Impact of Epithelial–Mesenchymal Transition on the Immune Landscape in Breast Cancer
Bibliographic record
Abstract
The impact of epithelial-mesenchymal transition (EMT) signature on the immune infiltrate present in the breast cancer tumor microenvironment (TME) is still poorly understood. Since there is mounting interest in the use of immunotherapy for the treatment of subsets of breast cancer patients, it is of major importance to understand the fundamental tumor characteristics which dictate the inter-tumor heterogeneity in immune landscapes. We aimed to assess the impact of EMT-related markers on the nature and magnitude of the inflammatory infiltrate present in breast cancer TME and their association with the clinicopathological parameters. Tissue microarrays were constructed from 144 formalin-fixed paraffin-embedded invasive breast cancer tumor samples. The protein expression patterns of Snail, Twist, ZEB1, N-cadherin, Vimentin, GRHL2, E-cadherin, and EpCAM were examined by immunohistochemistry (IHC). The inflammatory infiltrate in the TME was assessed semi-quantitatively on hematoxylin and eosin (H&E)-stained whole sections and was characterized using IHC. The inflammatory infiltrate was more intense in poorly differentiated carcinomas and triple-negative carcinomas in which the expression of E-cadherin and GRHL2 was reduced, while EpCAM was overexpressed. Most EMT-related markers correlated with plasma cell infiltration of the TME. Taken together, our findings reveal that the EMT signature might impact the immune response in the TME.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".