EXTH-52. CO-TARGETING ONCOSTATIN M RECEPTOR USING MONOCLONAL ANTIBODIES IN COMBINATION WITH PRESENT STANDARDS OF CARE FOR GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma Multiform (GBM) is the most aggressive primary tumor in the adult brain. The present standard of care includes surgical removal of the tumors followed by treatment with Temozolomide (TMZ) and radiation therapy. Despite intense efforts and advances in surgery and combination therapy, the median survival rate for GB patients remain 16 months following diagnosis. EGFRvIII/STAT3 signaling plays critical roles in GBM pathogenesis. We have recently discovered that the tumorigenic capacity of EGFRvIll/STAT3 pathway crucially depends on the cytokine receptor for Oncostatin M (OSMR). OSMR is a required co-receptor of EGFRvIII and a direct transcriptional target of STAT3. Strikingly, OSMR is highly expressed in brain tumor stem cells (BTSCs), a population of self-renewing malignant stem cells within GBM that contribute to tumor growth, recurrence and therapeutic resistance. We have generated therapeutic monoclonal antibodies against OSMR (OSMR mAb) that function as powerful inhibitors of OSMR. Treatment of BTSCs with OSMR mAb resulted in inhibition of OSMR-mediated signaling pathways and significantly impaired the phosphorylation of STAT3. Strikingly, treatment of BTSCs with OSMR mAb significantly attenuated BTSC self-renewal in limiting dilution assay and sensitized their response to tyrosine kinase inhibitors (TKI), ionizing radiation and TMZ. Using patient derived stem cell tumor xenografts, we have shown that OSMR mAb significantly reduced tumor growth compared to IgG control. Together, our findings suggest that targeting OSMR using therapeutic monoclonal antibodies in combination with EGFR inhibitors and/ or the current standard of care may provide a promising therapeutic strategy for glioblastoma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".