HDAC6 differentially regulates autophagy in stem-like versus differentiated cancer cells
Bibliographic record
Abstract
Cancer stem-like cells (CSCs), a small population of pluripotent cells residing within heterogeneous tumor mass, remain highly resistant to various chemotherapies as compared to the differentiated cancer cells. It is being postulated that CSCs possess unique molecular mechanisms, such as autophagic homeostasis, that allow CSCs to withstand the therapeutic assaults. Here we demonstrate that HDAC6 inhibition differentially modulates macroautophagy/autophagy in CSCs as compared to that of differentiated cancer cells. Using human and murine CSC models and differentiated cells, we show that the inhibition or knockdown (KD) of HDAC6 decreases CSC pluripotency by downregulating major pluripotency factors POU5F1, NANOG and SOX2. This decreased HDAC6 expression increases ACTB, TUBB3 and CSN2 expression and promotes differentiation in CSCs in an apoptosis-independent manner. Mechanistically, HDAC6 KD in CSCs decreases pluripotency by promoting autophagy, whereas the inhibition of pluripotency via retinoic acid treatment, POU5F1 or autophagy-related gene (ATG7 and ATG12) KD in CSCs decreases HDAC6 expression and promotes differentiation. Interestingly, HDAC6 KD-mediated CSC growth inhibition is further enhanced in the presence of autophagy inducers Tat-Beclin 1 peptide and rapamycin. In contrast to the results observed in CSCs, HDAC6 KD in differentiated breast cancer cells downregulates autophagy and increases apoptosis. Furthermore, the autophagy regulator p-MTOR, upstream negative regulators of p-MTOR (TSC1 and TSC2) and downstream effectors of p-MTOR (p-RPS6KB and p-EIF4EBP1) are differentially regulated in CSCs versus differentiated cancer cells following HDAC6 KD. Overall these data identify the differential regulation of autophagy as a molecular link behind the differing chemo-susceptibility of CSCs and differentiated cancer 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) | 0.000 | 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".