Abstract PR011: Inhibiting eIF4E phosphorylation sensitizes triple-negative breast cancer to CDK4/6 inhibition
Bibliographic record
Abstract
Abstract Despite significant progress in breast cancer treatment, Triple-Negative Breast Cancer (TNBC) presents a unique challenge due to the absence of three conventional drug targets found in other breast cancer subtypes, highlighting an urgent need for novel therapeutic strategies. A critical element of TNBC pathogenesis is the dysregulation of mRNA translation, a critical process required by rapidly dividing cancer cells to synthesize proteins as building blocks. This hallmark positions mRNA translation as a promising target for therapeutic intervention. At the forefront of regulating mRNA translation, is the mRNA cap-binding protein, eukaryotic initiation factor 4E (eIF4E). While its role in general mRNA translation initiation is crucial, eIF4E phosphorylation by the MNK1/2 [MAPK (Mitogen-Activated Protein Kinase)-Interacting Kinase1/2] has been implicated in tumorigenesis. Specifically, phosphorylated eIF4E (p-4E) selectively enhances the translation of mRNAs encoding proteins critical for cancer cell survival and metastasis, such as myeloid cell leukemia-1 (MCL1) and matrix metalloproteinase-3 (MMP3). Yet, inhibiting eIF4E phosphorylation markedly impairs TNBC metastasis without affecting the primary tumor’s growth rates in vivo, emphasizing its specific role in metastatic progression. To dissect the mechanism of p-4E in TNBC, we performed an in vitro shRNA synthetic lethal screen in TNBC cells to identify genes whose knockdown synergizes with p-4E inhibition to limit cell growth. A small-molecule inhibitor of MNK1/2, eFT508, was utilized to mitigate p-4E level during the screen. This screen uncovered that knockdown of cyclin-dependent kinase 4 (CDK4), known primarily for its role in cell cycle regulation, synergizes with p-4E inhibition. Through validation using various cell proliferation assays and three different FDA-approved CDK4/6 inhibitors (palbociclib, ribociclib, and abemaciclib), we demonstrated a strong synergy between CDK4/6 inhibition and eFT508 in reducing cell growth. The synergy between CDK4/6 and p-4E inhibition not only impaired TNBC cell proliferation, but also significantly reduced eIF4E activity and mRNA translation. Additionally, our findings reveal a novel role for CDK4/6 inhibitors in modulating mRNA translation via mTORC1 signaling pathways, extending beyond their canonical role in cell cycle regulation. This discovery suggests a potential combinatorial therapeutic approach for TNBC treatment involving MNK1/2 and CDK4/6 inhibitors. Such a strategy could exploit the vulnerabilities in TNBC’s dysregulated mRNA translation machinery, offering a promising direction in treating this aggressive breast cancer subtype. Citation Format: Qiyun Deng, Mehdi Amiri, Anastasija Ana Piric, Yasaman Bagherian, Zilan Li, Sidong Huang, Michael Pollak, Nahum Sonenberg. Inhibiting eIF4E phosphorylation sensitizes triple-negative breast cancer to CDK4/6 inhibition [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 PR011.
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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 | 0.000 |
| 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) | 0.005 | 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".