DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.
DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.
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DOI：
10.1073/pnas.1711365115
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发表时间：
2018-05-01
影响因子：
11.1
通讯作者：
Plevritis SK
中科院分区：
文献类型：
--
作者：
Anchang B;Davis KL;Fienberg HG;Williamson BD;Bendall SC;Karacosta LG;Tibshirani R;Nolan GP;Plevritis SK
单细胞高通量技术能够识别肿瘤内异质性的联合癌症疗法，这种现象已被证明会影响癌症治疗的有效性。我们开发并应用了一种方法，该方法在针对一组单一药物筛选单个肿瘤样本时，基于单细胞扰动响应来识别顶级药物组合。这种方法通过选择对单个样品产生最大细胞内期望效应的最小数量的药物来优化药物组合。单个恶性肿瘤由具有不同分子和表型特征的单细胞的异质集合组成，这种现象称为肿瘤内异质性。肿瘤内异质性对癌症治疗提出了挑战，激发了对联合治疗的需求。单细胞技术现在可用于指导有效的药物组合，通过分析药物组筛选的单个肿瘤样本的信号扰动来解释肿瘤内的异质性。具体而言，质谱流式细胞术飞行时间（CyTOF）是一种高通量单细胞技术，其能够在样品中数十万个细胞的单细胞水平上同时测量多个（40个）细胞内和表面标志物。我们开发了一个计算框架，题为药物嵌套效应模型（DRUG-NEM），分析CyTOF单药扰动数据的目的，个性化的药物组合。DRUG-NEM通过基于嵌套效应模型选择产生最大期望细胞内效应的最少数量的药物来优化药物组合。我们使用来自肿瘤细胞系和原发性白血病样本的单细胞药物扰动数据证明了DRUG-NEM的性能。
Single-cell high-throughput technologies enable the ability to identify combination cancer therapies that account for intratumoral heterogeneity, a phenomenon that has been shown to influence the effectiveness of cancer treatment. We developed and applied an approach that identifies top-ranking drug combinations based on the single-cell perturbation response when an individual tumor sample is screened against a panel of single drugs. This approach optimizes drug combinations by choosing the minimum number of drugs that produce the maximal intracellular desired effects for an individual sample. 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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影响因子：
82.9
作者：
Janes, Matthew R.;Limon, Jose J.;So, Lomon;Chen, Jing;Lim, Raymond J.;Chavez, Melissa A.;Vu, Collin;Lilly, Michael B.;Mallya, Sharmila;Ong, S. Tiong;Konopleva, Marina;Martin, Michael B.;Ren, Pingda;Liu, Yi;Rommel, Christian;Fruman, David A.
通讯作者：
Fruman, David A.
影响因子：
4.3
作者：
Niederberger T;Etzold S;Lidschreiber M;Maier KC;Martin DE;Fröhlich H;Cramer P;Tresch A
通讯作者：
Tresch A
影响因子：
5.8
作者：
Markowetz, F;Bloch, J;Spang, R
通讯作者：
Spang, R
影响因子：
14.8
作者：
Anchang, Benedict;Hart, Tom D. P.;Plevritis, Sylvia K.
通讯作者：
Plevritis, Sylvia K.
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP