DDIS-19. NOVEL PEPTIDE HOMING TO GLIOMA-SPECIFIC ISOFORM OF BREVICAN SELECTIVELY TARGETS MALIGNANT BRAIN TUMORS
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 safer and more efficacious brain cancer therapy strategies. Brevican (BCAN), a CNS-specific extracellular matrix protein is upregulated in glioma cells and its expression correlates with tumor progression. Particularly, a 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. Here, we describe the utilization of state-of-the-art technologies that we have recently developed to discover novel “B/bΔg-Targeting Peptides” (BTP). Briefly, small magnetic beads displaying B/bΔg were used to screen a one-bead-one-compound combinatorial library, enabling high-throughput labeling of library beads presenting peptide candidates with high affinity for B/bΔg. The “hit” beads were rapidly sorted using a microfluidic magnetic-activated sorter of our own design. The hits were then, exposed to cells expressing B/bΔg, and beads with the highest cell association were isolated and sequenced. Characterization of the purified peptides through kinetic binding analysis and cell uptake studies revealed BTP-7 as the lead candidate for B/bΔg binding. BTP-7 displayed 260 nanomolar affinity for recombinant B/bΔg protein, and had little association with the fully glycosylated isoform of BCAN. Scrambling of the BTP-7 sequence led to complete abrogation of B/bΔg binding. For in vivo evaluation, GBM-6 tumors derived from human patients were established intracranially in nude mice, and tumor formation was verified by MRI. Upon systemic administration, BTP-7 displayed approximately 10x greater binding to GBM-6 tumors than the control, as well as 4x higher tumor uptake compared to normal brain tissue. We are currently working on conjugating BTP-7 to a variety of toxic payload to selectively target gliomas, with the goal of improving brain cancer therapeutic efficacy that can ultimately benefit patient.
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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) | 0.001 | 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".