Gene expression microarray and CGH array of metabolically adaptable cells derived from SUM149 triple-negative Inflammatory Breast Cancer cell line
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE60017 | GEO | 2014/10/03
SECONDARY ACCESSION(S): PRJNA257280
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
| S-ECPF-GEOD-60017 | biostudies-other
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-10-03 | E-GEOD-60017 | biostudies-arrayexpress
Project description:CGH array of MA1 and MA2 variant cells as compared to the parental SUM149-Luc breast cancer cell line. The MA1 and MA2 variants were isolated based on the ability of rare cancer cells to survive and grow without adding glutamine in culture medium. To gain insight into the characteristic of metabolically adaptable MA cells that enables them to survive severe metabolic challenge, i.e., prolonged lack of glutamine and other challenges [Singh et al., PLoS ONE 7: e36510, 2012], we analyzed CGH array to compare these cells with the parental SUM149-Luc (luciferase-transfected) cells. We analyzed two independently selected cell populations, one from 0.5 million parental cells (designated MA1) and one from 1 million parental cells (designated MA2).
2014-10-03 | GSE60015 | GEO
Project description:CGH array of MA1 and MA2 variant cells as compared to the parental SUM149-Luc breast cancer cell line. The MA1 and MA2 variants were isolated based on the ability of rare cancer cells to survive and grow without adding glutamine in culture medium. To gain insight into the characteristic of metabolically adaptable MA cells that enables them to survive severe metabolic challenge, i.e., prolonged lack of glutamine and other challenges [Singh et al., PLoS ONE 7: e36510, 2012], we analyzed CGH array to compare these cells with the parental SUM149-Luc (luciferase-transfected) cells. We analyzed two independently selected cell populations, one from 0.5 million parental cells (designated MA1) and one from 1 million parental cells (designated MA2). Comparing the MA1 and MA2 variants to the common parental SUM149-Luc cell line. One sample each.
2014-10-03 | E-GEOD-60015 | biostudies-arrayexpress
Project description:To gain insight into the characteristic of metabolically adaptable MA cells that enables them to survive severe metabolic challenge, i.e., prolonged lack of glutamine and other challenges [Singh et al., PLoS ONE 7: e36510, 2012], we used gene expression microarrays to compare these cells with the parental SUM149-Luc (luciferase-transfected) cells. We analyzed two independently selected cell populations, one from 0.5 million parental cells (designated MA1) and one from 1 million parental cells (designated MA2). Comparing MA1 and MA2 variants to a common parental cell line SUM149-Luc. One sample each.
| S-ECPF-GEOD-60016 | biostudies-other
Project description:To gain insight into the characteristic of metabolically adaptable MA cells that enables them to survive severe metabolic challenge, i.e., prolonged lack of glutamine and other challenges [Singh et al., PLoS ONE 7: e36510, 2012], we used gene expression microarrays to compare these cells with the parental SUM149-Luc (luciferase-transfected) cells. We analyzed two independently selected cell populations, one from 0.5 million parental cells (designated MA1) and one from 1 million parental cells (designated MA2). Comparing MA1 and MA2 variants to a common parental cell line SUM149-Luc. One sample each.
2014-10-03 | E-GEOD-60016 | biostudies-arrayexpress
Project description:To gain insight into the characteristic of metabolically adaptable MA cells that enables them to survive severe metabolic challenge, i.e., prolonged lack of glutamine and other challenges [Singh et al., PLoS ONE 7: e36510, 2012], we used gene expression microarrays to compare these cells with the parental SUM149-Luc (luciferase-transfected) cells. We analyzed two independently selected cell populations, one from 0.5 million parental cells (designated MA1) and one from 1 million parental cells (designated MA2).
2014-10-03 | GSE60016 | GEO
Project description:BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.
| S-EPMC3475006 | biostudies-literature
Project description:Neuroblastoma has a very diverse clinical behaviour: from spontaneous regression to a very aggressive malignant progression and resistance to chemotherapy. This heterogeneous clinical behaviour might be due to the existence of Cancer Stem Cells (CSC), a subpopulation within the tumor with stem-like cell properties: a significant proliferation capacity, a unique self-renewal capacity, and therefore, a higher ability to form new tumors. We enriched the CSC-like cell population content of two commercial neuroblastoma cell lines by the use of conditioned cell culture media for neurospheres, and compared genomic gains and losses and genome expression by array-CGH and microarray analysis, respectively (in CSC-like versus standard tumor cells culture). Despite the array-CGH did not show significant differences between standard and CSC-like in both analyzed cell lines, the microarray expression analysis highlighted some of the most relevant biological processes and molecular functions that might be responsible for the CSC-like phenotype. Some signalling pathways detected seem to be involved in self-renewal of normal tissues (Wnt, Notch, Hh and TGF-β) and contribute to CSC phenotype. We focused on the aberrant activation of TGF-β and Hh signalling pathways, confirming the inhibition of repressors of TGF-β pathway, as SMAD6 and SMAD7 by RT-qPCR. The analysis of the Sonic Hedgehog pathway showed overexpression of PTCH1, GLI1 and SMO. We found overexpression of CD133 and CD15 in SIMA neurospheres, confirming that this cell line was particularly enriched in stem-like cells. This work shows a cross-talk among different pathways in neuroblastoma and its importance in CSC-like cells.
| S-EPMC4231109 | biostudies-literature
Project description:BackgroundArray-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.ResultsWe show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays).ConclusionWe have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.
| S-EPMC1523216 | biostudies-literature