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Modeling Human T Cell Acute Lymphoblastic Leukemia In Vitro and In Vivo with LMO2.

2010· article· en· W2521870844 on OpenAlexaff
James A. Kennedy, Sara Berthiaume, Frédéric Barabé

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsCentre hospitalier de l'Université LavalUniversity of Toronto
Fundersnot available
KeywordsBiologySevere combined immunodeficiencyProgenitor cellHaematopoiesisCancer researchStem cellT cellCD8In vivoCell cultureLeukemiaGenetic enhancementImmunologyMolecular biologyCell biologyGeneGeneticsAntigenImmune system

Abstract

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Abstract Abstract 3648 The studies identifying gene translocations and mutations in T-ALL cell lines and/or in patients have contributed significantly to the understanding of the genetic abnormalities involved in T-ALL. However, studies on the biology of these genes, the targeted cells, the sequence and the number of hits required to convert a primary human hematopoietic stem cell (HSC)/progenitor cell into a fully transformed leukemic cell require good experimental models of human T cell development both in vivo and in vitro. The only in vivo model of human T cell leukemogenesis came unexpectedly from the gene therapy trial on patients with X-linked severe combined immunodeficiency (SCID-X1). Three to five years after gene therapy, 4 out of 10 patients in the trial developed clonal T-ALL. In these patients, retroviral integrations were found in proximity to the LMO2 promoter in the malignant clones, leading to aberrant expression of the oncogene. However, little is known on the effect of LMO2 overexpression in human cells and how it facilitates the development of T-ALL. We have developed in vivo and in vitro models to study the role of T cell oncogenes in human cells. Using the OP9-DL1 co-culture system to differentiate human HSC into mature T cells in vitro, we culture human HSC transduced with lentiviruses expressing LMO2. LMO2 overexpressing cells are blocked at the double negative stage (CD4-CD8-) of differentiation when co-cultured on OP9-Delta-Like1 stroma and proliferate 50 to 100 times more than control cells. However, these cells are not immortalized and cultures lasted approximately 80 days. LMO2 overexpression have no effect on myeloid differentiation in vitro. In vivo, LMO2 transduced human HSC/progenitor cells engraft the bone marrow of immunodeficient mice to levels comparable to control cells, while normal myeloid and B cell populations 20–24 weeks post-transplantation. LMO2 transduced cells have an increased capacity to generate T cells in the thymus in comparison to control cells (42% engraftment vs 8%, p<0.0001). Surprisingly, thymic and peripheral LMO2 cells are not blocked in their differentiation. LMO2 cells did not engraft secondary mice, confirming that LMO2 doesn't induce self-renewal of human HSC. However, the increase in thymic repopulation by LMO2 cells and the lack of differentiation block in vivo suggest that LMO2 overexpression generates an abnormal T cell population with an increase repopulation advantage (increase proliferation or decrease apoptosis) in the thymus which becomes the substrate for additional genetic/epigenetic events. To test this hypothesis, we tried to immortalize LMO2 cells in vitro with secondary hits. Our preliminary results show that insertional mutagenesis can immortalized LMO2 cells in vitro. However these cells are not able to engraft immunodeficient mice or generate leukemia in vivo. The addition of intracellular NOTCH to one immortalized LMO2 cell line allows these cells to engraft and generate human T-ALL in vivo. Globally, these results show that T cell oncogenes can be studied in primary human hematopoietic cells both in vitro and in vivo. Also, at least three hits are required to transform a human primary HSC/progenitor cell into a leukemic cell able to engraft and generate leukemia in vivo. It also suggests that a non-engrafting cell can be turned into a leukemic cell generating leukemia in vivo, implying that a cell can regain self-renewing properties. Disclosures: No relevant conflicts of interest to declare.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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.

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
Published2010
Admission routes1
Has abstractyes

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