Abstract B007: Unexpected synthetic lethality mechanisms in eIF4A-targeted therapy
Bibliographic record
Abstract
Abstract BACKGROUND Dysregulation of mRNA translation, which is prevalent in cancer, presents an underexplored therapeutic vulnerability. The eukaryotic translation initiation factor 4A (eIF4A) promotes protein synthesis by unwinding secondary structures in the 5’-untranslated region of mRNAs to facilitate translation initiation. While perturbation of eIF4A minimally affects the health normal cells, it is crucial for the translation of many oncogenes and is considered essential in most cancer cell lines. Zotatifin, a first-in-class eIF4A inhibitor, is currently under evaluation in a Phase I/II clinical trial for KRAS-mutant tumors and ER+ breast cancers. In addition to the tumor-intrinsic effect, our recent publication in the Journal of Clinical Investigation revealed that pharmacological targeting of eIF4A not only affects tumor cells but also improves the tumor immune microenvironment. This results in tumor inhibition, heightened sensitivity to immune checkpoint blockade, and remarkable synergism with platinum in various preclinical triple-negative breast cancer mouse models. OBJECTIVES To investigate the mechanism underlying the synergy between eIF4A-targeted therapy and platinum. UNPUBLISHED RESULT Mass spectrometry proteomic analysis coupled with ELISA and qPCR analysis unveiled that eIF4A-targeted therapy promotes type I interferon (IFN) secretion and induces a robust type I IFN response both in vivo and in vitro. This suggests a tumor cell-autonomous effect of IFN induction by eIF4A inhibition. Combining platinum with eIF4A-targeted therapy further heightens the type I IFN response in vivo, leading to T cell-and macrophage-dependent synergistic tumor regression. Interestingly, this synergy was not observed in vitro, indicating the involvement of the host immune system in the efficacy of combination therapy. It is possible that platinum may stimulate certain immune populations to secrete IFN, which acts on tumor cells to upregulate their IFN response. CONCLUSION Activation of the type I IFN response by eIF4A inhibition combined with platinum in vivo can lead to synthetic lethality of tumor cells. These studies not only offer novel insights into eIF4A biology but also provide valuable guidance for ongoing and future clinical trials. Citation Format: Na Zhao, Elena Kabotyanski, Alexander Saltzman, Jeffrey Rosen. Unexpected synthetic lethality mechanisms in eIF4A-targeted therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B007.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".