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Leukemia-Initiating Cells in Acute Megakaryoblastic Leukemia and Transient Leukemia of Down Syndrome.

2007· article· en· W2549339569 on OpenAlexaff
Jian Chen, Yue Li, Monica Doedens, John E. Dick, Alvin Zipursky, Johann Hitzler

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health NetworkHospital for Sick Children
Fundersnot available
KeywordsAcute megakaryoblastic leukemiaLeukemiaGATA1HaematopoiesisDown syndromeTransplantationImmunologyAcute leukemiaMedicinePopulationCord bloodBiologyStem cellCancer researchInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract Background. Children with Down syndrome (DS) have a 500-fold greater risk of developing Acute Megakaryoblastic Leukemia (AMKL) than the general population. In addition, approximately one out of ten newborns with DS has circulating blasts in the blood, a condition termed Transient Leukemia (TL). Unlike AMKL, TL resolves spontaneously within three months but in about 20% of cases is followed by AMKL later in life. Both the blasts of TL and AMKL of DS (DS-AMKL) show megakaryocytic differentiation and both harbor somatic mutations of GATA1, resulting in the expression of the N-terminally truncated mutant protein GATA1s. We hypothesize that the difference between the reversible and irreversible phenotype of TL and AMKL in DS, respectively, is due to a functional difference of the leukemia-initiating cells in both conditions. Methods. To characterize the leukemia-initiating cells we established experimental models of AMKL and TL of DS by transplanting cryopreserved primary human cells into NOD/SCID mice. Cell doses ranging from 0.5 to 20x106 were injected intrafemorally into 8-week-old irradiated recipients, which had also been treated with anti-NK cell antibody (anti-CD122). Human hematopoietic growth factors (stem cell factor, interleukin-3 and thrombopoietin) were administered intraperitoneally during the first two weeks following transplantation. Phenotypic analysis using standard cytological, histological and flowcytometric methods was carried out approximately 8 weeks after transplantation. Results. Recipients transplanted with 2 (of a total of 7) AMKL samples showed engraftment with 32% (range 26–44%; n=3) and 73% (range 16–95%; n=8) human cells at the site of the original cell injection (right femur) and 15% (n=3) and 38% (n=8) at distant medullary sites. The engrafted human cells were trisomic for human chromosome 21, expressed the megakaryocytic marker CD61 and, compared with the transplanted primary AMKL cell population, harbored the concordant GATA1 mutation. Bone marrow biopsy revealed increased reticulin fibres 8 weeks after transplantation of AMKL cells. In our experiments, DS-AMKL-initiating cells were found to occur within a broad range of frequency (18x10−4 to 20x10−6) but were not defined by their expression of CD34 and/or CD38. In keeping with the self-renewal capacity of leukemia-initiating cells in human acute myeloid leukemia, DS-AMKL cells collected from the right femur (site of initial cell injection) and from distant bone marrow sites of primary recipients were able to engraft secondary recipients. In contrast, only one of five primary TL cell samples showed engraftment within the right femur (21%, range 3–81%; n=5), the site at which TL cells had been injected 8 weeks earlier. No TL cells or engrafted human cells were detected in any distal bone marrow site or extramedullary compartment such as the spleen. Conclusion. Our results indicate that the function of leukemia-initiating cells in DS-AMKL but not TL parallels those of non-DS human acute myeloid leukemia. Our model provides an experimental approach to distinguish the role of the cellular target vs. mutations cooperating with GATA1 mutations in the development of AMKL and TL in DS.

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: Observational · Consensus signal: none
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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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 designObservational
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

Citations0
Published2007
Admission routes1
Has abstractyes

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