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Mir-126 Governs Human Leukemia Stem Cell Quiescence and Chemotherapy Resistance

2013· article· en· W2419001201 on OpenAlexaff
Eric R. Lechman, Bernhard Gentner, Peter van Galen, Stanley W.K. Ng, Kolja Eppert, René Marke, Liran Slush, James A. Kennedy, Amanda Mitchell, Weihsu Claire Chen, Véronique Voisin, Gary D. Bader, Mark D. Minden, Jun Lü, Jean Wang, Luigi Naldini, John E. Dick

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMontreal Children's HospitalUniversity of TorontoPrincess Margaret Cancer CentreMcGill UniversityUniversity Health Network
Fundersnot available
KeywordsStem cellBiologyHaematopoiesisMyeloid leukemiaLeukemiamicroRNACancer researchImmunologyPopulationXenotransplantationTransplantationCell biologyMedicineInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Abstract miRNA expression is deregulated in human acute myeloid leukemia (AML), however the impact of altered post-transcriptional programs on the genesis and maintenance of leukemia stem cells (LSC) remains undefined. In order to elucidate the functional role of miRNA in LSC and identify relevant miRNA candidates, we performed global miRNA profiling on sorted cell subpopulations from 16 AML patient and 3 umbilical cord blood samples (Eppert et al, Nature Medicine 2011). Supervised analysis guided by the ability of each sub-population to initiate leukemic engraftment after xenotransplantation into immune-deficient mice generated a unique miRNA signature. miR-126, a miRNA that we previously demonstrated to have a conserved role in maintaining hematopoietic stem cell (HSC) quiescence (Lechman et al. Cell Stem Cell, 2012), was more highly expressed in LSC-enriched fractions and chosen for further validation. To confirm that miR-126 is a bona fide LSC determinant, we utilized a bidirectional lentiviral reporter vector specific for miR-126 (Gentner et al. Science Translational Medicine, 2010) to sort cells from AML patient samples based on miR-126 bioactivity, and demonstrated that all in vivo leukemia-initiating capacity was confined to cells with elevated miR-126 bioactivity. Lentiviral enforced expression of miR-126 in primary AML patient samples significantly increased LSC frequency (3.5-52.3 fold) as assessed by limiting dilution transplantation assays, while diminishing cell cycle entry, differentiation marker expression (CD14,CD15) and colony forming potential. Sponge-mediated knockdown of miR-126 expression resulted in the opposite effects. These findings suggest that high levels of miR-126 bioactivity support self-renewal/maintenance of primitive AML cells at the cost of aberrant differentiation. Moreover, by preserving LSC quiescence miR-126 promoted chemotherapy resistance, in part through suppression of CDK3, a gatekeeper of G0 to G1 cell cycle transit. Enforced expression of CDK3 partially rescued the functional consequences of supra-physiological levels of miR-126 bioactivity, rendering previously resistant LSC susceptible to killing by AraC/Daunorubicin combination chemotherapy. Our human LSC miRNA signature, optimized by regression analysis on a cytogenetically normal AML patient cohort, was prognostic for survival in a large independent AML patient cohort (Ley et. al N Engl. J Med, 2013) further validating the clinical significance of miRNA as stem cell determinants. Furthermore, miRNA-126 alone was prognostic for survival in two independent cohorts of AML patients with normal cytogenetics. These data demonstrate a mechanistic role for miR-126 in governing intrinsic LSC properties and establish miR-126 as a critical biomarker for clinical outcome. Disclosures: Wang: Trillium Therapeutics/Stem Cell Therapeutics: Research Funding.

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 head0.205
Teacher spread0.200 · 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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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