Abstract P2-07-03: Pax-5 regulates EMT and MET in breast cancer through FAK1 regulation
Bibliographic record
Abstract
Abstract Metastasis accounts for 90% of deaths in breast cancers patients. Therefore, the study of genetic factors regulating cancer malignancy is a top priority to mitigate the morbidity and mortality associated to this disease. One of these factors, Pax-5, normally regulates key biological functions such as cell viability, growth, and differentiation. However, an aberrant expression of this factor results in the development and progression of cancer. In this study, we developed breast cancer cell models with conditioned expression of the Pax-5 to evaluate signaling pathways relevant to breast metastasis and cancer progression. We found that Pax-5 extinguishes several aspects of cancer aggressivety such as: proliferation, spheroid formation, migration and invasion. At the molecular level, we found that Pax-5 modulates cancer malignancy through the regulation of various components of the epithelial to mesenchymal transitioning (EMT) process in addition to key signaling targets such as: NFκB and the Focal Adhesion Kinase 1 (FAK1). We also demonstrate that Pax-5 decreases FAK1 level trough up-regulation of miR-135b, a direct repressor of FAK1 expression. Altogether, our findings suggest that the presence of the Pax-5 lead to less aggressive breast cancers by promoting mesenchymal to Epithelial transitioning (MET). These findings bring light to molecular mechanisms driving breast cancer malignancy and benefit our quest in the development of diagnostic and therapeutic strategies against breast cancer progression. Citation Format: Sami Benzina, Pierre O'Brien, Annie-Pier Beauregard, Roxann Guérrette, Stéphanie Jean, Gilles Robichaud. Pax-5 regulates EMT and MET in breast cancer through FAK1 regulation [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-07-03.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.008 |
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".