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accession-icon GSE51931
Pancreatic cancer-induced cachexia syndrome
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Cancer cachexia syndrome is observed in 80% of patients with advanced-stage cancer, and it is one of the most frequent causes of death. Severe wasting accounts for more than 80% in patients with advanced pancreatic cancer. Here we wanted to define, by using an microarray approach and the Pdx1-cre;LSL-KrasG12D;INK4a/arffl/fl, the pathways involved in muscle, liver and white adipose tissue wasting.

Publication Title

Pancreatic cancer-induced cachexia is Jak2-dependent in mice.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage

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accession-icon GSE55513
Transcriptome Analysis Predicts Clinical Outcome and Sensitivity to Anticancer Drugs of patients with a Pancreatic Adenocarcinoma
  • organism-icon Homo sapiens
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

A major impediment to the effective treatment of patients with PDAC (Pancreatic Ductal Adenocarcinoma) is the molecular heterogeneity of the disease, which is reflected in an equally diverse pattern of clinical responses to therapy. We developed an efficient strategy in which PDAC samples from 17 consecutively patients were obtained by EUS-FNA or surgery, their cells maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. For these patients a clinical follow up was obtained. On the breathing tumors we studied the RNA expression profile by an Affymetrix approach. We observed a significant heterogeneity in their RNA expression profile, however, the transcriptome was able to discriminate patients with long- or short-time survival which correspond to moderately- or poorly-differentiated PDAC tumors respectively. Cells allowed us the possibility to analyze their relative sensitivity to several anticancer drugs in vitro by developing a chimiogram, like an antibiogram for microorganisms, with several anticancer drugs for obtaining an individual profile of drug sensitivity and as expected, the response was patient-dependent. Interestingly, using this approach, we also found that the transcriptome analysis could predict the sensitivity to some anticancer drugs of patients with a PDAC. In conclusion, using this approach, we found that the transcriptome analysis could predict the sensitivity to some anticancer drugs and the clinical outcome of patients with a PDAC.

Publication Title

Transcriptomic analysis predicts survival and sensitivity to anticancer drugs of patients with a pancreatic adenocarcinoma.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE89792
Gene Expression Profiling of Patient-Derived Pancreatic Cancer Xenografts predicts sensitivity to the BET bromodomain inhibitor JQ1: Implications to individualized medicine efforts
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

c-Myc controls more than 15% of genes responsible for proliferation, differentiation, and cellular metabolism in pancreatic as well as other cancers making this transcription factor a prime target for treating patients. The transcriptome of 55 patient derived xenografts show that 30% of them share an exacerbated expression profile of MYC transcriptional targets (MYC-high). This cohort is characterized by a high level of Ki67 staining, a lower differentiation state and a shorter survival time compared to the MYC-low subgroup. To define classifier expression signature, we selected a group of 10 MYC targets transcripts which expression is increased in the MYC-high group and 6 transcripts increased in the MYC-low group. We validated the ability of these markers panel to identify MYC-high patient-derived xenografts from both: discovery and validation cohorts as well as primary cells cultures from the same patients. We then showed that cells from MYC-high patients are more sensitive to JQ1 treatment compared to MYC-low cells, in both monolayer and 3D cultured spheroids, due to cell cycle arrest followed by apoptosis. Therefore, these results provide new markers and potentially novel therapeutic modalities for distinct subgroups of pancreatic tumors and may find application to the future management of these patients within the setting of individualized medicine clinics.

Publication Title

Gene expression profiling of patient-derived pancreatic cancer xenografts predicts sensitivity to the BET bromodomain inhibitor JQ1: implications for individualized medicine efforts.

Sample Metadata Fields

Disease

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accession-icon GSE26212
The effects of EBV transformation on gene expression and methylation levels
  • organism-icon Homo sapiens
  • sample-icon 82 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

The effects of EBV transformation on gene expression levels and methylation profiles.

Sample Metadata Fields

Sex, Specimen part, Subject

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accession-icon GSE58942
The effect of freeze-thaw cycles on gene expression levels in lymphoblastoid cell lines
  • organism-icon Homo sapiens
  • sample-icon 187 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) are a widely used renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. However, the extent to which LCLs are a faithful model system is relatively unknown. We have previously shown that gene expression profiles of newly established LCLs maintain a strong individual component. Here, we extend our study to investigate the effect of freeze-thaw cycles on gene expression patterns in mature LCLs, especially in the context of inter-individual variation in gene regulation. We found a profound difference in the gene expression profiles of newly established and mature LCLs. Once newly established LCLs undergo a freeze-thaw cycle, the individual specific gene expression signatures become much less pronounced as the gene regulatory programs in LCLs from different individuals converge to a more uniform profile, which reflects a mature transformed B cell phenotype. As expected, previously identified eQTLs are enriched among the relatively few genes whose regulations in mature LCLs maintain marked individual signatures. We thus conclude that findings and insight drawn from gene regulatory studies in mature LCLs are generally not affected by artificial nature of the LCL model system and are likely to faithfully reflect regulatory interactions in primary tissues. However, our data indicate that many aspects of primary B cell biology cannot be observed and studied in mature LCL cultures.

Publication Title

The effect of freeze-thaw cycles on gene expression levels in lymphoblastoid cell lines.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE26210
The effects of EBV transformation on gene expression
  • organism-icon Homo sapiens
  • sample-icon 82 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) provide a conveniently accessible and renewable resource for functional studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. A lingering concern, however, is that the changes associated with EBV transformation of LCLs reduce the usefulness of LCLs as a surrogate model for primary tissues. To evaluate the validity of this concern, we compared global gene expression profiles between CD20+ primary B cells and CD3+ primary T cells sampled from six individuals. Six independent replicates of transformed LCLs were derived from each sample.

Publication Title

The effects of EBV transformation on gene expression levels and methylation profiles.

Sample Metadata Fields

Sex, Specimen part, Subject

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accession-icon GSE21829
Differential gene expression in adrenal medulla after cardiac pressure overload
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Transcriptom analysis of microdissect adrenal medulla after 8 weeks of cardiac pressure overload caused by transverse aortic constriction.

Publication Title

Chronic cardiac pressure overload induces adrenal medulla hypertrophy and increased catecholamine synthesis.

Sample Metadata Fields

Sex

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accession-icon SRP041955
Homo sapiens Transcriptome or Gene expression
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be normalized, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples are at times the sole means of addressing specific questions – e.g., samples collected in the course of fieldwork.

Publication Title

RNA-seq: impact of RNA degradation on transcript quantification.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP097631
Sub-populations in the mammary repopulating units
  • organism-icon Mus musculus
  • sample-icon 20 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Elucidating the top of the mammary epithelial cell hierarchy is highly important for understanding its regeneration capabilities and identifying target cells for transformation. Aiming for enriched mammary epithelial stem cell population, CD200highCD200R1high epithelial cells were identified. These cells represent ~50% of the mammary repopulating units (MRUs, CD49fhigh CD24med ) and termed MRUCD200/CD200R1. Gene expression of these cells was compared to all other MRU cells, termed MRUnot CD200/CD200R1, as well as individual CD200+ population (MRU-CD200R1-) and CD200R1+ population (MRU-CD200-). Overall design: Gene expression from mammary epithelial cells carrying sorted by CD200, CD200R1 markers and MRU markers. Four populations were sequenced: MRU-positive CD200 positive and CD200R1 positive; MRU-positive and not CD200 positive CD200R1 positive; not MRU CD200 positive CD200R1 negative; not MRU CD200 negative CD200R1 positive. There are 5 replicates from 5 individual mice.

Publication Title

High Expression of CD200 and CD200R1 Distinguishes Stem and Progenitor Cell Populations within Mammary Repopulating Units.

Sample Metadata Fields

Sex, Specimen part, Cell line, Subject

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accession-icon GSE11045
Expression data from kidney and liver
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

mRNA expression differences between the liver and kidney of an adult male (homo sapien) were investigated using three technical replicates.

Publication Title

RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays.

Sample Metadata Fields

No sample metadata fields

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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