Lovastatin‐induced apoptosis is mediated by activating transcription factor 3 and enhanced in combination with salubrinal
Bibliographic record
Abstract
We have previously demonstrated the ability of lovastatin, a potent inhibitor of mevalonate synthesis, to induce tumor-specific apoptosis. The apoptotic effects of lovastatin were regulated in part by the integrated stress response (ISR) that regulates cellular responses to a wide variety of stress inducers. A key regulator of the ISR apoptotic response is activating transcription factor 3 (ATF3) and its target gene CHOP/GADD153. In our study, we demonstrate that in multiple lovastatin-resistant clones of the squamous cell carcinoma (SCC) cell line SCC9, lovastatin treatment (1-25 μM, 24 hr) in contrast to the parental line failed to significantly induce ATF3 expression. Furthermore, the SCC-derived cell lines SCC25 and HeLa that are sensitive to lovastatin-induced apoptosis also preferentially induce ATF3 expression compared to resistant breast (MCF-7) and prostate carcinoma (PC3)-derived cell lines. In HeLa cells shRNA targeting ATF3 expression as well as in ATF3-deficient murine embryonic fibroblasts, lovastatin-induced cytotoxicity and apoptosis were attenuated. In ex vivo HNSCC tumors, lovastatin also induced ATF3 mRNA expression in two of four tumors evaluated. Salubrinal, an agent that can sustain the activity of a key regulator of the ISR eIF2α, further increased the expression of ATF3 and demonstrated synergistic cytotoxicity in combination with lovastatin in SCC cells. Taken together, our results demonstrate preferential induction of ATF3 in lovastatin-sensitive tumor-derived cell lines that regulate lovastatin-induced apoptosis. Importantly, combining lovastatin with salubrinal enhanced ATF3 expression and induced synergistic cytotoxicity in SCC 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 | 0.001 |
| 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".