A Framework for The Study of Compound Interactions in L1000

Abstract

Treatment combinations are used broadly in clinical settings and are used to treat a variety of diseases and disorders. Despite the potential power of combination therapies to improve the lives of more patients, the complete potential of the space remains untapped. High-throughput screening methods allow for many experiments to be conducted in parallel, often testing hundreds of thousands or millions of single compounds. These approaches also enable researchers to test large volumes of compound combinations, but profiling many compound combinations is extremely resource demanding. This need has motivated many studies to computationally predict synergistic combinations using inputs such as compound targets, implicated pathways, or single-treatment transcriptional responses. Though some of these models have been successful, they are often generated with datasets intended for other applications, leaving ample room for improvement. To our knowledge there is currently no systemic dataset of transcriptional response intended to investigate the mechanisms of compound synergy. A dataset of this type would allow for an improvement of synergy prediction from phenotypic readouts in terms of model validation as well as the development of entirely novel models that may be retroactively applied to currently existing datasets. The Connectivity Map, and its underlying assay, the L1000 gene expression assay, is a powerful resource for high throughput small molecule screening and hypothesis generation (Subramanian et al., 2017). Though the assay has been used to profile single compounds extensively, there has yet been no large-scale effort to generate data using compound combinations. The L1000 platform relies strongly on comparisons to previously generated reference signatures (in L1000 and in other experimental contexts) and there is currently insufficient combinatorial reference data. We hope to enable future researchers to make use of those reference data generated to interrogate novel combinations.to predict synergies in novel disease areas. To address these shortcomings in both the synergistic research space as well as the L1000 platform reference dataset, we developed a framework to combine a cell-viability readout with an L1000-based gene expression readout on a diverse set of compound combinations. This allows us to better understand the molecular mechanisms of combinations shown previously to synergistically kill cancer cells. We have also developed a pilot workflow to carefully calibrate future experiments conducted with compound combinations, as well as identified several potential pitfalls to be avoided by future researchers generating combinatorial data on the L1000 platform. These data may subsequently be used to predict additional synergistic combinations and serve as a dataset to inform future experiments to be conducted on L1000 and other high-throughput screening platforms.