Afadin cooperates with Claudin-2 to promote breast cancer metastasis
Bibliographic record
Abstract
Claudin-2 promotes breast cancer liver metastasis by enabling seeding and early cancer cell survival. We now demonstrate that the PDZ-binding motif of Claudin-2 is necessary for anchorage-independent growth of cancer cells and is required for liver metastasis. Several PDZ domain-containing proteins were identified that interact with the PDZ-binding motif of Claudin-2 in liver metastatic breast cancer cells, including Afadin, Arhgap21, Pdlim2, Pdlim7, Rims2, Scrib, and ZO-1. We specifically examined the role of Afadin as a potential Claudin-2-interacting partner that promotes breast cancer liver metastasis. Afadin associates with Claudin-2, an interaction that requires the PDZ-binding motif of Claudin-2. Loss of Afadin also impairs the ability of breast cancer cells to form colonies in soft agar and metastasize to the lungs or liver. Immunohistochemical analysis of Claudin-2 and/or Afadin expression in 206 metastatic breast cancer tumors revealed that high levels of both Claudin-2 and Afadin in primary tumors were associated with poor disease-specific survival, relapse-free survival, lung-specific relapse, and liver-specific relapse. Our findings indicate that signaling downstream from a Claudin-2/Afadin complex enables the efficient formation of breast cancer metastases. Moreover, combining Claudin-2 and Afadin as prognostic markers better predicts the potential of breast cancer to metastasize to soft tissues.
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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".