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Record W2775826629 · doi:10.3324/haematol.2017.177485

Micro-ribonucleic acid-155 is a direct target of Meis1, but not a driver in acute myeloid leukemia

2017· article· en· W2775826629 on OpenAlexafffund
Edith Schneider, Anna Staffas, Linda Röhner, Erik Delsing Malmberg, Arghavan Ashouri, Kathrin Krowiorz, Nicole Pochert, Christina Miller, Stella Yuan Wei, Laleh S. Arabanian, Christian Buske, Hartmut Döhner, Lars Bullinger, Linda Fogelstrand, Michael Heuser, Konstanze Döhner, Ping Xiang, Jens Rüschmann, Oleh I. Petriv, Alireza Heravi‐Moussavi, Carl L. Hansen, Martin Hirst, R. Keith Humphries, A. MAUREEN ROUHI, Lars Palmqvist, Florian Kuchenbauer

Bibliographic record

VenueHaematologica · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsMyeloid leukemiaLeukemiaMyeloidCancer researchMedicineInternal medicineChemistry

Abstract

fetched live from OpenAlex

Micro-ribonucleic acid-155 (miR-155) is one of the first described oncogenic miRNAs. Although multiple direct targets of miR-155 have been identified, it is not clear how it contributes to the pathogenesis of acute myeloid leukemia. We found miR-155 to be a direct target of Meis1 in murine Hoxa9/Meis1 induced acute myeloid leukemia. The additional overexpression of miR-155 accelerated the formation of acute myeloid leukemia in Hoxa9 as well as in Hoxa9/Meis1 cells in vivo. However, in the absence or following the removal of miR-155, leukemia onset and progression were unaffected. Although miR-155 accelerated growth and homing in addition to impairing differentiation, our data underscore the pathophysiological relevance of miR-155 as an accelerator rather than a driver of leukemogenesis. This further highlights the complexity of the oncogenic program of Meis1 to compensate for the loss of a potent oncogene such as miR-155. These findings are highly relevant to current and developing approaches for targeting miR-155 in acute myeloid 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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.

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".

Quick stats

Citations12
Published2017
Admission routes2
Has abstractyes

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