Abstract 1682: Lyn drives cancer metastasis via post-translational regulation of SNAI proteins
Bibliographic record
Abstract
Abstract Introduction: Metastasis is the most common cause of death from cancer and occurs when malignant cells discard epithelial restraints and acquire invasive abilities, facilitating their dissemination to permissive micro-environments. This process is enhanced by tumor cell activation of Epithelial Mesenchymal Transition (EMT), a (normally embryonic) developmental program in which epithelial cells assume a mesenchymal phenotype during gastrulation and organogenesis, allowing single cell invasive movement away from the ectodermal layer. Recent evidence strongly implicates EMT induction in malignant progression and treatment resistance. For example, EMT regulatory transcription factors are required for breast cancer metastasis. Several oncogenic pathways (growth factors, Src family, MAPK, AKT) induce EMT. Lyn tyrosine kinase, a member of Src family tyrosine kinase is up-regulated in advanced prostate cancer and has been reported to correlate with aggressive breast cancer. Our objective is to determine the role of Lyn tyrosine kinase in EMT. Methods: LNCaP (Lymph Node Metastasis of Prostate Cancer), BT-549 (Triple Negative Breast Cancer) and UM-UC-13 (Bladder Cancer) cells were transfected with Lyn siRNA; EMT markers were monitored by western blot and qRT-PCR and immunofluorescence, migration by scratch assay and invasion by Boyden chamber. Sub cellular localization of proteins was examined by IF and nuclear/cyto extraction. In vivo experiments were performed in UC13-luc cells with shRNA of Lyn. Results: Here we report that Lyn expression is low in epithelial cells and is up-regulated in mesenchymal cells. Targeting Lyn using siRNA decreases EMT markers (Fibronectin, Vimentin and Zeb-1) at both mRNA and protein levels while increasing the epithelial marker (E-cadherin). Moreover, we found that Lyn siRNA decreases cell migration and invasion. Thisis decrease in mesenchymal phenotype can be attributed to the decrease in the amount of Slug and Snail, transcriptional repressors of E-Cadherin and activator of Vimentin and Fibronectin. Interestingly, we found that Lyn knockout induces a decrease of SLUG only at protein levels and not at mRNA levels. We discovered that Lyn triggers a signaling cascade through Vav-Rac-Pak1 pathway to alter sub cellular localization of the SNAI proteins leading to their proteasomal degradation. This effect results in decreased invasion and migration in vitro as well as decreased metastasis in vivo. Conclusion: Expression of Lyn kinase can be correlated to low prognosis and aggressive/metastatic phenotype. We show that knocking down Lyn by siRNA initiates a switch to a more epithelial phenotype reducing cell migration and invasion. Citation Format: Daksh Thaper, Sepideh Vahid, Ka Mun Nip, Igor Moskalev, Sebastian Frees, Morgan E. Roberts, Krisi Ketola, Kenneth W. Harder, Jennifer L. Bishop, Amina Zoubeidi. Lyn drives cancer metastasis via post-translational regulation of SNAI proteins. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1682.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".