Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen
Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen
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DOI：
10.1093/bioinformatics/btz774
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发表时间：
2020-03-01
期刊：
影响因子：
5.8
通讯作者：
Walter, Thomas
中科院分区：
文献类型：
--
作者：
Boyd, Joseph C.;Pinheiro, Alice;Walter, Thomas
动机：高内涵筛选是药物发现和表征的重要工具。通常，高含量的药物筛选是在一个单细胞系上进行的。然而，单细胞系不能被认为是完美的疾病模型。许多疾病具有重要的分子异质性。因此，一种药物可能对一种疾病的一种分子亚型有效，但对另一种分子亚型则不那么有效。表征药物的影响，不仅对一个细胞系，但对一组细胞系，因此是一个很有前途的策略，以简化药物发现process.Results：这篇文章的贡献是2倍。首先，我们研究是否可以在分子水平上预测药物作用机制（MOA），而无需优化MOA类别以筛选特异性。为此，我们基准的一组算法在传统的管道，并评估其MOA预测性能根据统计严格的框架。其次，我们将这种传统的管道扩展到多个细胞系的同时分析，每个细胞系都表现出潜在的不同形态基线。为此，我们提出了多任务自动编码器，包括用于构建跨细胞系的域不变特征表示的域自适应模型。我们将这些方法应用于两种三阴性乳腺癌细胞系的试点筛选，作为两种不同分子亚型的疾病模型。
Motivation: High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process.Results: The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.