察到的改进可以归因于将药物诱导的扰动效应与联合建模的治疗敏感性相结合。
使用 PyTorch（Paszke 等人，2017）处理的数据和软件实现可在以下网址获得：https://github.com/rampasek/DrVAE。
补充数据可在生物信息学在线获得。
