Abstract 2524: STAT3 is a key regulator of an “EMT-like” process mediated by Slug in GBM
Bibliographic record
Abstract
Abstract Glioblastoma Multiforme (GBM) is the most aggressive subtype of adult brain tumor with a median survival of 15 months. Despite a combination of maximal safe resection, radiation and chemotherapy, GBM invariably recurs, highlighting the need to better delineate the basis of recurrent disease and develop novel, more targeted and effective therapies. The Signal Transducer and Activator of Transcription 3 (STAT3) has been implicated in a proneural to mesenchymal shift associated with the emergence of a more aggressive, more resistant GBM phenotype at recurrence. Brain Tumor Initiating Cells (BTICs), defined by the key features of self-renewal, multipotency and tumorigenic potential, are integral players of recurrence post-treatment and represent a “reservoir of disease” that needs to be specifically targeted if GBM outcome is to be improved. Analysis of GBM patient transcriptomic data from The Cancer Genome Atlas (TCGA) shows that an Epithelial to Mesenchymal Transition (EMT) gene signature is particularly enriched in the mesenchymal subtype, tightly correlates with STAT3 activity and is associated with shorter survival. The key EMT transcription factor Slug is also highly expressed in mesenchymal samples and associated with poorer prognosis. Interestingly, we found stronger expression of EMT transcription factors Snail, Slug and Twist in faster proliferating, more aggressive BTIC lines. Noteworthy, Slug was more highly expressed in both BTICs and parental tumors compared to Snail and Twist and positively correlated with pSTAT3. We also found that Slug expression correlates with faster growth in vitro and shorter survival in orthotopic xenografts. Conversely, higher E-cadherin expression correlates with slower growth and longer survival of xenografted mice. While Slug is not a known STAT3 target gene (unlike Twist and Snail), we show that Slug expression is decreased after pharmacological inhibition of STAT3 signaling in BTICs. In contrast, activation of the STAT3 pathway via growth factor/cytokine treatment (EGF, OSM), as well as expression of a constitutively active form of STAT3 promotes Slug expression. We have identified a potential STAT3 consensus binding site in the Slug promoter and preliminary Chromatin Immuno Precipitation (ChIP) experiments suggest that Slug is a novel direct transcriptional target of STAT3. Over-expression of Slug in BTIC lines triggered down-regulation of E-cadherin and resulted in increased Cyclin D1 and pRB protein levels. However, we found that Cyclin D1 RNA levels remain unchanged suggesting that overexpression of Slug leads to the post-transcriptional stabilization of Cyclin D1 potentially via repression of UbcH5C. To conclude, STAT3 is a key regulator of an EMT-like process in GBM BTICs, mediated at least in part by Slug. Our results suggest that STAT3 and the key regulator Slug may be involved in the promotion of a more aggressive GBM phenotype and represent interesting therapeutic targets in GBM. Citation Format: Charles Chesnelong, H. Artee Luchman, J. Gregory Cairncross, Samuel Weiss. STAT3 is a key regulator of an “EMT-like” process mediated by Slug in GBM. [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 2524.
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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) | 0.005 | 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".