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accession-icon SRP041584
The role of HIF-1 in beta-glucan induced response in myeloid cell
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

beta-glucan induced glycolysis in HIF-1 depedent manner. We reported that beta-glucan injection in mice led to upregulated glycolysis. HIF-1a plays a major role in this process. Overall design: Mice receives beta-glucan via ip for 4 days. Splenocytes were isolated for RNA sequencing.

Publication Title

mTOR- and HIF-1α-mediated aerobic glycolysis as metabolic basis for trained immunity.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE34714
Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples. (test samples)
  • organism-icon Homo sapiens
  • sample-icon 117 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.

Publication Title

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples.

Sample Metadata Fields

Time

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accession-icon GSE34823
Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples
  • organism-icon Homo sapiens
  • sample-icon 27 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples.

Sample Metadata Fields

Specimen part, Time

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accession-icon GSE65496
The TCR activation acts as a tumor suppressor mechanism in T-ALL
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Developmental checkpoints in stem/progenitor cells are critical to the determination, commitment and differentiation into distinct lineages. Cancer cells often retain expression of lineage-specific checkpoint proteins, but their potential impact in cancer remains elusive. T lymphocytes mature in the thymus following a highly orchestrated developmental process that entails the successive rearrangements and expression of T-cell receptor (TCR) genes. Low affinity recognition of self-peptide/MHC complexes (self-pMHC) presented by thymic epithelial cells by the TCR of CD4+CD8+ (DP) cortical thymocytes transduces positive selection signals that ultimately shape the developing T cell repertoire. DP thymocytes not receiving these signals die by lack of stimulation whereas those that recognize self-pMHC with high affinity undergo TCR-mediated apoptosis and negative selection. In T-cell acute lymphoblastic leukaemia (T-ALL), leukaemic transformation of maturating thymocytes results from the acquisition of multiple genetic and epigenetic alterations in oncogenes and tumour suppressor genes, that disrupt the normal regulatory circuits and drive clonal expansion of differentiation-arrested lymphoblasts. We show here that TCR triggering by negatively-selecting self-pMHC prevented T-ALL development and leukaemia maintenance in mice. Induction of TCR signalling by high affinity self-pMHC or treatment with monoclonal antibodies to the CD3 signalling chain (anti-CD3) caused massive leukaemic cell death and a gene expression program resembling that of thymocyte negative selection. Importantly, anti-CD3 treatment hampered leukaemogenesis in mice transplanted with either mouse or patient-derived T-ALLs. These data provide a rationale for targeted therapy based on anti-CD3 treatment of T-ALL patients and demonstrate that endogenous developmental checkpoint proteins are amenable to therapeutic intervention in cancer cells.

Publication Title

Triggering the TCR Developmental Checkpoint Activates a Therapeutically Targetable Tumor Suppressive Pathway in T-cell Leukemia.

Sample Metadata Fields

Cell line

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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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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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