Antitumor Efficacy of Oblimersen Bcl-2 Antisense Oligonucleotide Alone and in Combination with Vinorelbine in Xenograft Models of Human Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Overexpression of Bcl-2 protein in cancer cells can inhibit programmed cell death and engender chemoresistance. Reducing Bcl-2 protein levels by using antisense oligonucleotides targeting the gene message can increase the sensitivity of cancer cells to cytotoxic agents. The objective of this work was to investigate the antitumor efficacy of the Bcl-2 antisense oligonucleotide oblimersen (Genasense; G3139), alone and in combination with vinorelbine (VNB), in an ectopic and orthotopic xenograft model of NCI-H460 human non-small-cell lung cancer. In addition to assessing therapeutic effect, Bcl-2 protein expression in tumor tissue isolated from lung and heart was measured. In the ectopic xenograft model, oblimersen at 5 and 10 mg/kg significantly inhibited tumor growth compared with saline-treated control groups, and furthermore, the antitumor effect of oblimersen was associated with down-regulation of Bcl-2 protein in isolated tumor tissue. Moreover, the combination of oblimersen with VNB was more active in inhibiting tumor growth than either drug used alone. In the orthotopic model, oblimersen treatment (5 mg/kg) increased the median survival time of mice to 33 days in comparison with a median survival time of 21 days in the control animals. With this model, the anticancer effect was demonstrated by assessing tumor growth in lung and heart tissues by hematoxylin and eosin staining and Bcl-2 expression by immunohistochemistry. When VNB at 5 mg/kg was combined with oblimersen administered at 5 mg/kg, 33% of mice survived more than 90 days. These data suggest that the combination of oblimersen and VNB may provide enhanced antitumor activities against non-small-cell lung cancer.
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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) | 0.001 | 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 | 0.001 |
| 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".