NTAL is associated with treatment outcome, cell proliferation and differentiation in acute promyelocytic leukemia
Bibliographic record
Abstract
Non-T cell activation linker (NTAL) is a lipid raft-membrane protein expressed by normal and leukemic cells and involved in cell signaling. In acute promyelocytic leukemia (APL), NTAL depletion from lipid rafts decreases cell viability through regulation of the Akt/PI3K pathway. The role of NTAL in APL cell processes, and its association with clinical outcome, has not, however, been established. Here, we show that reduced levels of NTAL were associated with increased all-trans retinoic acid (ATRA)-induced differentiation, generation of reactive oxygen species, and mitochondrial dysfunction. Additionally, NTAL-knockdown (NTAL-KD) in APL cell lines led to activation of Ras, inhibition of Akt/mTOR pathways, and increased expression of autophagy markers, leading to an increased apoptosis rate following arsenic trioxide treatment. Furthermore, NTAL-KD in NB4 cells decreased the tumor burden in (NOD scid gamma) NSG mice, suggesting its implication in tumor growth. A retrospective analysis of NTAL expression in a cohort of patients treated with ATRA and anthracyclines, revealed that NTAL overexpression was associated with a high leukocyte count (P = 0.007) and was independently associated with shorter overall survival (Hazard Ratio: 3.6; 95% Confidence Interval: 1.17-11.28; P = 0.026). Taken together, our data highlights the importance of NTAL in APL cell survival and response to treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".