Currently, cancer drug combinations primarily focus on mutational heterogeneity of the primary tumor and do not account for single-cell variations that can give rise to drug resistance. Moreover, even with the increasing number of potential FDA-approved targeted drugs including immunotherapies, methods are needed to identify better combination therapy that leverages intratumor heterogeneity, thereby potentially mitigating the need for trials with large numbers of patients. Despite advances in single-cell technologies that capture intratumor heterogeneity, there are no drug combination strategies that utilize single-cell platforms. One idea that has been proposed is to use Mass Cytometry Time-of-Flight (CyTOF) for drug screening by producing drug perturbation effects at the level of the single cell, but the required analytics for the resulting complex data were not addressed. To address this unmet need, we have developed a novel algorithm to optimize combination therapy for an individual patient by analyzing distinct single-cell drug perturbation responses on a tumor sample. This model framework, called “DRUGNEM,” can be applied to CyTOF data, single-cell RNA-seq, or any single-cell imaging data currently available. DRUGNEM is composed of three steps: (1) identify the subpopulations that make up the tumor and may respond differently to treatment; (2) reconstruct a drug-nested-effects model that integrates the drug effects across all subpopulations to capture sub-setting relationships among individual drug effects; and (3) systematically score potential drug combinations to identify or prioritize strategies that will be clinically (and economically) sustainable. Currently, DRUGNEM is optimized to select the minimum number of drugs that produces the maximal desired intracellular effect, predicated on the premise that fewer drugs lower treatment-related toxicities and costs, but the final selection criterion can be easily modified. As proof of concept, we applied the DRUGNEM framework to individualize drug combinations based on CyTOF data generated on de-identified malignant research samples from 30 ALL pediatric patients before and after exposure to 3 targeted FDA -pproved single drugs (dasatinib, tofacitinib and BEZ235). We found that the most common combination treatment strategy (dasatinib and BEZ235) might not be optimal for all 30 ALL patients, with 2 of the 30 likely responding best to tofacitinib alone. Using in vitro survival assays, we validated the DRUGNEM prediction of BEZ235 and dasatinib as a potential synergistic combination on ALL cell lines. In summary, DRUGNEM is a novel framework using single-cell technologies to guide drug-combination strategies and can be adapted to incorporate complementary molecular data and computational methods to ultimately achieve more effective therapy for the individual cancer patient.

Citation Format: Benedict Anchang, Kara Davis, Harris Fienberg, Sean Bendall, Loukia Karacosta, Garry Nolan, Sylvia K. Plevritis. Individualized drug combination based on single-cell drug perturbations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2275.