<i>RAS</i> mutations drive proliferative chronic myelomonocytic leukemia via activation of a novel KMT2A-PLK1 axis
Bibliographic record
Abstract
ABSTRACT Chronic myelomonocytic leukemia (CMML) is an aggressive hematological malignancy with limited treatment options. Whole exome (WES) and targeted sequencing of several independent cohorts of CMML patients, comparing dysplastic (dCMML) to proliferative (pCMML) CMML, as well as paired chronic phase disease and acute leukemic transformation (LT), associate acquisition of oncogenic RAS pathway mutations, the most common being NRAS G12D , with aggressive disease and with disease progression. Using patient derived progenitor colony assays and a NRAS G12D -Vav-Cre mouse model, we further demonstrate the role of mutant RAS signaling in driving and maintaining pCMML phenotype. RNA-sequencing links RAS pathway mutations with an increased expression of genes encoding the mitotic checkpoint kinases PLK1 and WEE1. Further, we dmeoinstrated that non-mutated lysine methyltransferase KMT2A (MLL1) acts as mediator of NRAS-induced PLK1 and WEE1 expression. Finally, we demonstrate the translational value of our findings by showing that pharmacological PLK1 inhibition decreases monocytosis and hepatosplenomegaly while improving hematopoiesis in RAS mutant patient-derived xenografts. Hence, we define severe CMML as oncogenic RAS pathway-enriched malignancies, with a unique gene expression profile regulated by KMT2A , amenable to therapeutic intervention.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".