Patient derived clear cell renal cell carcinoma models.
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB32502 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3319880.fastq.gz | Fastqsanger.gz | |||
| ERR3319890.fastq.gz | Fastqsanger.gz | |||
| ERR3319900.fastq.gz | Fastqsanger.gz | |||
| ERR3319881.fastq.gz | Fastqsanger.gz | |||
| ERR3319891.fastq.gz | Fastqsanger.gz |
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Project description:Analysis of 2D (transwell) and 3D (collagen type I) cultured MDCK cells and HGF (a MAPK activator). Traditional 2D cultures are fast and inexpensive but do no mimic natural niche/cell environment as well as the more laborious and costly 3D-cultures. 3D cultures, arguably, are better models for the study of developmental processes, such as tubulogenesis. Epithelial organs (such as kidney) develop via tubulogenesis, a process, at least in part, regulated by MAPK signaling. Therefore, 2D and 3D cells also treated with HGF plus MAPK inhibitors. Results provide insights into differential response to HGF-induced tubulogenesis depending on cell culture conditions (2D vs. 3D). 29 samples total: 2D and 3D control (untreated) in quadruplicate, respectively; 2D and 3D + HGF in quadruplicate, respectively; 2D + HGF + PD-98059 in quadruplicate; 3D + HGF + PD-98059 in triplicate; 2D + HGF + U0126 in triplicate; and 3D + HGF + U0126 in triplicate.
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Project description:The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional (2D) monolayers on plastic. However, many cellular features are impaired in these unnatural conditions and big alterations in gene expression in comparison to tumors have been reported. Three-dimensional (3D) cell culture models have become increasingly popular and are suggested to be better models than 2D monolayers due to improved cell-to-cell contacts and structures that resemble in vivo architecture. The aim of this study was to compare gene expression patterns of MCF7 breast cancer cells when grown as xenografts, in 2D, in polyHEMA coated anchorage independent 3D models and in Matrigel on-top 3D cell culture models. Surprisingly small variations in gene expression patterns were observed between the models indicating that 3D and xenograft are not always that different from 2D cell cultures. Gene expression analysis of MCF7 breast cancer cells cultured as xenografts for 43 days, in two dimensional cultures for seven days (2D7d), in polyHEMA three dimensional cell culture models for four and seven days (PH7d and PH7d), and in Matrigel three dimensional cultures for four and seven days (MG4d and MG7d). Two biological replicates was included for each sample.
2013-10-30 | E-GEOD-47583 | biostudies-arrayexpress
Project description:Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer associated with the development of abnormal tumor angiogenesis. Although multiple anti-angiogenic therapies have been developed, responses to individual treatment are highly variable between patients. Thus, the use of one-patient clinical trials has been suggested as an alternative to standard trials. We used a microfluidic device to generate organotypic primary patient-specific blood vessel models using normal (NEnC) and tumor-associated primary CD31+ selected cells (TEnC). Our model was able to recapitulate differences in angiogenic sprouting and vessel permeability that characterize normal and tumor-associated vessels. We analyzed the expression profile of vessel models to define vascular normalization in a patient-specific manner. Using this data, we identified actionable targets to normalize TEnC vessel function to a more NEnC-like phenotype. Finally, we tested two of these drugs in our patient-specific models to determine the efficiency in restoring vessel function showing the potential of the model for single-patient clinical trials.
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Project description:The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional (2D) monolayers on plastic. However, many cellular features are impaired in these unnatural conditions and big alterations in gene expression in comparison to tumors have been reported. Three-dimensional (3D) cell culture models have become increasingly popular and are suggested to be better models than 2D monolayers due to improved cell-to-cell contacts and structures that resemble in vivo architecture. The aim of this study was to develop a simple high-throughput 3D drug screening method and to compare drug responses in JIMT1 breast cancer cells when grown in 2D, in polyHEMA coated anchorage independent 3D models and in Matrigel on-top 3D cell culture models. We screened 102 compounds with multiple concentrations and biological replicates for their effects on cell proliferation. The cells were either treated immediately upon plating or they were allowed to grow in 3D for four days prior to the drug treatment. Big variations in drug responses were observed between the models indicating that comparisons of culture model influenced drug sensitivities cannot be made based on effects of a single drug. However, we show with the 63 most prominent drugs that, in general, JIMT1 cells grown on Matrigel were significantly more sensitive to drugs than cells grown in 2D cultures, while responses of cells grown in polyHEMA resembled those of 2D. Furthermore, comparison of gene expression profiles of the cell culture models to xenograft tumors indicated that cells cultured in Matrigel and as xenografts most closely resembled each other. In this study we also suggest that 3D cultures can provide a platform for systematic experimentation of larger compound collections in a high-throughput mode and be used as alternatives for traditional 2D screens towards better comparability to in vivo state. Gene expression analysis of JIMT1 breast cancer cells cultured as xenografts for 43 days, in two dimensional cultures for seven days (2D7d), in polyHEMA three dimensional cell culture models for four and seven days (PH7d and PH7d), and in Matrigel three dimensional cultures for four and seven days (MG4d and MG7d). Two biological replicates was included for each sample.
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Project description:Patient-derived xenografts (PDX) are high-fidelity cancer models typically credentialled by genomics, transcriptomics and proteomics. Characterization of metabolic reprogramming, a hallmark of cancer, is less frequent. Dysregulated metabolism is a key feature of clear cell renal cell carcinoma (ccRCC) and authentic preclinical models are needed to evaluate novel imaging and therapeutic approaches targeting metabolism. We characterized 5 PDX from high-grade or metastatic ccRCC by multiparametric magnetic resonance imaging (MRI) and steady state metabolic profiling and flux analysis. Similar to MRI of clinical ccRCC, T2-weighted images of orthotopic tumors of most PDX were homogeneous. The increased hyperintense (cystic) areas observed in one PDX mimicked the cystic phenotype typical of some RCC. The negligible hypointense (necrotic) areas of PDX grown under the highly vascularized renal capsule are beneficial for preclinical studies. Mean apparent diffusion coefficient (ADC) values were equivalent to those of ccRCC in human patients. Hyperpolarized (HP) [1-13C]pyruvate MRI of PDX showed high glycolytic activity typical of high-grade primary and metastatic ccRCC with considerable intra- and inter-tumoral variability, as has been observed in clinical HP MRI of ccRCC. Comparison of steady state metabolite concentrations and metabolic flux in [U-13C]glucose-labeled tumors highlighted the distinctive phenotypes of two PDX with elevated levels of numerous metabolites and increased fractional enrichment of lactate and/or glutamate, capturing the metabolic heterogeneity of glycolysis and the TCA cycle in clinical ccRCC. Culturing PDX cells and reimplanting to generate xenografts (XEN), or passaging PDX in vivo, altered some imaging and metabolic characteristics while transcription remained like that of the original PDX. These findings show that PDX are realistic models of ccRCC for imaging and metabolic studies but that the plasticity of metabolism must be considered when manipulating PDX for preclinical studies.
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