The Generation and Characterization of a Radiation-Resistant Model System to Study Radioresistance in Human Breast Cancer Cells
Bibliographic record
Abstract
To systematically study the selection of radioresistant cells in clinically advanced breast cancer, a model system was generated by treating MDA-MB231 breast cancer cells with fractionated gamma radiation. A clonogenic assay of the surviving cell populations showed that 2-6 Gy per fraction resulted in a rapid selection of radioresistant populations, within three to five fractions. Irradiation with additional fractions after this initial increase did not increase the radioresistance of the surviving population significantly. Doses of 0.5 and 8 Gy per fraction were not effective in selecting radioresistant cells. To further determine the cause of the changes in radiosensitivity, 15 clones were isolated from the cell populations treated with 40 or 60 Gy with 2 or 4 Gy per fraction, respectively, and were analyzed for radiosensitivity. The average D(10) for these clones was 6.75 +/- 0.36 Gy, which was higher than that for the parental cell population (D(10) = 6.0 +/- 0.2 Gy). The operation of cell cycle checkpoints and the doubling time were similar for both the nonirradiated parental population and the isolated radioresistant subclones. In contrast, a decrease in the apoptotic potential was correlated (r = 0.7, P < 0.01) with increased survival after irradiation, suggesting that apoptosis is an important factor in determining radioresistance under our experimental conditions. We also isolated several subclones from the nonirradiated parental cell population and analyzed them to determine their radiosensitivity after fractionated irradiation. Ten fractions of 4 Gy (40 Gy in total) did not result in a significant increase in the radioresistance of these subclones compared to the irradiated cell populations. The possible mechanisms of the increased radioresistance after fractionated irradiation are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".