Molecular mechanisms of radiation resistance in doxorubicin-resistant breast adenocarcinoma cells
Bibliographic record
Abstract
A positive response to breast cancer treatment is largely dependent on the successful combination of anticancer treatment modalities, such as chemotherapy and radiation therapy. Unfortunately, chemotherapy resistance occurs frequently. Furthermore, drug‑resistant tumors can become unresponsive to other antitumor therapies, and they often fail to respond to radiation therapy. The molecular structures underlying the radiation responses of chemoresistant cells and tumors are not well understood. We analyzed the effect of ionizing radiation on MCF-7 human breast adenocarcinoma cells and their doxorubicin‑resistant variant, MCF-7/DOX. The results demonstrated that drug‑resistant MCF-7/DOX cells were less susceptible to radiation-induced DNA damage and apoptosis. This was proven through gene expression profiling, lower levels of γH2AX foci upon irradiation, and altered levels of DNA repair proteins, including pATM, KU70 and RAD51. Additionally, MCF-7/DOX drug‑resistant cells harbored DNA polymerases with significantly low fidelity. In summary, our study revealed that drug-resistant MCF-7/DOX cells have high DNA repair potential and low-fidelity DNA polymerases, seemingly sacrificing specificity and efficiency to gain higher survival potential. In the long run, this may lead to an increased probability of mutation accumulation and further the development of an even more pronounced resistance phenotype. Therefore, this study provides a roadmap for the analysis of the roles of the DNA repair function and effectiveness, and apoptosis in response to radiation, chemotherapy and combinations of both treatment modalities.
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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".