scholarly journals Fibroblasts from metastatic sites induce broad-spectrum drug desensitization via modulation of mitochondrial priming

2017 ◽  
Author(s):  
Benjamin D. Landry ◽  
Thomas Leete ◽  
Ryan Richards ◽  
Peter Cruz-Gordillo ◽  
Gary Ren ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Tumor Heterogeneity ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Tumor Stroma ◽  
Fibroblast Cells ◽  
Drug Induced ◽  
Disease Stratification ◽  
Metastatic Sites

ABSTRACTDue to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor micro-environment. Using a large-scale co-culture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not cell intrinsic, but rather a product of tumor-fibroblast interactions. Additionally, we find that fibroblast cells influence tumor drug response in two distinct and divergent manners, which were predicable based on the anatomical origin from which the fibroblasts were harvested. These divergent phenotypes result from modulation of “mitochondrial priming” of tumor cells, caused by secretion of inflammatory cytokines, such as IL6 and IL8, from stromal cells.

scholarly journals Encircling the regions of the pharmacogenomic landscape that determine drug response

2018 ◽  
Author(s):  
Adrià Fernández-Torras ◽  
Miquel Duran-Frigola ◽  
Patrick Aloy
Keyword(s):  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Drug Repositioning ◽  
Gene Sets ◽  
Diffusion Analysis ◽  
Genome Wide ◽  
Molecular Determinants ◽  
Gene Modules

AbstractBackgroundThe integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.MethodsTo simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.ResultsWe apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.ConclusionsNetwork biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

scholarly journals Tumor‐stroma interactions differentially alter drug sensitivity based on the origin of stromal cells

2018 ◽  
Vol 14 (8) ◽  
Author(s):  
Benjamin D Landry ◽  
Thomas Leete ◽  
Ryan Richards ◽  
Peter Cruz‐Gordillo ◽  
Hannah R Schwartz ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Drug Sensitivity ◽  
Tumor Stroma

scholarly journals Gene isoforms as expression-based biomarkers predictive of drug response in vitro

2017 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Kelsie L. Thu ◽  
Jennifer Silvester ◽  
Petr Smirnov ◽  
Mathieu Lupien ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Kinase Inhibitor ◽  
Tissue Type ◽  
Breast Cancer Cell Lines ◽  
Molecular Features

ABSTRACTBackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.MethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.ResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.ConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

scholarly journals A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening

2018 ◽  
Author(s):  
Abhishekh Gupta ◽  
Prson Gautam ◽  
Krister Wennerberg ◽  
Tero Aittokallio
Keyword(s):  
Growth Rates ◽  
High Throughput Screening ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Drug Effects ◽  
Negative Control ◽  
Drug Induced ◽  
Current Cell ◽  
Growth Inhibitory ◽  
Culture Models

ABSTRACTAccurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

scholarly journals Assessment of pharmacogenomic agreement

2016 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

AbstractIn 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer1(GDSC) and Cancer Cell Line Encyclopedia2(CCLE), were inconsistent3. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response4, seemingly contradicting our previous findings3. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent5. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals Assessment of pharmacogenomic agreement

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 825 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals New insights in the expression of stromal caveolin 1 in breast cancer spread to axillary lymph nodes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cristian Scatena ◽  
Giovanni Fanelli ◽  
Giuseppe Nicolò Fanelli ◽  
Michele Menicagli ◽  
Paolo Aretini ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Cancer Metastasis ◽  
Tumor Stroma ◽  
Disease Recurrence ◽  
Metastatic Spread ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Axillary Lymph Node Metastases ◽  
Stromal Compartment ◽  
Metastatic Sites

AbstractRecent evidence suggests that a loss of expression of caveolin in the stromal compartment (sCav-1) of human invasive breast carcinoma (IBC) may be a predictor of disease recurrence, metastasis and poor outcome. At present, there is little knowledge regarding the expression of sCav-1 at the metastatic sites. We therefore studied sCav-1 expression in IBCs and in their axillary lymph nodes to seek a correlation with cancer metastasis. 189 consecutive invasive IBCs (53 with axillary lymph node metastases and 136 without) were studied by immunohistochemistry, using a rabbit polyclonal anti-Cav-1 antibody. In IBCs sCav-1 was evaluated in fibroblasts scattered in the tumor stroma whereas in lymph nodes sCav-1 was assessed in fibroblast-like stromal cells. For the first time, we observed a statistically significant progressive loss of sCav-1 from normal/reactive axillary lymph nodes of tumors limited to the breast to metastatic axillary lymph nodes, through normal/reactive axillary lymph nodes of tumors with axillary metastatic spread. These data indicate that Cav-1 expressed by the stromal compartment of lymph nodes, somehow, may possibly contribute to metastatic spread in IBC.

scholarly journals Alterations of mesenchymal stromal cells in cerebrospinal fluid: insights from transcriptomics and an ALS clinical trial

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ashley A. Krull ◽  
Deborah O. Setter ◽  
Tania F. Gendron ◽  
Sybil C. L. Hrstka ◽  
Michael J. Polzin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cerebrospinal Fluid ◽  
Growth Factors ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Large Scale ◽  
Therapeutic Benefit ◽  
Protein Levels ◽  
Genes Encoding ◽  
Intrathecal Space

Abstract Background Mesenchymal stromal cells (MSCs) have been studied with increasing intensity as clinicians and researchers strive to understand the ability of MSCs to modulate disease progression and promote tissue regeneration. As MSCs are used for diverse applications, it is important to appreciate how specific physiological environments may stimulate changes that alter the phenotype of the cells. One need for neuroregenerative applications is to characterize the spectrum of MSC responses to the cerebrospinal fluid (CSF) environment after their injection into the intrathecal space. Mechanistic understanding of cellular biology in response to the CSF environment may predict the ability of MSCs to promote injury repair or provide neuroprotection in neurodegenerative diseases. Methods In this study, we characterized changes in morphology, metabolism, and gene expression occurring in human adipose-derived MSCs cultured in human (hCSF) or artificial CSF (aCSF) as well as examined relevant protein levels in the CSF of subjects treated with MSCs for amyotrophic lateral sclerosis (ALS). Results Our results demonstrated that, under intrathecal-like conditions, MSCs retained their morphology, though they became quiescent. Large-scale transcriptomic analysis of MSCs revealed a distinct gene expression profile for cells cultured in aCSF. The aCSF culture environment induced expression of genes related to angiogenesis and immunomodulation. In addition, MSCs in aCSF expressed genes encoding nutritional growth factors to expression levels at or above those of control cells. Furthermore, we observed a dose-dependent increase in growth factors and immunomodulatory cytokines in CSF from subjects with ALS treated intrathecally with autologous MSCs. Conclusions Overall, our results suggest that MSCs injected into the intrathecal space in ongoing clinical trials remain viable and may provide a therapeutic benefit to patients.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals A Method for Measurement of Drug Sensitivity of Myeloma Cells Co-Cultured with Bone Marrow Stromal Cells

2013 ◽  
Vol 18 (6) ◽  
pp. 637-646 ◽  
Author(s):  
Kristine Misund ◽  
Katarzyna A. Baranowska ◽  
Toril Holien ◽  
Christoph Rampa ◽  
Dionne C. G. Klein ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Cell Survival ◽  
Stromal Cells ◽  
Bone Marrow Stromal Cells ◽  
Large Scale ◽  
Stromal Cell ◽  
Marrow Stromal Cells ◽  
Cell Nuclei ◽  
Myeloma Cells

The tumor microenvironment can profoundly affect tumor cell survival as well as alter antitumor drug activity. However, conventional anticancer drug screening typically is performed in the absence of stromal cells. Here, we analyzed survival of myeloma cells co-cultured with bone marrow stromal cells (BMSC) using an automated fluorescence microscope platform, ScanR. By staining the cell nuclei with DRAQ5, we could distinguish between BMSC and myeloma cells, based on their staining intensity and nuclear shape. Using the apoptotic marker YO-PRO-1, the effects of drug treatment on the viability of the myeloma cells in the presence of stromal cells could be measured. The method does not require cell staining before incubation with drugs, and less than 5000 cells are required per condition. The method can be used for large-scale screening of anticancer drugs on primary myeloma cells. This study shows the importance of stromal cell support for primary myeloma cell survival in vitro, as half of the cell samples had a marked increase in their viability when cultured in the presence of BMSC. Stromal cell–induced protection against common myeloma drugs is also observed with this method.

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