Abstract B1-39: Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer

Abstract

Abstract Purpose: To identify an optimal drug combination for addressing intratumor heterogeneity by measuring single drug responses at the level of single cells. Background: Drug combination strategies have been derived from drug-drug and drug-target interaction inferences made based on genomic network analysis, such as synthetic lethality network analysis and protein network analysis. A key limitation with these approaches is that they do not account for intratumor heterogeneity. A next generation high throughput single cell technology such as Cytometry Time-of-Flight (CyTOF) has the potential to circumvent this limitation by providing the key information for optimally targeting intratumor heterogeneity within the context of personalized medicine. Methods: We propose a systems level modeling framework for analyzing heterogeneous drug response data across a population of single cells. Our approach, called Mixture Nested Effects Models (MNEM), identifies multi-target drug combinations from single drug effects measured at the level of single cells. MNEM analyzes drug responses among subpopulations (aka clusters) of similar cells, where the subpopulations are defined by cellular surface markers. MNEM infers the nested drug target hierarchy (directed network) from a heterogeneous population of drug effects by combining subpopulation identification, subpopulation matching to infer the drug response pre- and post-treatment, and nested effects modeling for each subpopulation individually or through a mixture of subpopulations. An MNEM infers that Drug A targets are a superset of Drug B targets, if the heterogeneous downstream target effects resulting from Drug B are a noisy subset of those resulting from Drug A. This relationship is represented as a directed edge from Drug A to Drug B. From the graphical model of nested drug effects, a scoring function is superimposed to identify the optimal drug combinations. Results: We apply MNEM to CyTOF drug screening data derived from treating three well-established cancer cell lines: HeLa (ovarian), NCIH460 (lung) and HCT116 (colon) with Mek, mTOR, pP38MAPK, JnK I and PI3K inhibitors at different optimal dose levels in addition to the base line treatment TRAIL, an apoptotic stimulator. MNEM indicates that pP38MAPK and Mek inhibitors are important candidates for targeting the ovarian and lung cancer cell lines. The MNEM analysis from the colon cancer cell line data indicates that several optimal drug regimens may have equivalent efficacy. Survival data from drug combination invitro experiments using clonogenic assays support the results from MNEM. Overall, these analyses suggest that MNEM could be used to optimize drug combinations based on responses from single drugs measured at the single cell level. Conclusion: Intratumor heterogeneity in tumor response, measured at the level of single cells, upon exposure to single drugs may be combined through Mixture Nested Effects Modeling (MNEM) to infer optimal drug combinations. Citation Format: Benedict Anchang, Harris Fienberg, Sean Bendall, Robert Tibshirani, Sylvia K. Plevritis. Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-39.