Pseudo-mutant p53 as a targetable phenotype of <i>DNMT3A</i> -mutated pre-leukemia
Bibliographic record
Abstract
Abstract Pre-leukemic clones carrying DNMT3A mutations have a selective advantage and an inherent chemo-resistance, however the basis for this phenotype has not been fully elucidated. Mutations affecting the gene TP53 occur in pre-leukemic hematopoietic stem/progenitor cells (preL-HSPCs) and lead to chemo-resistance. Many of these mutations cause a conformational change and some of them were shown to enhance self-renewal capacity of preL-HSPCs. Intriguingly, a misfolded p53 was described in AML blasts that do not harbor mutations in TP53 , emphasizing the dynamic equilibrium between a wild-type (WT) and a “pseudomutant” conformations of p53. By combining single cell analyses and p53 conformation-specific monoclonal antibodies we studied preL-HSPCs from primary human DNMT3A AML samples. We found that while leukemic blasts express mainly the WT conformation, in preL-HSPCs the pseudomutant conformation is the dominant. HSPCs from non-leukemic samples expressed both conformations to a similar extent. Treatment with a short peptide that can shift the dynamic equilibrium favoring the WT conformation of p53, specifically eliminated preL-HSPCs that had dysfunctional canonical p53 pathway activity as reflected by single cell RNA sequencing. Our observations shed light upon a possible targetable p53 dysfunction in human preL-HSPCs carrying DNMT3A mutations. This opens new avenues for leukemia prevention.
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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) | 0.002 | 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".