Nucleotide-based therapies targeting clusterin chemosensitize human lung adenocarcinoma cells both<i>in vitro</i>and<i>in vivo</i>
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Lung cancer is highly lethal and resistant to most anticancer interventions. Treatment resistance is mediated, in part, by enhanced expression of cell survival proteins that help facilitate tumor progression. Clusterin is a stress-associated cytoprotective protein up-regulated by various apoptotic triggers in many cancers and confers treatment resistance when overexpressed. The objectives in this study were to evaluate clusterin expression levels in human lung cancer tissue, and to test effects of clusterin silencing using antisense oligonucleotides (ASOs) and short interfering double-stranded RNAs (siRNAs) on chemosensitivity in human lung cancer A549 cells. METHODS: Clusterin immunostaining was evaluated in a tissue microarray of 149 spotted human lung cancers. The effects of clusterin ASO or siRNA treatment on clusterin expression and chemosensitivity to paclitaxel was examined in A549 cells in vitro while the ability of clusterin ASO to chemosensitize in vivo was evaluated in immunocompromised mice bearing A549 tumors. RESULTS: More than 80% of human non-small cell lung cancers are immunoreactive for clusterin. Clusterin ASO or siRNA decreased clusterin mRNA expression in A549 cells >75% in a dose-dependent, sequence-specific manner, and significantly enhanced chemosensitivity to paclitaxel in vitro. Characteristic apoptotic DNA laddering was observed after combined treatment with ASO plus paclitaxel, but not with either agent alone. In vivo administration of clusterin ASO, compared to mismatch control oligonucleotide, synergistically enhanced the effects of paclitaxel or gemcitibine to significantly delay A549 tumor growth. CONCLUSION: These findings identify clusterin as a valid therapeutic target in strategies employing novel multimodality therapy for advanced lung cancer.
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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) | 0.001 | 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 it