RT Journal Article
SR Electronic
T1 Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 656025
DO 10.1101/656025
A1 Joseph C. Boyd
A1 Alice Pinheiro
A1 Elaine Del Nery
A1 Fabien Reyal
A1 Thomas Walter
YR 2019
UL http://biorxiv.org/content/early/2019/06/03/656025.abstract
AB 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.