STEM-17. CHARACTERIZATION OF THE CELL SURFACE PROTEOME IN RECURRENT GLIOBLASTOMA INITIATING CELLS
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in humans, bearing an overwhelmingly poor prognosis and characterized by a diverse cellular phenotype and genetic heterogeneity. Despite multimodal therapy, patients on average experience relapse at 9 months and median survival rarely extends beyond 15 months. Treatment of human GBM with standard-of-care (SOC) chemo-radiotherapy invariably leads to resistance. Poor patient survival correlates with marker expression of brain tumor-initiating cells (BTICs), which are implicated in therapeutic resistance and may explain tumor relapse. Targeting the cells that drive GBM formation as well as its inevitable and rapid recurrence post-therapy has remained a major challenge, likely due to intra-tumoral heterogeneity. Proteomic profiling constitutes a promising tool to identify novel cancer targets. In this study, we have adopted a non-biased mass spectrometry approach to identify cell surface peptides isolated from patient-derived GBM BTIC lines through a glycocapture approach. We have identified proteins that are differentially expressed in recurrent GBM BTICs compared to primary GBM BTICs. Moreover, we perform comparative proteomics from recurrent GBM BTICs generated from our in vivo mouse-adapted therapy model, which has the distinct advantage of generating recurrent, treatment-refractory GBM. Our surface proteomic profiling thus provides a comprehensive network of signaling molecules selectively enriched in recurrent GBM cells. Identification of cell surface proteins that may drive treatment resistance will contribute greatly to identify novel immunotherapeutic targets for GBM.
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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) | 0.001 | 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".