EXTH-51. BREVICAN-SPECIFIC PEPTIDES FOR THE DEVELOPMENT OF NEXT-GENERATION TARGETED THERANOSTICS FOR MALIGNANT GLIOMAS
Bibliographic record
Abstract
High-grade gliomas are deadly cancers, and current standard-of-care has demonstrated limited success. The ability to specifically target glioma cells can allow for the development of improved theranostic agents leading to better detection methods and safer anti-cancer therapies. Brevican (BCAN), a CNS-specific extracellular matrix protein is upregulated in glioma cells and its expression correlates with tumor progression. Particularly, a plasma membrane bound BCAN isoform lacking normal glycosylation, called B/bΔg is a unique glioma marker and is not expressed in non-cancerous tissues. Therefore, B/bΔg represents a valuable target for anti-cancer strategies. We describe here the discovery of novel high-affinity B/bΔg-Targeted Peptides (BTP) using rapid combinatorial library screens. Briefly, a one-bead-one-compound (OBOC) peptide library was screened against small magnetic particles decorated with B/bΔg. Positive “hit” beads labeled with magnetic particles were isolated using a high-throughput in-house microfluidic magnetic-activated sorter of our own design. These hits were exposed to cells expressing B/bΔg, and beads/peptides with the highest cell association were isolated and sequenced. Seven novel peptides were identified. Cell uptake and blocking studies revealed that 5 of these peptides displayed specific affinity for B/bΔg-overexpressing cells. These candidates displayed nano-/micromolar binding affinity for recombinant B/bΔg protein. Further analyses of these candidates using confocal microscopy revealed increased peptide binding/uptake in patient-derived glioma stem cells compared with primary human astrocytes. Flow cytometry analysis using GBM-X6 cells revealed BTP-2, 7 and 8 as the top three candidates. For in vivo evaluation, GBM-X6 tumors were established intracranially in nude mice, and tumor formation was verified by magnetic resonance imaging. We observed that upon systemic administration, BTP-7 displayed the highest and most rapid tumor uptake, followed by BTP-2 and then, BTP-8. Our data here provides strong evidence that these peptides can serve as tumor-specific agents, and offer substantial promise in the development of highly efficacious and safe brain cancer therapeutics.
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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) | 0.001 | 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) | 0.002 | 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".