GLIOMA SPECIFIC PEPTIDES: A PLATFORM FOR MOLECULAR IMAGING AND THERAPEUTIC TARGETING
Bibliographic record
Abstract
BACKGROUND: Despite intense investigation, the ability to treat high-grade glioma (HGG) has remained dismal, in part due to molecular and phenotypic heterogeneity. The cancer stem cell, a cell believed to be the clonogenic core of tumors including human glioma, has been a major focus for the development of new therapies but emerging evidence has exposed the dynamic and heterogenous characteristics of this stem-like population that facilitates evasion from current therapies. METHODS: To capture and target the complexity and heterogeneity of glioma, we employed a combinatorial phage-display biopanning strategy to isolate peptides that specifically bind and home in vivo to key disease reservoirs within glioma; namely the invasive and stem-like populations of glioma cells (GSC). Synthetic peptides, individually or in combination, were conjugated to gadolinium or a chemotherapeutic agent and administered to animals bearing orthotopic tumors established from human glioma-like-stem cells. RESULTS: Using this approach we identified a panel of peptides with the ability to detect the heterogeneity of patient gliomas in vivo successfully imaging a range of tumors including diffuse infiltrating tumours that are otherwise invisible by conventional imaging technologies. In addition, we identified peptides with multifunctional capabilities including peptides that functionally block glioma invasion, define a subpopulation of GSCs, and target chemotherapeutic agents in vivo. CONCLUSIONS: These data highlight the utility of the identified peptides as platforms for the precise and sensitive imaging of these heterogenous tumors and for the development of molecularly targeted therapeutics. SECONDARY CATEGORY: Tumor Biology.
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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 | 0.001 | 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".