The core autophagy protein ATG4B is a potential biomarker and therapeutic target in CML stem/progenitor cells
Bibliographic record
Abstract
Previous studies demonstrated that imatinib mesylate (IM) induces autophagy in chronic myeloid leukemia (CML) and that this process is critical to cell survival upon therapy. However, it is not known if the autophagic process differs at basal levels between CML patients and healthy individuals and if pretreatment CML cells harbor unique autophagy characteristics that could predict patients' clinical outcomes. We now demonstrate that several key autophagy genes are differentially expressed in CD34(+) hematopoietic stem/progenitor cells, with the highest transcript levels detected for ATG4B, and that the transcript and protein expression levels of ATG4 family members, ATG5 and BECLIN-1 are significantly increased in CD34(+) cells from chronic-phase CML patients (P < .05). Importantly, ATG4B is differentially expressed in pretreatment CML stem/progenitor cells from subsequent IM responders vs IM nonresponders (P < .05). Knockdown of ATG4B suppresses autophagy, impairs the survival of CML stem/progenitor cells and sensitizes them to IM treatment. Moreover, deregulated expression of ATG4B in CD34(+) CML cells inversely correlates with transcript levels of miR-34a, and ATG4B is shown to be a direct target of miR-34a. This study identifies ATG4B as a potential biomarker for predicting therapeutic response in treatment-naïve CML stem/progenitor cells and uncovers ATG4B as a possible drug target in these cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".