New docetaxel-peptide conjugate for the treatment of sortilin-positive triple-negative breast cancer.
Bibliographic record
Abstract
e12556 Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease which still lacks defined molecular biomarkers. In the last decade, targeting of specific gene/protein molecular signature of tumors emerged among the best anticancer strategies. Recently, increased expression of the Sortilin (SORT1) receptor has been reported in TNBC patients. Given SORT1 functions in protein internalization, sorting and trafficking, we developed a new peptide-anticancer drug conjugation platform to target SORT1-positive breast cancer by linking Docetaxel to a peptide (KA-peptide) that specifically targets SORT1. Methods: MDA-MB-231 cells were used as a TNBC cell model for in vitro and in vivo xenograft (CD1 nude mice) assays. Cell migration was assessed using the xCELLigence real-time system, whereas MTT assay was used for cell proliferation analysis. Apoptosis biomarkers expression was assessed by immunoblotting. Results: In MDA-MB-231, the Docetaxel-KA-peptide conjugate (DoceKA) exerted potent anti-proliferative and anti-migratory activities in vitro. DoceKA triggered faster and higher cell death mechanisms than did free Docetaxel alone. The apoptotic and anti-migratory effects were reversed by the SORT1 ligands Neurotensin and Progranulin, and upon siRNA-mediated silencing of SORT1. DoceKA altered microtubules polymerization and triggered the down-regulation of IL-6, Survivin, Bcl-xL and mutant p53 pro-survival biomarkers. In vivo, DoceKA exhibited a greater tumor regression capacity with a prolonged survival in a murine MDA-MB-231 xenograft tumor model than did Docetaxel. Conclusions: Collectively, we demonstrate that DoceKA is specifically internalized through a receptor-mediated mechanism. Such property allows for targeting SORT1-positive breast cancers, and makes DoceKA a promising novel therapy for the treatment of TNBC.
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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".