Effective drug treatment identified by in vivo screening in a transplantable patient-derived xenograft model of chronic myelomonocytic leukemia
Bibliographic record
Abstract
To establish novel and effective treatment combinations for chronic myelomonocytic leukemia (CMML) preclinically, we hypothesized that supplementation of CMML cells with the human oncogene Meningioma 1 (MN1) promotes expansion and serial transplantability in mice, while maintaining the functional dependencies of these cells on their original genetic profile. Using lentiviral expression of MN1 for oncogenic supplementation and transplanting transduced primary mononuclear CMML cells into immunocompromised mice, we established three serially transplantable CMML-PDX models with disease-related gene mutations that recapitulate the disease in vivo. Ectopic MN1 expression was confirmed to enhance the proliferation of CMML cells, which otherwise did not engraft upon secondary transplantation. Furthermore, MN1-supplemented CMML cells were serially transplantable into recipient mice up to 5 generations. This robust engraftment enabled an in vivo RNA interference screening targeting CMML-related mutated genes including NRAS, confirming that their functional relevance is preserved in the presence of MN1. The novel combination treatment with azacitidine and the MEK-inhibitor trametinib additively inhibited ERK-phosphorylation and thus depleted the signal from mutated NRAS. The combination treatment significantly prolonged survival of CMML mice compared to single-agent treatment. Thus, we identified the combination of azacitidine and trametinib as an effective treatment in NRAS-mutated CMML and propose its clinical development.
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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) | 0.001 | 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".