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Deregulation of Transcription Factors GATA-1, GATA-2 and C/EBPa in Acute Promyelocytic Leukemia.

2008· article· en· W2579778415 on OpenAlexaff
Mahadeo A. Sukhai, Mariam Thomas, Rashmi S. Goswami, Yali Xuan, Patrícia P. Reis, Suzanne Kamel‐Reid

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsAcute promyelocytic leukemiaBiologyMyeloidCancer researchHaematopoiesisRetinoic acidLeukemiaTranscription factorMyelopoiesisGATA1Myeloid leukemiaTransgeneCell biologyMolecular biologyImmunologyGeneticsGeneStem cell

Abstract

fetched live from OpenAlex

Abstract Acute promyelocytic leukemia (APL) accounts for ~10% of all acute myelogenous leukemia (AML) cases, and is characterized by accumulation of abnormal promyelocytes in the bone marrow. APL is uniquely associated with balanced chromosomal translocations involving the retinoic acid receptor alpha (RARA) locus. A functional chimeric protein, X-RARA, is created, whose N-terminus is derived from the partner (“X”) gene, and retains its oligomerization domain. The C-terminus is derived from RARA. X-RARA contributes to the APL phenotype by interfering with granulocyte differentiation, through acquisition of novel transactivating activity and/or interference with the normal functions of RARA and X. Several studies have indicated that defective retinoid signaling, though sufficient to block myeloid differentiation, cannot induce a leukemia. Thus, pathways external to retinoid signaling must be deregulated by X-RARA in order to give rise to APL. We previously reported whole genome gene expression analysis of the hCG-NuMA-RARA transgenic mouse model (Sukhai et al, Blood, Blood, 2006: a2247), and that the NuMA-RARA fusion protein deregulated a wide array of hematopoietic and myeloid transcription factors in leukemic cells derived from transgenic mice. Here, we report that the deregulated expression of this transcription factor set (specifically, Gata-1, Gata-2, C/ebpa and Pu.1) accurately distinguished leukemic TM mice from WT animals, and was dependent upon both the presence of functional RXRA (Sukhai et al, Oncogene, 2008) and transgene dosage. We thus formulated the hypothesis that NuMA-RARA initiated the deregulation of a range of transcription factors, which in turn were responsible for deregulating pathways within the cell. In order to determine whether this transcription factor signature was unique to APL, we focused on GATA-1, GATA-2, PU.1 and C/EBPA, and examined their expression in a range of AML cell lines. Strikingly, the over-expression of GATA-1 was unique to APL cell lines and the OCI/AML4 cell line. GATA-2 was over-expressed in most cell systems tested, suggesting that its up-regulation may play a general role in leukemogenesis. C/EBPA was under-expressed in NB4 cells specifically, while PU.1 was not significantly deregulated in any cell system tested. Having identified that APL cell lines recapitulated our observation of deregulated myeloid transcription factor expression in transgenic mice, we sought to extend these studies to human patients. We analyzed the expression of GATA- 1, GATA-2, PU.1 and C/EBPA in a series of 12 APL patients, 10 AML patients with a range of diagnoses, and 12 normal BM samples. We observed specific up-regulation of GATA-1 (2.0–20.0-fold change, 7/12 patients) and GATA-2 (2.0–25.0-fold change, 9/12 patients) and under-expression of C/EBPA (0.05–0.5-fold change, 11/12 patients), in APL, but not AML, patient samples, in comparison to normal BM. The deregulated expression of these three transcription factors could accurately distinguish APL patient samples from AML and normal BM in both principle component analysis and hierarchical clustering analysis. We therefore report herein that NuMA-RARA deregulated a wide array of myeloid transcription factors, suggesting that a state of globally deregulated myeloid transcription exists in APL cells. Furthermore, a specific subset of transcription factors, GATA-1, GATA-2 and C/EBPA, can be used to specifically classify APL patient samples. These deregulated transcription factors may therefore serve as potential therapeutic targets and a means of distinguishing APL from other forms of acute myelogenous leukemia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.214
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2008
Admission routes1
Has abstractyes

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