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Mus musculus
12 Downloadable Samples
Affymetrix Murine Genome U74A Version 2 Array (mgu74av2)
Description
Screening for mouse cDNA that was highly expressed in positive-selector H-2b AND-TCR-transgenic thymocytes using Affymetrix Murine Genome arrays.
Publication Title
IAN family critically regulates survival and development of T lymphocytes.
Sample Metadata Fields
No sample metadata fields
View Samples- Drosophila melanogaster
- 2 Downloadable Samples
- Affymetrix Drosophila Genome Array (drosgenome1)
Description
Drosophila tdf, another name apontic (apt), encodes a bZIP transcription factor that is required for the development of trachea, heart, head and neural system. However, little is known about the target of TDF/Apt.
Publication Title
Evolutionarily conserved transcription factor Apontic controls the G1/S progression by inducing cyclin E during eye development.
Sample Metadata Fields
No sample metadata fields
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GSE64352- Arabidopsis thaliana
- 6 Downloadable Samples
- Affymetrix Arabidopsis ATH1 Genome Array (ath1121501)
Description
This work studies the impact of AtNIGT1/HRS1-GR entrance in the nucleus upon DEX treatment in protoplasts.
Publication Title
AtNIGT1/HRS1 integrates nitrate and phosphate signals at the Arabidopsis root tip.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 33 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Currently there is no method available to predict response to farnesyltransferase inhibitors (FTI). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib, in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1:APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy. This two-gene ratio was validated by quantitative PCR (QPCR) in the newly diagnosed AML cohort. We further demonstrated that this classifier could predict response to tipifarnib in an independent set of 54 samples from relapsed or refractory AML, with a negative predictive value (NPV) and positive predictive value (PPV) of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days, p = 0.0001), which was shown to be independent of other prognostic factors including a previously described gene expression classifier predictive of overall survival. Therefore, these data indicate that a two-gene expression assay may have utility in categorizing a population of AML patients who are more likely to respond to tipifarnib.
Publication Title
A 2-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia.
Sample Metadata Fields
Sex, Age, Disease
View Samples- Homo sapiens
- 177 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Primitive neuroectodermal tumors of the central nervous system (CNS PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children. Using DNA methylation and gene expression profiling we have demonstrated that a significant proportion of institutionally diagnosed CNS PNETs display molecular profiles indistinguishable from those of various other well defined CNS tumor entities, facilitating diagnosis and appropiate therapy for children with these tumors. From the remaining fraction of CNS PNETs, we have identified four distinct new CNS tumor entities extending to other neuroepithelial tumors, each associated with a recurrent genetic alteration and particular histopathological and clinical features. These molecular entities, designated CNS Neuroblastoma with FOXR2 activation (CNS NB FOXR2), CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT CIC), CNS high grade neuroepithelial tumor with MN1 alteration (CNS HGNET MN1), and CNS high grade neuroepithelial tumor with BCOR alteration (CNS HGNET BCOR), will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by these poorly differentiated CNS tumors.
Publication Title
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.
Sample Metadata Fields
Sex, Age
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