scholarly journals Quantification of XRCC and DNA-PK proteins in cancer cell lines and human tumors by LC–MS/MS

Bioanalysis ◽  
2014 ◽  
Vol 6 (22) ◽  
pp. 2969-2983 ◽  
Author(s):  
Matthew V Myers ◽  
Stephen E Maxwell ◽  
Xiaomin Wang
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Tumors

HER-2 receptor expression, localization, and activation in colorectal cancer cell lines and human tumors

2003 ◽  
Vol 108 (4) ◽  
pp. 540-548 ◽  
Author(s):  
Elizabeth Half ◽  
Russell Broaddus ◽  
Kathleen D. Danenberg ◽  
Peter V. Danenberg ◽  
Gregory D. Ayers ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Tumors ◽  
Receptor Expression ◽  
Colorectal Cancer Cell ◽  
Colorectal Cancer Cell Lines ◽  
Her 2

scholarly journals Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

2019 ◽  
Author(s):  
Gabriela S. Kinker ◽  
Alissa C. Greenwald ◽  
Rotem Tal ◽  
Zhanna Orlova ◽  
Michael S. Cuoco ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Tumors ◽  
Model Systems ◽  
Cellular Heterogeneity ◽  
Specific Cell ◽  
Rna Seq ◽  
Mesenchymal Transition

AbstractCultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

scholarly journals SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiqun Zhang ◽  
Fengju Chen ◽  
Chad J. Creighton
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Integrative Analysis ◽  
Human Tumors ◽  
Gene Fusions ◽  
Expression Data ◽  
Altered Expression ◽  
Altered Gene

Abstract Background Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Results Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. Conclusion Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress. SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

scholarly journals Genomic and phenotypic profiles of two Brazilian breast cancer cell lines derived from primary human tumors

2013 ◽  
Vol 29 (4) ◽  
pp. 1299-1307 ◽  
Author(s):  
NATÁSSIA C.R. CORRÊA ◽  
HELLEN KUASNE ◽  
JERUSA A.Q.A. FARIA ◽  
CIÇA C.S. SEIXAS ◽  
IRIA G.D. SANTOS ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Tumors ◽  
Breast Cancer Cell Lines

797: Noggin Blocks the Osteoinductive Properties of Human Prostate Cancer Cell Lines

2006 ◽  
Vol 175 (4S) ◽  
pp. 258-258
Author(s):  
Ruth Schwaninger ◽  
Cyrill A. Rentsch ◽  
Antoinette Wetterwald ◽  
Irena Klima ◽  
Gabri Van der Pluijm ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Prostate Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Prostate ◽  
Human Prostate Cancer ◽  
Human Prostate Cancer Cell ◽  
Prostate Cancer Cell Lines
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