Posttranscriptional deregulation of MYC via PTEN constitutes a major alternative pathway of MYC activation in T-cell acute lymphoblastic leukemia
Bibliographic record
Abstract
Cumulative evidence indicates that MYC, one of the major downstream effectors of NOTCH1, is a critical component of T-cell acute lymphoblastic leukemia (T-ALL) oncogenesis and a potential candidate for targeted therapy. However, MYC is a complex oncogene, involving both fine protein dosage and cell-context dependency, and detailed understanding of MYC-mediated oncogenesis in T-ALL is still lacking. To better understand how MYC is interspersed in the complex T-ALL oncogenic networks, we performed a thorough molecular and biochemical analysis of MYC activation in a comprehensive collection of primary adult and pediatric patient samples. We find that MYC expression is highly variable, and that high MYC expression levels can be generated in a large number of cases in absence of NOTCH1/FBXW7 mutations, suggesting the occurrence of multiple activation pathways in addition to NOTCH1. Furthermore, we show that posttranscriptional deregulation of MYC constitutes a major alternative pathway of MYC activation in T-ALL, operating partly via the PI3K/AKT axis through down-regulation of PTEN, and that NOTCH1(m) might play a dual transcriptional and posttranscriptional role in this process. Altogether, our data lend further support to the significance of therapeutic targeting of MYC and/or the PTEN/AKT pathways, both in GSI-resistant and identified NOTCH1-independent/MYC-mediated T-ALL patients.
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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 | 0.001 | 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".