Lyn modulates Claudin-2 expression and is a therapeutic target for breast cancer liver metastasis
Bibliographic record
Abstract
// Sébastien Tabariès 1, 2 , Matthew G. Annis 1, 2 , Brian E. Hsu 1, 2 , Christine E. Tam 1, 2 , Paul Savage 1, 2 , Morag Park 1, 2, 3, 4 , Peter M. Siegel 1, 2, 3 1 Goodman Cancer Research Centre, McGill University, Montréal, Québec, Canada, H3A 1A3 2 Department of Medicine, McGill University, Montréal, Québec, Canada, H3A 1A3 3 Department of Biochemistry, McGill University, Montréal, Québec, Canada, H3A 1A3 4 Department of Oncology, McGill University, Montréal, Québec, Canada, H3A 1A3 Correspondence to: Peter M. Siegel, e-mail: peter.siegel@mcgill.ca Keywords: breast cancer, liver metastasis, claudins, Src family kinase, Lyn Received: January 26, 2015 Accepted: January 31, 2015 Published: March 25, 2015 ABSTRACT Claudin-2 enhances breast cancer liver metastasis and promotes the development of colorectal cancers. The objective of our current study is to define the regulatory mechanisms controlling Claudin-2 expression in breast cancer cells. We evaluated the effect of several Src Family Kinase (SFK) inhibitors or knockdown of individual SFK members on Claudin-2 expression in breast cancer cells. We also assessed the potential effects of pan-SFK and SFK-selective inhibitors on the formation of breast cancer liver metastases. This study reveals that pan inhibition of SFK signaling pathways significantly elevated Claudin-2 expression levels in breast cancer cells. In addition, our data demonstrate that pan-SFK inhibitors can enhance breast cancer metastasis to the liver. Knockdown of individual SFK members reveals that loss of Yes or Fyn induces Claudin-2 expression; whereas, diminished Lyn levels impairs Claudin-2 expression in breast cancer cells. The Lyn-selective kinase inhibitor, Bafetinib (INNO-406), acts to reduce Claudin-2 expression and suppress breast cancer liver metastasis. Our findings may have major clinical implications and advise against the treatment of breast cancer patients with broad-acting SFK inhibitors and support the use of Lyn-specific inhibitors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".