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Record W2530306381 · doi:10.1182/blood.v124.21.777.777

Dose Dependent Role of Gfi1 in Human MDS and AML and Its Suitability As a Novel Target

2014· article· en· W2530306381 on OpenAlexaff
Judith Hönes, Lacramioara Botezatu, Lars Michel, Christian Thiede, Bert van der Reijden, Michael Heuser, Hideki Makishima, Jaroslaw P. Maciejewski, Tomas Radiovoyevitch, Gerhard Ehninger, Ulrich Dührsen, Tarik Möröy, Cyrus Khandanpour

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsMontreal Clinical Research Institute
Fundersnot available
KeywordsMyeloid leukemiaCancer researchHaematopoiesisBone marrowStem cellMyeloidTranscription factorLeukemiaMedicineBiologyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Acute myeloid Leukemia (AML) characteristically involves a block in hematopoietic stem cell (HSC) differentiation to mature cells. This results in blast accumulation in the bone marrow (BM). Myelodysplastic syndrome (MDS) is also characterized by disturbed differentiation, but with less severe blockage of lineages. MDS often progresses to AML. Differentiation of HSC to myeloid cells is regulated by transcription factors like growth factor independence 1 (GFI1) on chromosome 1. Understanding is needed to develop AML/MDS treatments that target such factor. We investigated the role of different Gfi1 levels on the initiation, progression and prognosis of AML and whether Gfi1 could serve as a new therapeutic target of AML therapy. First, analysis of published expression array data showed that Gfi1 is expressed at a lower level in blast cells compared to control cells. In addition low Gfi1 expression level in blast cells of patients was associated with an inferior prognosis (9 months median event free survival) compared to patients with higher Gfi1 expression (32 months). With regard to MDS patients, deletion or lower levels of Gfi1 were associated with higher level of blasts. We found that in low Gfi1 expressing blasts Gfi1 target genes are up-regulated, confirming a role of Gfi1 in leukemia development. To verify that low levels or deletion of one Gfi1 allele causes AML we used mice expressing Gfi1 at the following levels: Gfi1 deficient mice (Gfi1 KO) with no expression; mice expressing Gfi1 only at 20% of WT Gfi1 expression level (Gfi1 KD); mice heterozygous for Gfi1 (Gfi1 het) with ~50% of WT expression, and WT mice. These mice were crossed with Nup98HoxD13 mice that recapitulate MDS in humans. Nup98HoxD13 tg mice develop pancytopenia and some mice develop AML or ALL. We observed that Gfi1 KD (n=13) accelerated AML development compared to Gfi1 WT mice (n=21). Complete loss of Gfi1 (n=14) inhibited AML development (p=0.03). Furthermore, deletion of one Gfi1 allele (Gfi1 het) mice (n= 26) shortened leukemia free survival (p=0.024) compared to WT mice with a higher AML-incidence of 50% whereas WT (n=46) mice have only 31%. We then confirmed our findings in an independent AML-mouse model: we crossed WT (n=31) and Gfi1-KD (n=24) mice with MLL-AF9 transgenic mice and found marginally (p=0.06) decreased leukemia free survival in Gfi1-KD mice. It has been shown that retroviral expression of onco-fusion proteins such as MLL-AF9 or AML1-Eto9a can cause AML in mice. To confirm our findings, we transplanted Gfi1 KD and WT MLL-AF9 transduced Lin-BM cells into lethally irradiated recipient mice. Mice transplanted with MLL-AF9-Gfi1 KD cells (n=7) developed AML faster than mice transplanted with MLL-AF9-WT cells (n=12; p< 0.0001). In contrast to Gfi1 KD, Gfi1 KO prevented leukemia initiation in a retroviral AML mouse model. If Gfi1 KD accelerates the emergence of AML, Gfi1 overexpression should hamper the development of AML. We thus retrovirally co-transduced, haematopoietic progenitor cells from WT mice with retroviruses overexpressing onco-fusion proteins and either Gfi1 or an empty vector. We sed the cells in liquid culture and methylcellulose. Overexpression of Gfi1 was associated with 4-fold fewer colonies and total cells relative to cells transduced with empty vector (p=0.01). In summary, either complete loss of Gfi1 or overexpression could be use as an approach to treat leukemia. To examine the role of different Gfi1 levels in AML maintenance in-vivo, we transplanted leukemic Gfi1 conditional (Gfi1 expression can be conditionally deleted) and Gfi1 up-regulation conditional (Gfi1-overexpression can be induced) cells into irradiated mice. Induced up-regulation of Gfi1 led to terminal differentiation of blast cells whereas induced loss of Gfi1 led to complete absence of blasts due to increased apoptosis. Analyzing different leukemic samples expressing Gfi1 at different level using RNA-Seq, we observed that, at low levels, Gfi1 inhibits apoptosis by inhibiting p53, at medium levels it also represses oncogenes, and at high levels it induces differentiation. Thus Gfi1 plays a dose-dependent role in AML and either very high expression or ablation of Gfi1 could be a useful novel therapeutic objective. We are currently investigating these effects on human cells. Disclosures No relevant conflicts of interest to declare.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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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Citations0
Published2014
Admission routes1
Has abstractyes

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