The MNK1/2–eIF4E Axis Supports Immune Suppression and Metastasis in Postpartum Breast Cancer
Bibliographic record
Abstract
Abstract Breast cancer diagnosed within 10 years following childbirth is defined as postpartum breast cancer (PPBC) and is highly metastatic. Interactions between immune cells and other stromal cells within the involuting mammary gland are fundamental in facilitating an aggressive tumor phenotype. The MNK1/2–eIF4E axis promotes translation of prometastatic mRNAs in tumor cells, but its role in modulating the function of nontumor cells in the PPBC microenvironment has not been explored. Here, we used a combination of in vivo PPBC models and in vitro assays to study the effects of inactivation of the MNK1/2–eIF4E axis on the protumor function of select cells of the tumor microenvironment. PPBC mice deficient for phospho-eIF4E (eIF4ES209A) were protected against lung metastasis and exhibited differences in the tumor and lung immune microenvironment compared with wild-type mice. Moreover, the expression of fibroblast-derived IL33, an alarmin known to induce invasion, was repressed upon MNK1/2–eIF4E axis inhibition. Imaging mass cytometry on PPBC and non-PPBC patient samples indicated that human PPBC contains phospho-eIF4E high–expressing tumor cells and CD8+ T cells displaying markers of an activated dysfunctional phenotype. Finally, inhibition of MNK1/2 combined with anti–PD-1 therapy blocked lung metastasis of PPBC. These findings implicate the involvement of the MNK1/2–eIF4E axis during PPBC metastasis and suggest a promising immunomodulatory route to enhance the efficacy of immunotherapy by blocking phospho-eIF4E. Significance: This study investigates the MNK1/2–eIF4E signaling axis in tumor and stromal cells in metastatic breast cancer and reveals that MNK1/2 inhibition suppresses metastasis and sensitizes tumors to anti–PD-1 immunotherapy.
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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) | 0.000 | 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 | 0.000 |
| 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".