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GSE10139
Homo sapiens
106 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 66 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patients lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients.
Publication Title
A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 40 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patients lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients.
Publication Title
A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 16 Downloadable Samples
NextSeq 500
Description
Long intergenic non-coding RNAs (lincRNAs) are emerging as integral components of signaling pathways in various cancer types. In neuroblastoma, only a handful of lincRNAs are known as upstream regulators or downstream effectors of oncogenes. Here, we exploit RNA sequencing data of primary neuroblastoma tumors, neuroblast precursor cells, neuroblastoma cell lines and various cellular perturbation model systems to define the neuroblastoma lincRNome and map lincRNAs up- and downstream of neuroblastoma driver genes MYCN, ALK and PHOX2B. Each of these driver genes controls the expression of a particular subset of lincRNAs, several of which are associated with poor survival and are differentially expressed in neuroblastoma tumors compared to neuroblasts. By integrating RNA sequencing data from both primary tumor tissue and cancer cell lines, we demonstrate that several of these lincRNAs are expressed in stromal cells. Deconvolution of primary tumor gene expression data revealed a strong association between stromal cell composition and driver gene status, resulting in differential expression of these lincRNAs. We also explored lincRNAs that putatively act upstream of neuroblastoma driver genes, either as presumed modulators of driver gene activity, or as modulators of effectors regulating driver gene expression. This analysis revealed strong associations between the neuroblastoma lincRNAs MIAT and MEG3 and MYCN and PHOX2B activity or expression. Together, our results provide a comprehensive catalogue of the neuroblastoma lincRNome, highlighting lincRNAs up- and downstream of key neuroblastoma driver genes. This catalogue forms a solid basis for further functional validation of candidate neuroblastoma lincRNAs. Overall design: CLB-GA was transduced with control or inducible shPHOX2B. The cells were treated with doxycycline for 5 days.
Publication Title
Integrative analysis identifies lincRNAs up- and downstream of neuroblastoma driver genes.
Sample Metadata Fields
Cell line, Treatment, Subject
View Samples- Homo sapiens
- 57 Downloadable Samples
- NextSeq 500
Description
T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive type of blood cancer resulting from malignant transformation of T-cell precursors. Several oncogenes, including the 'T-cell leukemia homeobox 1' TLX1 (HOX11) transcription factor, have been identified as early driver events that cooperate with other genetic aberrations in leukemic transformation of progenitor T-cells. The TLX1 controlled transcriptome in T-ALL has been investigated extensively in the past in terms of protein-coding genes, but remains unexplored thus far at the level of long non-coding RNAs (lncRNAs), the latter renown as well-established versatile and key players implicated in various cancer hallmarks. In this study, we present the first extensive analysis of the TLX1 regulated transcriptome focusing on lncRNA expression patterns. We present an integrative analysis of polyA and total RNA sequencing of ALL-SIL lymphoblasts with perturbed TLX1 expression and a primary T-ALL patient cohort (including 5 TLX1+ and 12 TLX3+ cases). We expanded our initially presented dataset of TLX1 and H3K27ac ChIP data in ALL-SIL cells (Durinck et al., Leukemia, 2015) with H3K4me1, H3K4me3, and ATAC-seq data to accurately define (super-) enhancer marked lncRNAs and assigned potential functional annotations to candidate TLX1-controlled lncRNAs through an in silico guilt-by-association approach. Our study paves the way for further functional analysis of selected lncRNAs as potential novel therapeutic targets for a precision medicine approach in the context of T-ALL. Overall design: polyA+ RNA-seq data was generated for a primary T-ALL patient cohort
Publication Title
A comprehensive inventory of TLX1 controlled long non-coding RNAs in T-cell acute lymphoblastic leukemia through polyA+ and total RNA sequencing.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 9 Downloadable Samples
- NextSeq 500
Description
T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive type of blood cancer resulting from malignant transformation of T-cell precursors. Several oncogenes, including the 'T-cell leukemia homeobox 1' TLX1 (HOX11) transcription factor, have been identified as early driver events that cooperate with other genetic aberrations in leukemic transformation of progenitor T-cells. The TLX1 controlled transcriptome in T-ALL has been investigated extensively in the past in terms of protein-coding genes, but remains unexplored thus far at the level of long non-coding RNAs (lncRNAs), the latter renown as well-established versatile and key players implicated in various cancer hallmarks. In this study, we present the first extensive analysis of the TLX1 regulated transcriptome focusing on lncRNA expression patterns. We present an integrative analysis of polyA and total RNA sequencing of ALL-SIL lymphoblasts with perturbed TLX1 expression and a primary T-ALL patient cohort (including 5 TLX1+ and 12 TLX3+ cases). We expanded our initially presented dataset of TLX1 and H3K27ac ChIP data in ALL-SIL cells (Durinck et al., Leukemia, 2015) with H3K4me1, H3K4me3, and ATAC-seq data to accurately define (super-) enhancer marked lncRNAs and assigned potential functional annotations to candidate TLX1-controlled lncRNAs through an in silico guilt-by-association approach. Our study paves the way for further functional analysis of selected lncRNAs as potential novel therapeutic targets for a precision medicine approach in the context of T-ALL. Overall design: Total RNA-seq data was generated for the T-ALL cell line ALL-SIL upon TLX1 knockdown
Publication Title
A comprehensive inventory of TLX1 controlled long non-coding RNAs in T-cell acute lymphoblastic leukemia through polyA+ and total RNA sequencing.
Sample Metadata Fields
Cell line, Subject
View Samples- Homo sapiens
- 18 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
The MuvB complex sequentially recruits B-Myb and FoxM1 to promote mitotic gene expression.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 12 Downloadable Samples
- NextSeq500
Description
We sequenced mRNA from HCT116 p21-/- cells treated with Nutlin-3a, doxorubicin, or DMSO for 24 h. Overall design: Examination of mRNA levels from HCT116 p21-/- cells treated with Nutlin-3a, doxorubicin, or DMSO for 24 h using four replicates each.
Publication Title
Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Translation initiation factor eIF4E is overexpressed early in breast cancers in association with disease progression and reduced survival. Much remains to be understood regarding the role of eIF4E in human cancer. Using immortalized human breast epithelial cells, we report that elevated expression of elF4E translationally activates the TGF pathway, promoting cell invasion, loss of cell polarity, increased cell survival and other hallmarks of early neoplasia. Overexpression of eIF4E is shown to facilitate selective translation of integrin 1 mRNA, which drives the translationally controlled assembly of a TGF receptor signaling complex containing 31 integrins, -catenin, TGF receptor I, E-cadherin and phosphorylated Smads2/3. This receptor complex acutely sensitizes non-malignant breast epithelial cells to activation by typically sub-stimulatory levels of activated TGF. TGF can promote cellular differentiation or invasion and transformation. As a translational coactivator of TGF, eIF4E confers selective mRNA translation, reprogramming non-malignant cells to an invasive phenotype by reducing the set-point for stimulation by activated TGF. Overexpression of eIF4E may be a pro-invasive facilitator of TGF activity.
Publication Title
Eukaryotic Translation Initiation Factor 4E Is a Feed-Forward Translational Coactivator of Transforming Growth Factor β Early Protransforming Events in Breast Epithelial Cells.
Sample Metadata Fields
Sex, Specimen part, Cell line
View Samples- Homo sapiens
- 20 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
The data contained in this record are used to differentiate between the effects of IFN-a and IFN-b on 48h cultures of the ex vivo pbmcs of ATL patients, using Affymetrix microarrays (HuGene 1.0).
Publication Title
IFN-β induces greater antiproliferative and proapoptotic effects and increased p53 signaling compared with IFN-α in PBMCs of Adult T-cell Leukemia/Lymphoma patients.
Sample Metadata Fields
Specimen part, Subject
View Samples