TY - JOUR
T1 - DRUG-NEM
T2 - Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity
AU - Anchang, Benedict
AU - Davis, Kara L.
AU - Fienberg, Harris G.
AU - Williamson, Brian D.
AU - Bendall, Sean C.
AU - Karacosta, Loukia G.
AU - Tibshirani, Robert
AU - Nolan, Garry P.
AU - Plevritis, Sylvia K.
N1 - Publisher Copyright:
© 2018 National Academy of Sciences. All rights reserved.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and pheno-typic 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 Cytome-try 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.
AB - An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and pheno-typic 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 Cytome-try 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.
KW - Combination therapy
KW - Intratumor heterogeneity
KW - Leukemia
KW - Nested effects models
KW - Single-cell analysis
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U2 - 10.1073/pnas.1711365115
DO - 10.1073/pnas.1711365115
M3 - Article
C2 - 29654148
AN - SCOPUS:85046286330
SN - 0027-8424
VL - 115
SP - E4294-E4303
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 18
ER -