Mechanism and Efficacy of Sub–50-nm Tenfibgen Nanocapsules for Cancer Cell–Directed Delivery of Anti-CK2 RNAi to Primary and Metastatic Squamous Cell Carcinoma
Bibliographic record
Abstract
Improved survival for patients with head and neck cancers (HNC) with recurrent and metastatic disease warrants that cancer therapy is specific, with protected delivery of the therapeutic agent to primary and metastatic cancer cells. A further objective should be that downregulation of the intracellular therapy target leads to cell death without compensation by an alternate pathway. To address these goals, we report the utilization of a sub-50-nm tenfibgen (s50-TBG) nanocapsule that delivers RNAi oligonucleotides directed against the essential survival signal protein kinase CK2 (RNAi-CK2) in a cancer cell-specific manner. We have evaluated mechanism and efficacy of using s50-TBG-RNAi-CK2 nanocapsules for therapy of primary and metastatic head and neck squamous cell carcinoma (HNSCC). s50-TBG nanocapsules enter cancer cells via the lipid raft/caveolar pathway and deliver their cargo (RNAi-CK2) preferentially to malignant but not normal tissues in mice. Our data suggest that RNAi-CK2, a unique single-stranded oligonucleotide, co-opts the argonaute 2/RNA-induced silencing complex pathway to target the CK2αα' mRNAs. s50-TBG-RNAi-CK2 inhibited cell growth corresponding with reduced CK2 expression in targeted tumor cells. Treatment of three xenograft HNSCC models showed that primary tumors and metastases responded to s50-TBG-RNAi-CK2 therapy, with tumor shrinkage and 6-month host survival that was achieved at relatively low doses of the therapeutic agent without any adverse toxic effect in normal tissues in the mice. We suggest that our nanocapsule technology and anti-CK2 targeting combine into a therapeutic modality with a potential of significant translational promise.
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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".