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Proc Natl Acad Sci U S A. 2018 May 1;115(18):E4294-E4303. doi: 10.1073/pnas.1711365115. Epub 2018 Apr 13.

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

Author information

1
Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305.
2
Department of Pediatrics, Division of Hematology and Oncology, Stanford University, Stanford, CA 94305.
3
Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305.
4
Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305.
5
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
6
Department of Statistics, Stanford University, Stanford, CA 94305.
7
Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305; sylvia.plevritis@stanford.edu.

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.

KEYWORDS:

combination therapy; intratumor heterogeneity; leukemia; nested effects models; single-cell analysis

PMID:
29654148
PMCID:
PMC5939057
DOI:
10.1073/pnas.1711365115
[Indexed for MEDLINE]
Free PMC Article

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