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

被引:34
|
作者
Anchang, Benedict [1 ]
Davis, Kara L. [2 ]
Fienberg, Harris G. [3 ]
Williamson, Brian D. [1 ]
Bendall, Sean C. [4 ]
Karacosta, Loukia G. [1 ]
Tibshirani, Robert [5 ,6 ]
Nolan, Garry P. [3 ]
Plevritis, Sylvia K. [1 ,5 ]
机构
[1] Stanford Univ, Dept Radiol, Ctr Canc Syst Biol, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Pediat, Div Hematol & Oncol, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Microbiol & Immunol, Stanford, CA 94305 USA
[4] Stanford Univ, Dept Pathol, Sch Med, Stanford, CA 94305 USA
[5] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA
[6] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
基金
美国国家科学基金会;
关键词
single-cell analysis; combination therapy; nested effects models; intratumor heterogeneity; leukemia; NESTED EFFECTS MODELS; MASS CYTOMETRY; LEUKEMIA; EXPRESSION; HIERARCHY; TRAIL; DEATH; LINE;
D O I
10.1073/pnas.1711365115
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:E4294 / E4303
页数:10
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