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Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

Abstract : 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. Availability and implementation:
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https://hal-mines-paristech.archives-ouvertes.fr/hal-02440603
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Joseph Boyd, Alice Pinheiro, Elaine Nery, Fabien Reyal, Thomas Walter. Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen. Bioinformatics, Oxford University Press (OUP), 2019, ⟨10.1093/bioinformatics/btz774⟩. ⟨hal-02440603⟩

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