Selective Therapeutic Targeting of the Anaplastic Lymphoma Kinase With Liposomal siRNA Induces Apoptosis and Inhibits Angiogenesis in Neuroblastoma
Bibliographic record
Abstract
The anaplastic lymphoma kinase (ALK) is a tyrosine kinase receptor that is involved in the pathogenesis of different types of human cancers, including neuroblastoma (NB). In NB, ALK overexpression, or point mutations, are associated with poor prognosis and advanced stage disease. Inhibition of ALK kinase activity by small-molecule inhibitors in lung cancers carrying ALK translocations has shown therapeutic potential. However, secondary mutations may occur that, generate tumor resistance to ALK inhibitors. To overcome resistance to ALK inhibitors in NB, we adopted an alternative RNA interference (RNAi)-based therapeutic strategy that is able to knockdown ALK, regardless of its genetic status [mutated, amplified, wild-type (WT)]. NB cell lines, transduced by lentiviral short hairpin RNA (shRNA), showed reduced proliferation and increased apoptosis when ALK was knocked down. In mice, a nanodelivery system for ALK-specific small interfering RNA (siRNA), based on the conjugation of antibodies directed against the NB-selective marker GD(2) to liposomes, showed strong ALK knockdown in vivo in NB cells, which resulted in cell growth arrest, apoptosis, and prolonged survival. ALK knockdown was associated with marked reductions in vascular endothelial growth factor (VEGF) secretion, blood vessel density, and matrix metalloproteinases (MMPs) expression in vivo, suggesting a role for ALK in NB-induced neoangiogenesis and tumor invasion, confirming this gene as a fundamental oncogene in NB.
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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".