Pharmacological activation of SIRT6 suppresses progression of head and neck and esophageal squamous cell carcinoma by modulation of cellular metabolism and protein translation
Bibliographic record
Abstract
Sirtuin 6 (SIRT6), a NAD+-dependent histone deacetylase, has been shown to function as a tumor suppressor in several cancer types, including squamous cell carcinoma of the head and neck and the esophagus (HNSCC and ESCC). However, the potential of therapies involving the activation of SIRT6 in HNSCC and ESCC remains unexplored. In this work, we investigated the therapeutic potential and mechanisms of action of the allosteric SIRT6 activator MDL-800 in HNSCC and ESCC models. MDL-800 treatment inhibited the proliferation and migration of HNSCC and ESCC cell lines in vitro, and delayed tumor growth in cell-derived xenograft models, in vivo. Mechanistically, global H3K9ac acetylation profiling and protein arrays demonstrated that MDL-800 treatment potently inhibits glucose metabolism and protein translation by inhibiting mTOR signaling, E2F-related G1/S transcription, ribosomal protein S6 (S6) and 4E-BP1 activity. This inhibition of mTOR induces a compensatory feedback loop activating IGF-1R/INSR signaling, limiting the anti-tumor activity of MDL-800. Combination of MDL-800 with the alpha-specific PI3K inhibitor (BYL719/Alpelisib) disrupted this feedback loop activation and resulted in a synergistic anti-proliferative effect. In vivo, the combined treatment of MDL-800 and BYL719 resulted in a prolonged anti-tumor response. Overall, our study identifies the molecular mechanism underlying SIRT6 activation in HNSCC and ESCC and highlights the therapeutic potential of SIRT6 activators, alone or in combination with PI3K inhibitors in cancers where SIRT6 is downregulated or serves as a tumor suppressor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".