Abstract 2158: Combining cell panel profiling and synthetic lethality data to efficiently screen for synergistic combinations

Abstract

Abstract In cancer therapy, combination drug treatment aims to improve response rate and decrease the development of drug resistance. The discovery of new effective drug combinations is constrained by the cost and effort of carrying out large unbiased screens and by poor translation of results towards the clinic. Here we describe how focusing on the biological mechanisms underlying the activity of drug candidates may aid a priori selection of promising synergy candidates and help in translate synergistic combinations towards a clinical situation. We have previously shown [1] that curve shift analysis as developed by Straetemans et al. [2] is a better method than combination-matrix screening. Also a dose based score such as the isobologram or the CI-index more robustly assesses synergy than an effect-based score such as the Bliss-additivity [1]. On this basis, we developed a two-step synergy screening approach, called SynergyScreen™. By distinguishing separate synergy screening and synergy confirmation stages, this setup capitalizes on insights from high throughput screening to discover robust and reproducible pharmacologically synergistic pairs. To further improve the efficiency of synergy screening, we focused on pre-selecting compounds in our screening library according to their biological mechanism. We profiled a library of more than 160 anti-cancer agents in a cell panel of 102 cell lines from diverse tumor origins [3]. Agents were clustered according to response and so-called exemplars were collected into a focused library that represents the spectrum of biological mechanisms of current cancer therapy. This synergy screening library includes many standard of care chemotherapeutic agents, approved and pre-clinical kinase inhibitors, epigenetic modulators and compounds acting by other mechanisms. Finally, we harnassed recent insights into the biology of synergy to understand and predict synergistic pairs. A tool was developed that uses the response of a compound in a 102 cell line panel to pick potential synergistic partners from the database of preprofiled compounds. It does this by computationally assessing if pairs can pharmacologically mimick clinically validated synthetic lethal interactions [4]. We optimized prediction accuracy using the results of internal and external synergy screens. The tool was applied to specifically enrich test libraries and to predict synergies at clinically relevant doses, including the results of a SynergyScreen™ with the poly-ADP ribose polymerase (PARP) inhibitor niraparib and the BET bromodomain inhibitor JQ1.