Phosphorylation of eIF4E in the stroma drives the production and spatial organisation of collagen type I in the mammary gland
Bibliographic record
Abstract
The extracellular matrix (ECM) plays critical roles in breast cancer development. Whether ECM composition is regulated by the phosphorylation of eIF4E on serine 209, an event required for tumorigenesis, has not been explored. Herein, we used proteomics and mouse modeling to investigate the impact of mutating serine 209 to alanine on eIF4E (i.e., S209A) on mammary gland (MG) ECM. The proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028953. We discovered that S209A knock-in mice, expressing a non-phosphorylatable form of eIF4E, have less collagen-I deposition in native and tumor-bearing MGs, leading to altered tumor cell invasion. Additionally, phospho-eIF4E deficiency impacts collagen topology; fibers at the tumor-stroma boundary in phospho-eIF4E-deficient mice run parallel to the tumor edge but radiate outwards in wild-type mice. Finally, a phospho-eIF4E-deficient tumor microenvironment resists anti-PD-1 therapy-induced collagen deposition, correlating with an increased anti-tumor response to immunotherapy. Clinically, we showed that collagen-I and phospho-eIF4E are positively correlated in human breast cancer samples, and that stromal phospho-eIF4E expression is influenced by tumor proximity. Together, our work defines the importance of phosphorylation of eIF4E on S209 as a regulator of MG collagen architecture in the tumor microenvironment, thereby positioning phospho-eIF4E as a therapeutic target to augment response to therapy.
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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".