DLK1 is a GATA1s-Driven Dependency and Therapeutic Target in Down Syndrome-Associated Myeloid Leukemia
Bibliographic record
Abstract
Abstract Children with Down syndrome have a markedly increased risk of developing myeloid leukemia (ML-DS). Although having an excellent prognosis, 10–20% develop relapsed or refractory disease with poor survival, highlighting the need for new targeted approaches. The pathogenesis of ML-DS is tightly linked to fetal hematopoiesis and mutations in GATA1, generating the truncated GATA1short(s) isoform. We identified Delta-like non-canonical Notch ligand 1 (DLK1) as a direct GATA1s target. DLK1, a paternally imprinted transmembrane protein, is highly expressed in fetal liver CD34⁺ cells but absent in adult hematopoiesis, making it an attractive immunotherapeutic target. Chromatin profiling revealed GATA1s occupancy at a distal enhancer within the DLK1–DIO3 locus, driving aberrant DLK1 upregulation in ML-DS. Functional studies demonstrated that DLK1 is a leukemia dependency, as its genetic ablation impaired proliferation and engraftment, induced apoptosis, and altered Notch and β-catenin signaling. Therapeutically, a DLK1-directed antibody-drug conjugate (DLK1-ADC) induced selective cytotoxicity, abrogated colony formation, and significantly prolonged survival in refractory ML-DS PDX models, achieving durable remissions at higher doses. These findings establish DLK1 as a leukemia-specific vulnerability and provide preclinical proof-of-concept for DLK1-targeted therapies in ML-DS and other leukemias with fetal-like expression programs. Key Points DLK1 is a GATA1s-driven leukemia dependency in ML-DS, linking fetal hematopoietic programs to leukemic stemness and refractory disease. Therapeutic targeting of DLK1 with an antibody–drug conjugate selectively eradicates leukemia cells and prolongs survival in ML-DS PDX models
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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".