Systematic drug perturbations on cancer cells reveal diverse exit paths from proliferative state

Zhou, Joseph; Işık, Zerrin; Xiao, Caide; Rubin, Irit; Kauffman, Stuart; Schroeder, Michael; Huang, Sui

doi:10.18632/oncotarget.7294

Cited by 13 publications

(12 citation statements)

References 32 publications

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“…The core interface in Morpheus is a graphical color heat map representing protein marker and size parameter measurements applied to each single-cell object. Determination of the Pearson correlation coefficient between each measured parameter generates a reorganized heat map of multiparametric similarity relationships for each classified MCF-7 drug dataset [40]. Using these hierarchical similarity clustering heat maps, we compared the grouping pattern of mitotic pHistone3 S28 -positive cells across the multiple ROIs of compound-treated MCF-7 ( Fig.…”

Section: Classification Of Drug-treated Mcf-7 Phenotypic Changes By Smentioning

confidence: 99%

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Bouzekri

Esch

Ornatsky

2019

Preprint

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In pharmaceutical research, high-content screening is an integral part of lead candidate development. Drug response in vitro over 40 parameters including biomarkers, signaling molecules, cell morphological changes, proliferation indexes and toxicity in a single sample could significantly enhance discovery of new therapeutics. As a proof of concept, we present a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single-cell quantitative information visualized as t-SNE plots and heatmaps. Human breast cancer-derived cell lines SKBR3, HCC1143 and MCF-7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF-7 cells dosed with three target-specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3 S28 , Ki-67 and p4E-BP1 T37/T46 in classified mitotic cells and anti-correlation with cell surface markers. Our study demonstrates that IMC data expands the number of measured parameters in single cells and brings higherdimension analysis to the field of cell-based screening in early lead compound discovery.

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Section: Classification Of Drug-treated Mcf-7 Phenotypic Changes By Smentioning

confidence: 99%

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Bouzekri

Esch

Ornatsky

2019

Preprint

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“…Therefore, signaling pathway and interaction network analysis became more attractive to give a new perspective for the classic analysis of gene expression data [14]- [16]. New algorithms have started to highlight important regulator proteins and International Journal of Bioscience, Biochemistry and Bioinformatics cellular processes in pathways; such results had not been found by applying naive differentially expression analysis [9], [17]- [20].…”

Section: Resultsmentioning

confidence: 99%

Estimation of Drug Treatment Effects on the Signaling Pathways

Erce¹,

Işık²

2017

IJBBB

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Developing a computational method to observe cell behaviors can help to understand the expected outcome of a drug treatment even before doing the wet-lab experiments. The main goal of this research is computing the effects of a drug treatment in the signaling level for a better understanding of cellular responses after a drug treatment. The proposed algorithm can work on various biological networks to quantitatively evaluate the effects of a drug treatment in the cell by using gene expression data. The method was applied on the integrated KEGG signaling pathway and the most effected proteins were identified after the application of 14 different drugs on lymphoma cells. The results showed that the most affected proteins are not the direct target proteins of the given drugs. Indeed, the protein activities in the distant parts of signaling pathways are highly changed upon drug treatments. The literature validation showed that some of the proteins, which are commonly effected by the treatment of several drugs, have cancer related cellular activities as well.

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“…These identified mediators can provide insight into the underlying mechanisms for SNP-gene-trait associations to improve detection of gene-trait associations and to prioritize biological units for functional follow-up studies. Using MOSTWAS and iCOGs summary-level GWAS statistics for breast cancer-specific survival [36], we identified 11 survival-associated loci that are enriched for p53 binding and oxidoreductase activity pathways [66,67]. These loci include two genes (MAP3K6 and MAP4K5 ) encoding mitogen-activated protein kinases, which are signaling transduction molecules involved in the progression of aggressive breast cancer hormone subtypes [68].…”

Section: Discussionmentioning

confidence: 99%

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Bhattacharya

Love

2020

Preprint

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Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or possible effects underlying the SNP-gene association. Here, we outline multi-omic strategies for transcriptome imputation from germline genetics for testing gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final model as fixed effects with local SNPs to the gene included as regularized effects. In the second extension, we assess distal-eSNPs (SNPs in eQTLs) for their mediation effect through mediators local to these distal-eSNPs. Highly mediated distal-eSNPs are then included in the eventual transcriptomic prediction model. We show considerable gains in percent variance explained of gene expression and TWAS power to detect gene-trait associations using simulation analysis and real data applications with TCGA breast cancer data and in ROS/MAP brain tissue data. This integrative approach to transcriptome-wide imputation and association studies aids in understanding the complex interactions underlying genetic regulation within a tissue and identifying important risk genes for various traits and disorders.the matrix X Mj be the local-genotype dosages in a 500 kilobase window around mediator j, 1 ≤ j ≤ m G . Furthermore, let M j be the intensity of mediator j (methylation M -value if j is a CpG site, expression if j is a miRNA or a gene, etc). Prior to any modeling, we scale Y G and all M j , 1 ≤ j ≤ m G to zero mean and unit variance. We also residualize M j , 1 ≤ j ≤ m G and Y G with the covariate matrix X C to account for population stratification using principal components of the global genotype matrix and relevant clinical covariates to obtainM j , 1 ≤ j ≤ m G andỸ G .Transcriptome prediction in MeTWAS draws from two-step regression, as summarized in Figure 1.First, in the training set for a given training-test split, for 1 ≤ j ≤ m G , we model the residualized intensitỹ M j of training-set specific mediator j with the following additive model:where w j is the effect-sizes of the SNPs in X † Mj onM j in the training set. As in traditional transcriptomic imputation models [3,4], we findŵ j using one of the two following methods with the largest predicted adjusted R 2 : (1) elastic net regression with mixing parameter α = 0.5 and λ tuned over 5-fold cross validation using glmnet [19], or (2) linear mixed modeling assuming random effects for X Mj using rrBLUP [20]. Only significantly heritable (default P < 0.05 for the likelihood ratio test) [21] and well-cross validated (default R 2 ≥ 0.01) expression models are considered.For all j, using these opti...

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Systematic drug perturbations on cancer cells reveal diverse exit paths from proliferative state

Cited by 13 publications

References 32 publications

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Estimation of Drug Treatment Effects on the Signaling Pathways

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

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