Genentech - Cell line exome sequencing
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001002554 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The study aimed to comprehensively characterize human myoblastic cell line RCMH using using electron microscopic and proteomic approaches. Myoblastic cell lines can be useful to investigate the complex biochemical changes occuring under different conditions that reflect the physiological and pathophysiological mechanisms of muscle. So far, there are no suitable in vitro models of human muscle origin to study a variety of muscle related processes including responses to mechanical stress, EC-coupling and (ER-associated) myopathic disorders. Therefore, we characterized the human immortal myoblastic cell line RCMH and the results suggest RCMH as a suitable in vitro model for investigating human muscle related processes and disorders.
2016-07-20 | MSV000079948 | MassIVE
Project description:The study aimed to comprehensively characterize human myoblastic cell line RCMH using using electron microscopic and proteomic approaches. Myoblastic cell lines can be useful to investigate the complex biochemical changes occuring under different conditions that reflect the physiological and pathophysiological mechanisms of muscle. So far, there are no suitable in vitro models of human muscle origin to study a variety of muscle related processes including responses to mechanical stress, EC-coupling and (ER-associated) myopathic disorders. Therefore, we characterized the human immortal myoblastic cell line RCMH and the results suggest RCMH as a suitable in vitro model for investigating human muscle related processes and disorders.
2016-01-26 | PXD001974 | Pride
Project description:Hass2017-PanRTK model for single cell
line
The model structure comprises
heterodimerization and receptor trafficking as described in detail
in the article below. For ligand input, set a respective
event. The illustrated event sets the EGF concentration to 2.5 nMol
in the model file.
This model is described in the article:
Predicting ligand-dependent
tumors from multi-dimensional signaling features.
Hass H, Masson K, Wohlgemuth S,
Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J,
MacBeath G, Schoeberl B, Raue A.
NPJ Syst Biol Appl 2017; 3: 27
Abstract:
Targeted therapies have shown significant patient benefit in
about 5-10% of solid tumors that are addicted to a single
oncogene. Here, we explore the idea of ligand addiction as a
driver of tumor growth. High ligand levels in tumors have been
shown to be associated with impaired patient survival, but
targeted therapies have not yet shown great benefit in
unselected patient populations. Using an approach of applying
Bagged Decision Trees (BDT) to high-dimensional signaling
features derived from a computational model, we can predict
ligand dependent proliferation across a set of 58 cell lines.
This mechanistic, multi-pathway model that features receptor
heterodimerization, was trained on seven cancer cell lines and
can predict signaling across two independent cell lines by
adjusting only the receptor expression levels for each cell
line. Interestingly, for patient samples the predicted tumor
growth response correlates with high growth factor expression
in the tumor microenvironment, which argues for a co-evolution
of both factors in vivo.
This model is hosted on
BioModels Database
and identified by:
MODEL1708210000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1708210000 | BioModels
Project description:Genomic characterisation of a large series of cancer cell lines.
| EGAS00001000978 | EGA
Project description:SNP array data for gastric cancer cell lines
| EGAD00010000622 | EGA
Project description:NCI-60 cancer cell lines were profiled with their genome-wide gene expression patterns using Affymetrix HG-U133A chips. Keywords: NCI-60 cancer cell line expression profiling
2009-08-26 | GSE5846 | GEO
Project description:HT-29 cells were barcoded using the CloneTracker lentiviral barcode library and then dabrafenib resistant derivatives of these cell lines were established, respectively. Five million barcoded HT-29 cells were seeded into 15 cm cell culture dishes. When the cells reached confluency, two million cells per dish were seeded into four different 15 cm dishes (DMSO Control, Replica A, B, C) and two million cell pellets were stocked as initial cell population. Harvesting used medium through the experiment was performed at monthly intervals. Barcoded HT-29 cell line replicates A, B, and C were treated with 2XIC50 (199.6 nM) of dabrafenib concentration for the duration of 3 months.Barcoded data can be accessed via accession code E-MTAB-13018. Whole exome sequencing of dabrafenib-resistant A replicate and DMSO control cell lines were carried out.
2023-09-06 | E-MTAB-13338 | biostudies-arrayexpress
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
2016-12-01 | MSV000080374 | GNPS
Project description:GTO sequencing of Human Cell Lines [cell line GTO-seq]
| PRJNA528898 | ENA
Project description:Pre-clinical model to study tumor immune resistance are essential. To this end, we generated cell lines from tumors induced on BrafV600E Pten-/- mice. The cell lines have been characterized to evaluate the tumor growth in vivo and response to immunotherapies. In addition, we have also chaacterised the DNA expression profile of the cell lines.
2023-07-30 | GSE209729 | GEO