CellLineNavigator: a workbench for cancer cell line analysis

Krupp, Markus; Itzel, Timo; Maass, T; Hildebrandt, Andreas; Galle, Peter R.; Teufel, Andreas

doi:10.1093/nar/gks1012

Cited by 19 publications

(18 citation statements)

References 26 publications

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“…As for leukocytes (T and B cells, monocyte, and neutrophil), Mfge8 expression may be regulated by complex mechanisms involving cell differentiation and environmental conditions. Multiple genome-wide microarray studies performed to measure the transcriptomics profi le of various cancer cell lines [ 49 ] indicate that several cancer cell lines derived from cancer tissues of mammary gland, ovary, brain, and uterus show (Table 1.3 ), which is consistent with the expression profi le of human post mortem tissue samples (see Table 1.1 ).…”

Section: Expression Profi Ling By Genomic Studiessupporting

confidence: 58%

MFG-E8: Origin, Structure, Expression, Functions and Regulation

Oshima

Yasueda

Nishio

et al. 2014

MFG-E8 and Inflammation

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Milk Fat Globule-Epidermal Growth Factor-Factor 8 (MFG-E8) with bivalent binding activity to integrin receptors and acidic phospholipids is a secretary glycoprotein exhibiting versatile functions in cell physiology affecting health and diseases. Recent progress in genomics and structural biology studies, in addition to long time accumulation of classical biochemical studies, has been showing its unique molecular structure advantageous to the link between cells and their target cells as well as molecular complexes and its critical roles in regulating infl ammation and immunity. MFG-E8 is expressed and secreted by a variety of cells and tissues, especially professional and non-professional phagocytes such as macrophages, immature dendritic cells and the epithelial cells of mammary glands and epididymis. Although MFG-E8 expression has been shown to be up-regulated by a lactogenic hormone, prolactin (PRL), and neuronal chemokine, fractalkine, molecular mechanisms involved in MFG-E8 expression in health and disease states remain largely unknown. The secreted MFG-E8 specifi cally recognizes the acidic phospholipids and opsonized the targets to be cleared by phagocytosis mainly in paracrine and occasionally in endocrine manners. In addition to the enhancing role in phagocytosis, MFG-E8 binds to its target cells expressing MFG-E8 receptors and acts as a ligand, which modulates infl ammatory responses. Moreover, MFG-E8 alone shows ligand activity to intestinal epithelial cells locally exposing the acidic phospholipids and vascular endothelial cells expressing the integrins.

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Section: Expression Profi Ling By Genomic Studiessupporting

confidence: 58%

MFG-E8: Origin, Structure, Expression, Functions and Regulation

Oshima

Yasueda

Nishio

et al. 2014

MFG-E8 and Inflammation

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“…34), in all 861 cancer cell lines other than breast cancer, the ER pathway was predicted to be inactive (data not shown), indicating a very high specificity of the current model for ER pathway activity in breast cancer. Furthermore, running the pathway model on replicate experiments from datasets E-MTAB-37 (35) and GSE23593 (36) showed good reproducibility of predictions (Supplementary Table S4 and Supplementary Fig. S1).…”

Section: Initial Validation Of the Er Pathway Modelmentioning

confidence: 85%

Selection of Personalized Patient Therapy through the Use of Knowledge-Based Computational Models That Identify Tumor-Driving Signal Transduction Pathways

et al. 2014

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Increasing knowledge about signal transduction pathways as drivers of cancer growth has elicited the development of "targeted drugs," which inhibit aberrant signaling pathways. They require a companion diagnostic test that identifies the tumor-driving pathway; however, currently available tests like estrogen receptor (ER) protein expression for hormonal treatment of breast cancer do not reliably predict therapy response, at least in part because they do not adequately assess functional pathway activity. We describe a novel approach to predict signaling pathway activity based on knowledge-based Bayesian computational models, which interpret quantitative transcriptome data as the functional output of an active signaling pathway, by using expression levels of transcriptional target genes. Following calibration on only a small number of cell lines or cohorts of patient data, they provide a reliable assessment of signaling pathway activity in tumors of different tissue origin. As proof of principle, models for the canonical Wnt and ER pathways are presented, including initial clinical validation on independent datasets from various cancer types. Cancer Res; 74(11); 2936-45. Ó2014 AACR.

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“…Curly brackets indicate differently migrating isoforms for the relevant proteins. ( C ) Scatterplot of protein abundance from quantitative MS versus microarray gene expression levels from CellLineNavigator (32) for all identified proteins in MOLT-4 nuclear extracts. The dotted line shows a line of best fit and an R 2 value from Spearman correlation is also indicated.…”

Section: Resultsmentioning

confidence: 99%

“…To rule out that reverseChIP is biased towards detecting the binding of only the most abundantly expressed transcription factors, we performed quantitative MS to determine the abundance of all proteins in the nucleoplasm, the input material for our reverseChIP experiments. We assessed the correlation between protein abundance and gene transcription, using publicly available microarray data from MOLT-4 cells in CellLineNavigator (32). As shown in Figure 3C, there is generally a good correlation between protein levels and gene expression ( R 2 = 0.545).…”

Section: Resultsmentioning

confidence: 99%

“…The log 2 value of the normalized protein intensity was used to plot against microarray gene expression. Microarray gene expression data from leukaemic cell lines samples were obtained from CellLineNavigator (32). The raw expression files were pre-processed including background subtractions, quantile normalization, log 2 transformation and batch effect removal using Partek Genomics Suite software (Partek Inc).…”

Section: Methodsmentioning

confidence: 99%

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A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of anERG-driven transcriptional network

et al. 2016

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Aberrant stem cell-like gene regulatory networks are a feature of leukaemogenesis. The ETS-related gene (ERG), an important regulator of normal haematopoiesis, is also highly expressed in T-ALL and acute myeloid leukaemia (AML). However, the transcriptional regulation of ERG in leukaemic cells remains poorly understood. In order to discover transcriptional regulators of ERG, we employed a quantitative mass spectrometry-based method to identify factors binding the 321 bp ERG +85 stem cell enhancer region in MOLT-4 T-ALL and KG-1 AML cells. Using this approach, we identified a number of known binders of the +85 enhancer in leukaemic cells along with previously unknown binders, including ETV6 and IKZF1. We confirmed that ETV6 and IKZF1 were also bound at the +85 enhancer in both leukaemic cells and in healthy human CD34+ haematopoietic stem and progenitor cells. Knockdown experiments confirmed that ETV6 and IKZF1 are transcriptional regulators not just of ERG, but also of a number of genes regulated by a densely interconnected network of seven transcription factors. At last, we show that ETV6 and IKZF1 expression levels are positively correlated with expression of a number of heptad genes in AML and high expression of all nine genes confers poorer overall prognosis.

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CellLineNavigator: a workbench for cancer cell line analysis

Cited by 19 publications

References 26 publications

MFG-E8: Origin, Structure, Expression, Functions and Regulation

MFG-E8: Origin, Structure, Expression, Functions and Regulation

Selection of Personalized Patient Therapy through the Use of Knowledge-Based Computational Models That Identify Tumor-Driving Signal Transduction Pathways

A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of anERG-driven transcriptional network

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