FLT4 activation promotes chemoresistance in pediatric leukemia through stabilization of MDM2/MDMX and inactivation of p53
Bibliographic record
Abstract
Abstract Aberrant Receptor Tyrosine Kinase (RTK) signaling allows cancer cells to modulate survival, proliferation and death, leading to tumorigenesis and chemoresistance. In leukemia, the RTK FMS-Related Tyrosine Kinase 4 (FLT4) (also known as VEGFR-3, Vascular Endothelial Growth Factor- 3) is deregulated and correlates with cancer progression. However, the underlying consequences of its deregulation remain to be determined. Moreover, chemotherapy treatment requires that cancer cells retain a wild type p53 (wt) in order to respond to DNA damage by tumor suppressing activities, i.e. apoptosis. p53 activity is predominantly limited by its two major negative regulators, MDM2 and MDMX, which inactivate p53 by promoting its degradation and/or cytoplasmic localization. In this study, we have shown that activation of FLT4 by either overexpression or binding of its ligand, VEGF-C, leads to an increase in MDM2/MDMX stability, inactivation of p53 and resistance to DNA damaging therapies. Through immunoprecipitation and mass spectrometry analysis, we observed that FLT4 induced phosphorylation of MDMX at Ser-314, a consensus sequence of CDK4/6. Our data revealed that phosphorylation of MDMX on Ser-314 increases the stability of MDMX, which subsequently affects MDM2 and p53 degradation and could be reversed by the CDK4/6 inhibitor Palbociclib. More importantly, leukemic cells treated with Palbociclib were more susceptible to DNA damaging induction of apoptosis and had reduced cell proliferation. Altogether, our research proposes an innovative way to reactivate p53 in pediatric leukemia through the pharmacological inhibition of FLT4 signaling, which could serve as a potential treatment option for this disease.
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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".