Enhancement of radiation sensitivity with BH3I-1 in non-small cell lung cancer.
Bibliographic record
Abstract
BACKGROUND: Anti-apoptotic proteins, such as Bcl-2 and Bcl-xL, are frequently over-expressed in human malignancies, and this is correlated with resistance to chemotherapeutic drugs and gamma- radiation. Recently identified small organic molecules capable of inhibiting Bcl-2 and/or Bcl-xL function, may enhance radiation sensitivity of cancer cells in which they are over expressed. We examined whether specific blockade of the BH3-domain binding to Bcl-xL could sensitize cancer cells to gamma- radiation. METHODS: Human non-small-cell lung cancer H460 cells with wild-type p53 and H1792 cells with mutant p53 were exposed to various doses of radiation and/or BH3I-1 and for different points of time to BH3I-1 treatment. XTT and clonogenic survival assays were used to evaluate the growth-inhibitory effects of the antagonist BH3I-1, ionizing radiation or both. Western blot analysis was used to examine the cellular effect of the expression of Bcl-xL, Bax, and p53. Apoptosis and cell cycle distribution were analyzed by confocal microscopy with Hoechst 33258 staining and cytochrome c, and flow cytometry, respectively. RESULTS: BH3I-1 appeared to induce a dose- and time-dependent apoptosis in H460 and H1792 cells, regardless of p53 status. After 2 days of BH3I-1 treatment, the cells that remained attached were exposed to ionizing radiation. Followed by clonogenic assay, BH3I-1 treatment enhanced the radiation sensitivity of H1792 surviving cells with mutant p53, but not in H460 cells with wild-type p53. A transient time-dependent cell cycle blockade at G2-M phase was identified for H1792 cells without subsequent modification of cell cycle distribution. CONCLUSION: These findings suggest a potential role for the small molecule inhibitor as a novel radiation sensitizer in 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".