Quantum Dot-Based Screening Identifies F3 Peptide and Reveals Cell Surface Nucleolin as a Therapeutic Target for Rhabdomyosarcoma
Bibliographic record
Abstract
Active drug delivery by tumor-targeting peptides is a promising approach to improve existing therapies for rhabdomyosarcoma (RMS), by increasing the therapeutic effect and decreasing the systemic toxicity, e.g., by drug-loaded peptide-targeted nanoparticles. Here, we tested 20 different tumor-targeting peptides for their ability to bind to two RMS cell lines, Rh30 and RD, using quantum dots Streptavidin and biotin-peptides conjugates as a model for nanoparticles. Four peptides revealed a very strong binding to RMS cells: NCAM-1-targeting NTP peptide, nucleolin-targeting F3 peptide, and two Furin-targeting peptides, TmR and shTmR. F3 peptide showed the strongest binding to all RMS cell lines tested, low binding to normal control myoblasts and fibroblasts, and efficient internalization into RMS cells demonstrated by the cytoplasmic delivery of the Saporin toxin. The expression of the nucleophosphoprotein nucleolin, the target of F3, on the surface of RMS cell lines was validated by competition with the natural ligand lactoferrin, by colocalization with the nucleolin-binding aptamer AS1411, and by the marked sensitivity of RMS cell lines to the growth inhibitory nucleolin-binding N6L pseudopeptide. Taken together, our results indicate that nucleolin-targeting by F3 peptide represents a potential therapeutic approach for RMS.
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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".