Abstract 1000: E-cadherin and EGFR interactions result in hyper-proliferation via ERK signaling in breast cancer
Bibliographic record
Abstract
Abstract The loss of the intercellular adhesion molecule E-cadherin is a hallmark of the epithelial-mesenchymal transition (EMT), during which tumor cells transition into an invasive phenotype. Thus, E-cadherin has long been considered a tumor suppressor gene. However, recent studies have provided evidence that E-cadherin may promote metastasis rather than suppress it, suggesting oncogenic behavior. Here we provide data that E-cadherin plays an oncogenic role in breast cancer by promoting a hyper-proliferative phenotype in breast cancer cells via interaction with EGFR. This interaction results in the activation of the MEK/ERK signaling pathway, leading directly to changes in proliferation via transcription factors such as c-Fos. Pharmacological inhibition of MEK in E-cadherin positive breast cancer cells significantly decreases both tumor growth and macro-metastasis in vivo. This work provides evidence for a novel role of E-cadherin in breast tumor progression and identifies a potential new target to treat hyper-proliferative E-cadherin-positive breast tumors. Citation Format: Gabriella C. Russo, Ashleigh Crawford, David J. Clark, Julie Cui, Ryan Carney, Michelle N. Karl, Boyang Su, Batrholomew Starich, Tung-Shing Lee, Qiming Zhang, Pei-hsun Wu, Meng-Horng Lee, Hon S. Leong, Vito Rebecca, Hui Zhang, Denis Wirtz. E-cadherin and EGFR interactions result in hyper-proliferation via ERK signaling in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1000.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".