MLL leukemia induction by genome editing of human CD34+ hematopoietic cells
Bibliographic record
Abstract
Chromosomal rearrangements involving the mixed-lineage leukemia (MLL) gene occur in primary and treatment-related leukemias and confer a poor prognosis. Studies based primarily on mouse models have substantially advanced our understanding of MLL leukemia pathogenesis, but often use supraphysiological oncogene expression with uncertain implications for human leukemia. Genome editing using site-specific nucleases provides a powerful new technology for gene modification to potentially model human disease, however, this approach has not been used to re-create acute leukemia in human cells of origin comparable to disease observed in patients. We applied transcription activator-like effector nuclease-mediated genome editing to generate endogenous MLL-AF9 and MLL-ENL oncogenes through insertional mutagenesis in primary human hematopoietic stem and progenitor cells (HSPCs) derived from human umbilical cord blood. Engineered HSPCs displayed altered in vitro growth potentials and induced acute leukemias following transplantation in immunocompromised mice at a mean latency of 16 weeks. The leukemias displayed phenotypic and morphologic similarities with patient leukemia blasts including a subset with mixed phenotype, a distinctive feature seen in clinical disease. The leukemic blasts expressed an MLL-associated transcriptional program with elevated levels of crucial MLL target genes, displayed heightened sensitivity to DOT1L inhibition, and demonstrated increased oncogenic potential ex vivo and in secondary transplant assays. Thus, genome editing to create endogenous MLL oncogenes in primary human HSPCs faithfully models acute MLL-rearranged leukemia and provides an experimental platform for prospective studies of leukemia initiation and stem cell biology in a genetic subtype of poor prognosis leukemia.
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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) | 0.001 | 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".