Abstract A024: A potent eIF4A1/2 inhibitor CR-1-31B down-modulates the antioxidant stress response in osteosarcoma cells and inhibits <i>in vivo</i> lung metastases
Bibliographic record
Abstract
Abstract Background: Effective treatment of metastatic disease remains a major challenge in the improvement of patient outcomes in osteosarcoma (OS). Novel anti-metastatic therapies are needed to treat distant metastases. To this end, the current research evaluates whether targeting the dysregulated mRNA translation machinery in OS can inhibit metastases. Hypothesis: We hypothesize that mRNA translation factors present at an abnormally high abundance in OS cells support the rapid synthesis of cytoprotective proteins that are needed to survive in the oxidative stress-rich microenvironment of the lung. Experimental Approach: Databases of OS cell lines and patient tumor data (A. Sweet-Cordero, UCSF) were queried to identify mRNA translation factors with abnormal transcript levels. A limited drug screen of small molecule inhibitors (SMIs) against identified candidates was carried out to evaluate IC50 values in metastatic OS cells. A candidate inhibitor identified from these data was further characterized for synergy with chemical inducers of oxidative stress (e.g. tert-butylhydroquinone [tBHQ]) that mimics conditions encountered in the lung. Drug combination studies examined 2D and 3D tumor spheroid growth, cellular oxidative stress, and PARP-cleavage, under +/- inhibitor and +/- oxidative stress conditions. Metastatic OS cells were engineered to express an antioxidant response element (ARE)-mCherry fluorescent reporter to directly monitor the antioxidant response by fluorescence microscopy. Polysome profiling was used to assess inhibitor-mediated changes in global mRNA translation. The anti-metastatic activity of the inhibitor was tested in the ex vivo pulmonary metastasis assay (PuMA) and in in vivo metastasis models. Results: From cell and patient sample screening, eIF4A1/2 was identified as being abnormally regulated in metastatic OS cells. The SMI, CR-1-31B, specifically targets eIF4A1/2 and was found to have an IC50 of just 8 nM. CR-1-31B was found to inhibit tumor cell growth in 2D and 3D, increase cellular oxidative stress, and enhance PARP-cleavage, but only under oxidative stress conditions. Western analysis of tBHQ-treated metastatic OS cells with the ARE-mCherry reporter confirmed that the temporal expression of mCherry correlated with the upregulation of Nuclear factor erythroid 2-related factor-2 (Nrf2), a key transcriptional regulator of the antioxidant response. CR-1-31B blunted the upregulation of the antioxidant response in 2D and 3D tumor growth conditions with oxidative stress. CR-1-31B, in a dose-dependent manner, decreased the amount of polysomal mRNAs. CR-1-31B reduced the lung tumor burden in the ex vivo PuMA model, delayed primary tumor growth, and reduced lung metastases in in vivo xenograft OS models. Conclusions: Our data demonstrates that dysregulated mRNA translation is a metastatic vulnerability that can be exploited with SMIs. Altogether, these data support the inhibition of metastatic OS by CR-1-31B, highlighting the potential therapeutic utility of this selective translation inhibitor. Citation Format: Michael M. Lizardo, Christopher Hughes, Yue Zhou Huang, Alberto Delaidelli, Taras Shyp, Haifeng Zhang, Sol Snir Shaool, Poul H. Sorensen. A potent eIF4A1/2 inhibitor CR-1-31B down-modulates the antioxidant stress response in osteosarcoma cells and inhibits in vivo lung metastases [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A024.
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".