Inhibition of MLLT1 limits growth of MLL-AF4 leukaemias without killing healthy haematopoietic stem cells
Bibliographic record
Abstract
Abstract A major challenge in cancer therapeutics has been the identification of targets that are selectively toxic to cancer cells while displaying limited effects on healthy counterparts. Toxicities related to blood production from haematopoietic stem and progenitor cells (HSPCs) can be particularly problematic and result in patient morbidity and mortality. Within haematological malignancies, therapy response rates and patient survival vary widely between cancer subtypes, with leukaemias driven by the MLL-AF4 fusion protein associated with poor prognosis. MLLT1 has been recently identified as a key potential target in acute myeloid leukaemia. Here we evaluated a panel of leukaemia cell lines and healthy HSPCs for their sensitivity to the MLLT1 inhibitor SGC-iMLLT. We found that SGC-iMLLT strongly inhibited MLL-AF4-driven leukaemia growth in vitro and in vivo. By contrast, SGC-iMLLT did not alter in vitro colony forming potential of human HSPCs or affect long-term in vivo function of mouse HSPCs. These results suggest that SGC-iMLLT may have a promising therapeutic window in the treatment of MLL-AF4-driven leukaemias, and that further clinical development is warranted.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".