A 14q32.31 Genomic-Imprinted DLK1-DIO3 microrna promotes Leukemogenesis By Inducing Stem Cell Quiescence and Inhibiting NK Cell Anti-Cancer Immunity
Bibliographic record
Abstract
Leukemia emergence, maintenance, relapse and/or progression are causally linked to the presence of drug-resistant leukemia-initiating cells and impaired natural killer (NK) cell anti-tumor immune-response. Bone marrow microenvironment (BMM)- and/or leukemia-derived signals induce aberrant non-coding RNA expression and inhibit protein phosphatase 2A (PP2A) tumor suppressor activity. PP2A loss-of-function is essential for NK cell activity and leukemic but not normal stem and progenitor cell proliferation and survival. The human MIR300 gene is an intergenic miRNA that belongs to the 14q32.31 DLK1-DIO3 genomic-imprinted tumor suppressor miRNA cluster B. MIR300 was found involved in loss-of heterozygosity, inhibited in several tumor types with high mitotic index and during epithelial-to-mesenchymal transition (EMT), and associated with a cancer stem cell phenotype. By using primary cells from Philadelphia-positive (Ph+) chronic myelogenous leukemia (CML) in chronic (CP) and blastic (BC) phase, and complex karyotype (CK) acute myeloid leukemia (AML) patients, as paradigmatic examples of stem cell-derived neoplasms characterized by constitutive expression of oncogenic kinases, PP2A loss-of-function, altered microRNA expression and impaired NK cell proliferation and cytotoxicity, we found that MIR300 is a cell context-independent tumor suppressor with anti-proliferative and PP2A-dependent pro-apoptotic activities which are sequentially activated in a MIR300 dose-dependent manner through inhibition of CCND2/CDK6 and SET (PP2A inhibitor), respectively. To prevent PP2A-induced apoptosis, MIR300 is inhibited by oncogenic signals in CD34+CML (CP and BC) and CK-AML progenitors. Conversely, tumor-naïve BMM-induced C/EBPbeta-mediated signals (hypoxia and MSC exosomes) markedly upregulate MIR300 expression in primary CML and AML CD34+CFSEmax leukemic stem (LSC) and CD56+CD3-NK cells to induce/maintain quiescence (increased CD34+leukemic blasts in G0) and impair immune-response (suppression of NK cell proliferation and cytotoxic activity toward CD34+ leukemic blasts and CFSEmaxCD34+ CML-BC quiescent LSCs), respectively. Inhibition of MIR300 expression/activity rescues NK cell proliferation and anti-tumor cytotoxicity and prevented MSC- and hypoxia-induced growth-suppression of CD34+leukemic blasts by inhibiting degradation of MIR300 targets (e.g. SET, CCND2). We found that CML and AML LSCs escape MIR300-induced PP2A-mediated apoptosis through the hypoxia- and tumor-dependent TGFb1-FoxM1-mediated upregulation of TUG1 lncRNA. TUG1 is an oncogenic lncRNA described as a MIR300sponge and found upregulated in solid tumors, in which it has strong diagnostic, prognostic and therapeutic relevance and is associated with cancer stem cell maintenance and EMT. In quiescent CML and AML LSCs, TUG1 uncouples and limits MIR300 tumor suppressor functions to cytostasis by maintaining unbound MIR300 at levels sufficient to inhibit CCND2 and CDK6 but not SET expression. Exposure to clinically-relevant CpG-modified oligonucleotides modulating MIR300levels and/or inhibiting TUG1 MIR300-sponging activity, restores NK cell proliferation and cytotoxic activity, and suppresses human leukemic but not normal hematopoiesis by eradicating nearly all (> 95% reduction) CFSEmaxCD34+ and CD45+CD34+CD38-CD90+ LSCs and CD34+leukemic CML (CP and BC) and CK-AML blasts in vitro (CFCs, LTC-IC, and CFSEmaxCD34+cell tracking) and/or in NRG-SGM3 PDX mouse models of acute and chronic myeloid leukemias. Altogether, this work highlights the therapeutic importance of altering MIR300 expression in anti-LSC and NK cell-based approaches for myeloid leukemias, and indicates that tumor-naïve BMM-induced MIR300 tumor suppressor anti-proliferative and PP2A-activating functions may support leukemogenesis by promoting the formation and initial expansion of the quiescent LSC pool through the induction of LSC dormancy and inhibition of quiescent LSC killing by cytokine-activated NK cells, respectively. (G.S. and R.T. equally contributed to this work) Disclosures Stagno: BMS: Honoraria; Incyte: Honoraria; Pfizer: Honoraria; Novartis: Honoraria. Deininger:TRM: Consultancy; Sangoma: Consultancy; Incyte: Honoraria; Novartis: Honoraria; Sangamo: Consultancy; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Humana: Honoraria; Ascentage Pharma: Consultancy, Honoraria; Blueprint: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Research Funding; Fusion Pharma: Consultancy; Adelphi: Consultancy. Milojkovic:BMS: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Incyte: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau. Apperley:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Baer:Takeda: Research Funding; Incyte: Research Funding; Kite: Research Funding; Forma: Research Funding; AI Therapeutics: Research Funding; Abbvie: Research Funding; Astellas: Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".