p66ShcA Promotes Breast Cancer Plasticity by Inducing an Epithelial-to-Mesenchymal Transition
Bibliographic record
Abstract
Breast cancers are stratified into distinct subtypes, which influence therapeutic responsiveness and patient outcome. Patients with luminal breast cancers are often associated with a better prognosis relative to that with other subtypes. However, subsets of patients with luminal disease remain at increased risk of cancer-related death. A critical process that increases the malignant potential of breast cancers is the epithelial-to-mesenchymal transition (EMT). The p66ShcA adaptor protein stimulates the formation of reactive oxygen species in response to stress stimuli. In this paper, we report a novel role for p66ShcA in inducing an EMT in HER2(+) luminal breast cancers. p66ShcA increases the migratory properties of breast cancer cells and enhances signaling downstream of the Met receptor tyrosine kinase in these tumors. Moreover, Met activation is required for a p66ShcA-induced EMT in luminal breast cancer cells. Finally, elevated p66ShcA levels are associated with the acquisition of an EMT in primary breast cancers spanning all molecular subtypes, including luminal tumors. This is of high clinical relevance, as the luminal and HER2 subtypes together comprise 80% of all newly diagnosed breast cancers. This study identifies p66ShcA as one of the first prognostic biomarkers for the identification of more aggressive tumors with mesenchymal properties, regardless of molecular subtype.
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) | 0.002 | 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".