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Frontiers in Mathematical Oncology


Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

Sylvia Plevritis

Stanford University

Abstract:  

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses serious challenges to cancer treatment, motivating the need for combination therapies. We optimize drug combination by accounting for intratumoral heterogeneity through the analysis of single cell signaling perturbations when an individual tumor sample is screened by a drug panel. Mass Cytometry Time-of-Flight (CyTOF) is a high throughput single cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample, analyzed pre- and post-treatment. We developed a computational framework, entitled DRUG-NEM, to analyze  CyTOF-based single cell drug perturbation data for the purpose of individualizing drug combinations. In its current implementation, DRUG-NEM optimizes the drug combinations by choosing the minimum number of drugs that produces the maximal desired intercellular effects based on nested-effects modeling. We demonstrate the performance of  DRUG-NEM for leveraging single cell perturbation data to identify optimal drug combinations on tumor cell lines and primary leukemia samples.