Arsenic Trioxide Overcomes Rapamycin-Induced Feedback Activation of AKT and ERK Signaling to Enhance the Anti-Tumor Effects in Breast Cancer
Bibliographic record
Abstract
Inhibitors of the mammalian target of rapamycin (mTORi) have clinical activity; however, the benefits of mTOR inhibition by rapamycin and rapamycin-derivatives (rapalogs) may be limited by a feedback mechanism that results in AKT activation. Increased AKT activity resulting from mTOR inhibition can be a result of increased signaling via the mTOR complex, TORC2. Previously, we published that arsenic trioxide (ATO) inhibits AKT activity and in some cases, decreases AKT protein expression. Therefore, we propose that combining ATO and rapamycin may circumvent the AKT feedback loop and increase the anti-tumor effects. Using a panel of breast cancer cell lines, we find that ATO, at clinically-achievable doses, can enhance the inhibitory activity of the mTORi temsirolimus. In all cell lines, temsirolimus treatment resulted in AKT activation, which was decreased by concomitant ATO treatment only in those cell lines where ATO enhanced growth inhibition. Treatment with rapalog also results in activated ERK signaling, which is decreased with ATO co-treatment in all cell lines tested. We next tested the toxicity and efficacy of rapamycin plus ATO combination therapy in a MDA-MB-468 breast cancer xenograft model. The drug combination was well-tolerated, and rapamycin did not increase ATO-induced liver enzyme levels. In addition, combination of these drugs was significantly more effective at inhibiting tumor growth compared to individual drug treatments, which corresponded with diminished phospho-Akt and phospho-ERK levels when compared with rapamycin-treated tumors. Therefore, we propose that combining ATO and mTORi may overcome the feedback loop by decreasing activation of the MAPK and AKT signaling pathways.
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 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 | 0.001 |
| 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".