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Record W3096229548 · doi:10.1182/blood-2020-135867

A Human Model of Down Syndrome Associated Leukemia Reveals Different Cell of Origins for Initiation and Progression

2020· article· en· W3096229548 on OpenAlexaff
Elvin Wagenblast, Joana Araújo, Olga I. Gan, Sarah K. Cutting, Alex Murison, Jessica McLeod, Gabriela Krivdova, Maria Azkanaz, Sabrina Smith, Johann Hitzler, John E. Dick, Eric R. Lechman

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsAcute megakaryoblastic leukemiaGATA1Cancer researchLeukemiaBiologyHaematopoiesisGeneticsStem cell

Abstract

fetched live from OpenAlex

Introduction: Leukemia is the most common cancer in children and sequencing data suggest that the first genetic alterations often occur in utero. Children with Down syndrome (Trisomy 21, T21) have a 150-fold increased risk of childhood leukemia. In 30% of newborns with Down syndrome, a transient myeloproliferative disorder (pre-leukemia) occurs, which is characterized by a clonal proliferation of immature megakaryoblasts carrying somatic mutations in the GATA1 transcription factor (GATA1s) and resolves spontaneously in most cases. In 20% of the cases, acute megakaryoblastic leukemia (AMKL) evolves from the pre-leukemic clone by acquisition of additional mutations, such as in the cohesin subunit STAG2. It is hypothesized that this represents a multi-step process of leukemogenesis with three distinct genetic events: T21, GATA1s and STAG2. Yet, it remains unclear how an extra copy of chromosome 21 predisposes towards leukemia, the interplay between each genetic event and the cellular origin of transformation. Methods: Human long-term hematopoietic stem cells (LT-HSCs) were sorted from normal karyotype and T21 fetal livers (N-FL and T21-FL) and subsequently CRISPR/Cas9 edited to try to establish a humanized model of Down Syndrome associated pre-leukemia and AMKL. To model the initiation of the pre-leukemic state, GATA1s mutations were introduced, while additional STAG2- mutations were overlaid to model the progression to fully transformed AMKL. CRISPR/Cas9-edited control, GATA1s, STAG2- and GATA1s/STAG2- LT-HSCs were functionally interrogated in near-clonal xenograft assays, along with transcriptional and epigenetic profiling. Results: T21 status in combination with GATA1s had a profound synergistic effect on megakaryocytic lineage output in vivo compared to normal karyotype with GATA1s. Moreover, a high percentage of blasts were found in xenografts of GATA1s edited T21-FL LT-HSCs (>30%) but not in xenografts of GATA1s edited N-FL LT-HSCs. Conversely, GATA1s/STAG2- edited LT-HSCs generated grafts with >50% of blasts, regardless of T21 status. The immunophenotype of these blasts recapitulated those observed in patients diagnosed with Down Syndrome pre-leukemia and AMKL (CD117+CD34+CD41+CD71+CD33+CD4+CD7+). Thus, T21 is required for pre-leukemia development, but seems dispensable for AMKL as both N- and T21-FL LT-HSCs underwent leukemic transformation upon GATA1s/STAG2-. Serial xenotransplantation assays from primary engrafted mice were carried out to assess self-renewal properties of GATA1s-induced pre-leukemia and GATA1s/STAG2- induced AMKL. Only GATA1s/STAG2- edited N- and T21-FL grafts were able to propagate the leukemic phenotype with a high stem cell frequency, which was endowed by the additional STAG2- knock-out. ATACseq and RNAseq profiling of blast populations revealed an enrichment of GATA-binding sites with concomitant up-regulation of genes implicated in translation. To assess the role of progenitor cells in pre-leukemic initiation and leukemic progression, we CRISPR/Cas9 edited short-term HSCs, common myeloid progenitors and myelo-erythroid progenitors with GATA1s and/or STAG2- and subjected them to xenotransplantation. Strikingly, all progenitor subsets with combined GATA1s/STAG2- editing were able to drive leukemic transformation, while single GATA1s editing in the same subsets did not initiate pre-leukemia. This data strongly suggests that the initial GATA1s mutation must occur in T21 LT-HSCs, but subsequent STAG2 mutations can occur further downstream in progenitors. Lastly, to gain insight into how chromosome 21 predisposes towards pre-leukemia, three chromosome 21 miRNAs (miR-99a, -125b-2 and -155) were identified to be up-regulated in T21-FL LT-HSCs compared to N-FL LT-HSCs. Over-expression of these miRNAs in N-FL LT-HSCs induced a T21-like state with increased myeloid and megakaryocytic skewing. Dramatically, CRISPR/Cas9-edited knock-out of these miRNAs in GATA1s edited T21-FL LT-HSCs resulted in a block of pre-leukemia initiation. Conclusion: Our findings demonstrate that T21 is required for pre-leukemia initiation, which is mediated by over-expression of chromosome 21 miRNAs in LT-HSCs. Further, this data demonstrates different cell of origins between pre-leukemia initiation and AMKL progression. Ongoing studies focus on preventing the progression of pre-leukemia to AMKL by pharmacological targeting. Figure Disclosures Dick: Bristol-Myers Squibb/Celgene: 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 score0.004
Threshold uncertainty score0.014

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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
Published2020
Admission routes1
Has abstractyes

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