Investigating MUC1/ICAM-1 Binding Induced Signaling in Breast Cancer Metastasis
Bibliographic record
Abstract
Abstract : Breast cancer is the second leading cause of cancer death in Canadian women. To metastasize, cells must move through the stroma of the breast, enter the circulation, survive transit, exit the circulation, and form a secondary tumor. It is not fully understood how breast cancer cells gain the ability to move or what signaling pathways mediate these events, and identification of critical components of these pathways would represent potential targets for anti-metastatic therapies. The MUC1 glycoprotein is expressed on the apical membrane of normal breast epithelia. In many human breast carcinomas, MUC1 is overexpressed and loses apical polarization, events that correlate with increased metastasis. Several critical steps of the metastatic cascade require cell adhesion, and it has been reported that MUC1 is a ligand for ICAM-1, which is expressed throughout the migratory tract of a metastasizing breast cancer cell. It was subsequently reported that MUC1/ICAM-1 binding initiates calcium oscillations, cytoskeletal reorganization, and cell migration, suggesting that binding could be important in metastasis. Here, we investigate the mechanism of MUC1/ICAM-1 binding induced signaling. We show that MUC1 forms constitutive dimers which are required for Src recruitment and ICAM-1 binding induced signaling. We show that MUC1 dimers are not covalently linked and do not require cytoplasmic domain cysteine residues. These results reveal information on the mechanism of MUC1/ICAM-1 signalling, which can be used to identify novel targets and combinational strategies for anti-metastatic therapy in breast cancer.
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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) | 0.002 | 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".