The synergy between alkylating agents and ERCC1–XPF inhibitors is p53 dependent
Bibliographic record
Abstract
BACKGROUND: DNA repair plays a major role in maintaining genomic stability, thus limiting the transformation of normal cells into cancer cells. However, in cancer patients treated with DNA-targeting drugs, DNA repair can decrease efficacy by removing the damage generated by such molecules that is needed to induce pharmacological activity. Inhibiting DNA repair thus represents an interesting approach to potentiating the activity of chemotherapy in this setting. OBJECTIVES: Here, we continue the characterization of an inhibitor of the interaction between Excision Repair Cross-Complementing Rrodent repair deficiency complementation group 1 (ERCC1) and Xeroderma Pigmentousum group F (XPF) (B9), two key proteins of nucleotide excision repair. METHODS: We used various cell lines and co-incubation studies for the determination of cell survival and DNA repair capacities. RESULTS: We show that it is synergistic with other platinum derivatives than previously described, and that synergy is lacking in cells not expressing ERCC1 or XPF. Finally, a series of experiments show that potentiation is observed only in cells expressing wild-type p53. CONCLUSION: Our results confirm the mechanism of action of our ERCC1-XPF inhibitor and give important additional data on this approach to enhance the activity of already existing cancer drugs.
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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) | 0.001 | 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) | 0.002 | 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".