Human models of NUP98-KDM5A megakaryocytic leukemia in mice contribute to uncovering new biomarkers and therapeutic vulnerabilities
Bibliographic record
Abstract
Acute megakaryoblastic leukemia (AMKL) represents ∼10% of pediatric acute myeloid leukemia cases and typically affects young children (<3 years of age). It remains plagued with extremely poor treatment outcomes (<40% cure rates), mostly due to primary chemotherapy refractory disease and/or early relapse. Recurrent and mutually exclusive chimeric fusion oncogenes have been detected in 60% to 70% of cases and include nucleoporin 98 (NUP98) gene rearrangements, most commonly NUP98-KDM5A. Human models of NUP98-KDM5A-driven AMKL capable of faithfully recapitulating the disease have been lacking, and patient samples are rare, further limiting biomarkers and drug discovery. To overcome these impediments, we overexpressed NUP98-KDM5A in human cord blood hematopoietic stem and progenitor cells using a lentiviral-based approach to create physiopathologically relevant disease models. The NUP98-KDM5A fusion oncogene was a potent inducer of maturation arrest, sustaining long-term proliferative and progenitor capacities of engineered cells in optimized culture conditions. Adoptive transfer of NUP98-KDM5A-transformed cells into immunodeficient mice led to multiple subtypes of leukemia, including AMKL, that phenocopy human disease phenotypically and molecularly. The integrative molecular characterization of synthetic and patient NUP98-KDM5A AMKL samples revealed SELP, MPIG6B, and NEO1 as distinctive and novel disease biomarkers. Transcriptomic and proteomic analyses pointed to upregulation of the JAK-STAT signaling pathway in the model AMKL. Both synthetic models and patient-derived xenografts of NUP98-rearranged AMKL showed in vitro therapeutic vulnerability to ruxolitinib, a clinically approved JAK2 inhibitor. Overall, synthetic human AMKL models contribute to defining functional dependencies of rare genotypes of high-fatality pediatric leukemia, which lack effective and rationally designed treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".