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GSE80447
Homo sapiens
4 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Total RNA was isolated from proliferating and senescent IMR90 cells to compare gene-expression to the changes in nucleolus-association in proliferating and senescent IMR90 cells.
Publication Title
Nucleolus association of chromosomal domains is largely maintained in cellular senescence despite massive nuclear reorganisation.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 271 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background: Germinal center B-cell (GCB) lymphomas are common in children and adults. The prognosis strongly depends on age. Subgroups of GCB-lymphomas are characterized by chromosomal translocations affecting immunoglobulin (IG) loci leading to oncogene deregulation.
Publication Title
Translocations activating IRF4 identify a subtype of germinal center-derived B-cell lymphoma affecting predominantly children and young adults.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 127 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data.
Sample Metadata Fields
Specimen part, Cell line, Treatment, Time
View Samples- Homo sapiens
- 133 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Molecular classification of mature aggressive B-cell lymphoma using digital multiplexed gene expression on formalin-fixed paraffin-embedded biopsy specimens.
Sample Metadata Fields
Sex, Age, Specimen part, Disease
View Samples- Homo sapiens
- 74 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.
Publication Title
Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Homo sapiens
- 84 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Since follicular lymphoma (FL) grade 3A often coexist with a FL1/2 component a linear progression model of FL1, FL2 and FL3A has been developed. FL3B, on the other hand, is supposed to be more closely related to diffuse large B-cell lymphoma (DLBCL) and both FL3B and DLBCL are often simultaneously present in one tumor (DLBCL/FL3B).
Publication Title
Gene expression profiling reveals a close relationship between follicular lymphoma grade 3A and 3B, but distinct profiles of follicular lymphoma grade 1 and 2.
Sample Metadata Fields
Sex, Age, Specimen part
View Samples- Homo sapiens
- 60 Downloadable Samples
- Affymetrix Human Gene 1.1 ST Array (hugene11st)
Description
High-grade gliomas are amongst the most deadly human tumors. Treatment results are overall disappointing. Nevertheless, in several trials around 20% of patients respond to therapy. Diagnostic strategies to identify those patients that will ultimately profit from a specific targeted therapy are urgently needed. Gene expression profiling of untreated tumors is a well established approach for identifying biomarkers or diagnostic signatures. However, reliable signatures predicting treatment response in gliomas do not exist. Here we suggest a novel strategy for developing diagnostic signatures. We postulate that predictive gene expression patterns emerge only after tumor cells have been treated with the agent in vitro. Moreover, we postulate that enriching specimens for tumor initiating cells sharpens predictive expression patterns. Here, we report on the prediction of treatment response of cancer cells in vitro. As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated but not from untreated glioma cells allowed to predict therapy-induced impairment of proliferation of glioma cells in vitro. Prediction can be achieved with as little as 6 genes allowing for a straightforward translation into the clinic once the predictive power of the signature is shown also in vivo. Our strategy of using expression profiles from in vitro treated BTIC-enriched cultures opens new ways for trial design for patients with malignant gliomas.
Publication Title
Response-predictive gene expression profiling of glioma progenitor cells in vitro.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 43 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The most frequent mature aggressive B-cell lymphomas are diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Patients suffering from molecularly defined BL (mBL) but treated with a regimen developed for DLBCL show an unfavorable outcome compared to mBL treated with chemotherapy regimens for BL. Distinguishing BL from DLBCL by conventional histopathology is challenging in lymphomas that have features common to both diseases (aggressive B-cell lymphoma unclassifiable with features of DLBCL and BL [intermediates]). Moreover, DLBCL are a heterogeneous group of lymphomas comprising distinct molecular subtypes: the activated B-cell (ABC)-like, the germinal center B-cell-like (GCB) and the unclassifyable subtype as defined by gene expression profiling (GEP). Attempts to replace GEP with techniques applicable to formalin-fixed paraffin-embedded (FFPE) tissue led to algorithms for immunohistochemical stainings (IHS). Disappointingly, the algorithms yielded conflicting results with respect to their prognostic potential, raising concerns about their validity. Furthermore, IHS algorithms did not provide a fully resolved classification: They did not identify mBL; nor did they separate ABC from unclassified DLBCL.
Publication Title
Molecular classification of mature aggressive B-cell lymphoma using digital multiplexed gene expression on formalin-fixed paraffin-embedded biopsy specimens.
Sample Metadata Fields
Sex, Age, Specimen part
View Samples- Homo sapiens
- 39 Downloadable Samples
- Affymetrix Human Gene 1.1 ST Array (hugene11st)
Description
Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.
Publication Title
No associated publication
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 36 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The spectrum of entities, the therapeutic strategy and the outcome of mature aggressive B-cell lymphomas (maB-NHL) differs between children and adolescents on the one hand and adult patients on the other. Whereas adult maB-NHL have been studied in detail, data on molecular profiling of pediatric maB-NHL are hitherto lacking. Our aim was to characterize pediatric maB-NHL on the molecular level and to evaluate whether a molecular diagnosis of pediatric maB-NHL reveals clinically relevant groups.
Publication Title
Molecular profiling of pediatric mature B-cell lymphoma treated in population-based prospective clinical trials.
Sample Metadata Fields
Age
View Samples