Anti-<i>hTERT</i> siRNA-Loaded Nanoparticles Block the Growth of Anaplastic Thyroid Cancer Xenograft
Bibliographic record
Abstract
Abstract The high frequency of hTERT-promoting mutations and the increased expression of hTERT mRNA in anaplastic thyroid cancer (ATC) make TERT a suitable molecular target for the treatment of this lethal neoplasm. In this study, we encapsulated an anti-hTERT oligonucleotide in biocompatible nanoparticles and analyzed the effects of this novel pharmaceutical preparation in preclinical models of ATC. Biocompatible nanoparticles were obtained in an acidified aqueous solution containing chitosan, anti-hTERT oligoRNAs, and poloxamer 188 as a stabilizer. The effects of these anti-hTERT nanoparticles (Na-siTERT) were tested in vitro on ATC cell lines (CAL-62 and 8505C) and in vivo on xenograft tumors obtained by flank injection of CAL-62 cells into SCID mice. The Na-siTERT reduced the viability and migration of CAL-62 and 8505C cells after 48-hour incubation. Intravenous administration (every 48 hours for 13 days) of this encapsulated drug in mice hosting a xenograft thyroid cancer determined a great reduction in the growth of the neoplasm (about 50% vs. untreated animals or mice receiving empty nanoparticles), and decreased levels of Ki67 associated with lower hTERT expression. Moreover, the treatment resulted in minimal invasion of nearby tissues and reduced the vascularity of the xenograft tumor. No signs of toxicity appeared following this treatment. Telomere length was not modified by the Na-siTERT, indicating that the inhibitory effects of neoplasm growth were independent from the enzymatic telomerase function. These findings demonstrate the potential suitability of this anti-TERT nanoparticle formulation as a novel tool for ATC treatment. Mol Cancer Ther; 17(6); 1187–95. ©2018 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".