CBIO-21. STAT3 REGULATES AN SL/PL TRANSITION IN BTICs THROUGH AN EMT-LIKE PROCESS MEDIATED BY SLUG
Bibliographic record
Abstract
The Signal Transducer and Activator of Transcription 3 (STAT3) is essential for GBM progression and crucial for invasion, proliferation and survival of Brain Tumor Initiating Cells (BTICs). STAT3 may drive a putative Proneural to Mesenchymal transition (PMT) associated with a more aggressive GBM phenotype at recurrence. Interestingly, the previously identified Stem–Like (SL) BTICs associate with the Proneural subtype while Progenitor-Like (PL) BTICs resemble the Mesenchymal subtype. This finding suggests a possible shift from slower growing SL-BTICs towards a more aggressive PL phenotype, mirroring the PMT observed in GBM. We thus propose that STAT3 may regulate an SL/PL transition in BTICs and a PMT in GBM through an Epithelial to Mesenchymal Transition (EMT)-like process. Here, we confirm that STAT3 and EMT pathways are strongly over-activated in PL-BTICs. We further highlight SLUG as the main mediator of this transition as it is the most highly expressed EMT regulator in BTICs and its expression is strikingly correlated with both STAT3/EMT pathways and the SL/PL state. Further, we show that SLUG expression is decreased after STAT3 inhibition, increased following STAT3 activation and confirm by ChIP that SLUG is a novel direct transcriptional target of STAT3. We also confirm that SLUG over-expression leads to E-cadherin down-regulation and promotes migrative abilities. Finally, using The Cancer Genome Atlas (TCGA) transcriptomic data, we demonstrate that STAT3 and EMT activity scores are tightly correlated, enriched in mesenchymal GBM samples and predict poorer survival. Most importantly, SLUG expression also correlates with STAT3 and EMT scores, is enriched in mesenchymal samples and is associated with poor patient outcome. The STAT3/EMT axis may regulate stem-like characteristics, proliferation, invasion and resistance, promoting a more aggressive mesenchymal GBM phenotype via an EMT-like process mediated by SLUG. Blocking this process represents a novel actionable therapeutic strategy in GBM with clinically relevant JAK2 and STAT3 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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".