CSIG-22. REGULATION OF GLIOMA STEM CELL SELF-RENEWAL AND TUMORIGENESIS BY CARBOHYDRATE BINDING PROTEINS
Bibliographic record
Abstract
Abstract Glioma Stem Cells (GSC) are a population of malignant self-renewing stem cells in glioblastoma (GBM) tumors, the most common and aggressive primary brain tumor in the adult brain. GSC promote tumor growth and recurrence and acquire resistance to therapy. Strategies to target GSC in the tumor bulk are urgently needed in order to develop more effective therapeutic strategies for GBM. EGFRvIII/STAT3 signaling is a major oncogenic pathway in GBM. Here, we report our discovery that Galectin1, a family member of carbohydrate-binding proteins with affinity for b-galactosides, promotes GSC self-renewal and tumor growth in EGFRvIII-expressing subset of tumors. Analysis of RNA-Seq data shows that LGALS1, the gene encoding Galectin1, is highly expressed in human EGFRvIII-expressing GSC and its expression correlates with the expression of transcription factor STAT3. Importantly, STAT3 directly binds the promoter of LGALS1 to upregulate its expression and knockdown of STAT3 significantly attenuates LGALS1 mRNA levels. Genetic knockdown of LGALS1 impairs the ability of GSC to form spheres, as assayed in limiting dilution assay, suggesting that LGALS1-STAT3 signaling regulates glioma stem cell fate in EGFRvIII tumor subset. Strikingly, we employed genetic and pharmacological approaches in patient derived xenografts and found that LGALS1/Galectin1 impairs gliomas stem cell growth and tumorigenesis. Subcutaneous and stereotactic intracranial implantation of CRISPR knockout LGALS1 EGFRvIII-expressing GSC into SCID mice showed significantly reduced in vivo tumor xenografts growth and increased survival rate compared with controls. Treatment of GSC with OTX008, a specific inhibitor of galectin-1, significantly attenuates the self-renewal ability of these cells and impairs tumorigenesis. Together, our findings suggest that targeting LGALS1/Galectin1 in combination with current standards of care may provide an effective strategy for the future treatment of these deadly brain tumors.
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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 | 0.002 |
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".