Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 115, Issue 18, Pages E4294-E4303Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1711365115
Keywords
single-cell analysis; combination therapy; nested effects models; intratumor heterogeneity; leukemia
Categories
Funding
- National Cancer Institute [U54CA149145, U54CA209971]
- Parker Institute [122835]
- US Food and Drug Administration [HHSF223201610018C]
- Scripps Research Institute [U19A1100627]
- NetApp St. Baldrick's Foundation Scholar Award
- CureSearch Young Investigator Award
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1608987] Funding Source: National Science Foundation
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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 (>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.
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