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DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral
Benedict Anchang1, Kara L Davis2, Harris G Fienberg3
1Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305.
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 challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.
Insights
Intratumoral heterogeneity in cancer necessitates combination therapies. A new computational framework, DRUG-NEM, analyzes single-cell data to personalize drug combinations for improved cancer treatment.
Area of Science:
- Oncology
- Computational Biology
- Biotechnology
Background:
- Malignant tumors exhibit intratumoral heterogeneity, complicating cancer treatment and driving the need for combination therapies.
- Single-cell technologies offer a way to analyze this heterogeneity and guide the selection of effective drug combinations.
Purpose of the Study:
- To develop a computational framework, DRUG-NEM, for analyzing single-cell drug perturbation data to personalize cancer drug combinations.
- To optimize drug combinations by identifying the minimal set of drugs yielding maximal desired intracellular effects.
Main Methods:
- Utilized Mass Cytometry Time-of-Flight (CyTOF) to generate high-throughput single-cell data measuring multiple markers.
- Developed the Drug Nested Effects Models (DRUG-NEM) computational framework to analyze CyTOF perturbation data.
- Applied nested effects modeling to optimize drug combinations based on intracellular effects.
Main Results:
- DRUG-NEM was developed to analyze single-cell drug perturbation data for personalized medicine.
- The framework optimizes drug combinations by selecting a minimal drug set for maximal therapeutic effect.
- Demonstrated DRUG-NEM's efficacy using cell line and leukemia patient data.
Conclusions:
- DRUG-NEM provides a computational approach to leverage single-cell data for personalized cancer therapy.
- This framework aids in selecting optimal drug combinations by accounting for tumor heterogeneity.
- The study highlights the potential of computational tools in advancing precision oncology.
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