Trifluoperazine-induced exosomal gene expression profiles serve as predictive drug response biomarkers for glioblastoma

Novel treatment strategies are of paramount importance to improve clinical outcomes in pediatric AML. Since chemotherapy is likely to remain the cornerstone of curative treatment of AML, insights in the molecular mechanisms that determine its cytotoxic effects could aid further treatment optimization. To assess which genes and pathways are implicated in tumor drug resistance, we correlated ex vivo drug response data to genome-wide gene expression profiles of 73 primary pediatric AML samples obtained at initial diagnosis. Ex vivo response of primary AML blasts towards cytarabine (Ara C), daunorubicin (DNR), etoposide (VP16), and cladribine (2-CdA) was associated with the expression of 101, 345, 206, and 599 genes, respectively (p < 0.001, FDR 0.004–0.416). Microarray based expression of multiple genes was technically validated using qRT-PCR for a selection of genes. Moreover, expression levels of BRE, HIF1A, and CLEC7A were confirmed to be significantly (p < 0.05) associated with ex vivo drug response in an independent set of 48 primary pediatric AML patients. We present unique data that addresses transcriptomic analyses of the mechanisms underlying ex vivo drug response of primary tumor samples. Our data suggest that distinct gene expression profiles are associated with ex vivo drug response, and may confer a priori drug resistance in leukemic cells. The described associations represent a fundament for the development of interventions to overcome drug resistance in AML, and maximize the benefits of current chemotherapy for sensitive patients.

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Predicting chemosensitivity using drug perturbed gene dynamics

BMC Bioinformatics ◽

10.1186/s12859-020-03947-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Joshua D. Mannheimer ◽

Ashok Prasad ◽

Daniel L. Gustafson

Keyword(s):

Gene Expression ◽

Cell Line ◽

Computational Methods ◽

Predictive Models ◽

Drug Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Signatures

Abstract Background One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel. Results This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes. Conclusion Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

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Abstract 68: Neuroblastoma drug response profiles are associated with gene expression profiles

10.1158/1538-7445.am2016-68 ◽

2016 ◽

Author(s):

Jeffrey P. Bond ◽

Elizabeth VanSickle ◽

Thomas S. Dexhemier ◽

Richard R. Neubig ◽

Giselle L. Sholler

Keyword(s):

Gene Expression ◽

Drug Response ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Response Profiles

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Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

10.1101/145268 ◽

2017 ◽

Author(s):

Xinguo Lu ◽

Jibo Lu ◽

Bo Liao ◽

Keqin Li

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Drug Response ◽

Somatic Mutations ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Driver Genes ◽

Mutation Matrix ◽

Driving Pattern ◽

Gene Modules

The multiple types of high throughput genomics data create a potential opportunity to identify driver pattern in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. However, it is a great challenging work to integrate omics data, including somatic mutations, Copy Number Variations (CNVs) and gene expression profiles, to distinguish interactions and regulations which are hidden in drug response dataset of ovarian cancer. To distinguish the candidate driver genes and the corresponding driving pattern for resistant and sensitive tumor from the heterogeneous data, we combined gene co-expression modules and mutation modulators and proposed the identification driver patterns method. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles via weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNVs and somatic mutations, and a mutation network is constructed from this mutation matrix. The candidate modulators are selected from the significant genes by clustering the vertex of the mutation network. At last, regression tree model is utilized for module networks learning in which the achieved gene modules and candidate modulators are trained for the driving pattern identification and modulator regulatory exploring. Many of the candidate modulators identified are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

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