TY - JOUR T1 - Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen JF - bioRxiv DO - 10.1101/656025 SP - 656025 AU - Joseph C. Boyd AU - Alice Pinheiro AU - Elaine Del Nery AU - Fabien Reyal AU - Thomas Walter Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/06/03/656025.abstract N2 - High Content Screening (HCS) is an important tool in drug discovery and characterisation. Often, drug screens are performed in 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 characterise 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. The contribution of this paper is twofold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimisation 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 multitask 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. ER -