Targeting the DNA Repair Endonuclease ERCC1-XPF with Green Tea Polyphenol Epigallocatechin-3-Gallate (EGCG) and Its Prodrug to Enhance Cisplatin Efficacy in Human Cancer Cells
Bibliographic record
Abstract
The 5′-3′ structure-specific endonuclease ERCC1/XPF (Excision Repair Cross-Complementation Group 1/Xeroderma Pigmentosum group F) plays critical roles in the repair of cisplatin-induced DNA damage. As such, it has been identified as a potential pharmacological target for enhancing clinical response to platinum-based chemotherapy. The goal of this study was to follow up on our previous identification of the compound NSC143099 as a potent inhibitor of ERCC1/XPF activity by performing an in silico screen to identify structural analogues that could inhibit ERCC1/XPF activity in vitro and in vivo. Using a fluorescence-based DNA-endonuclease incision assay, we identified the green tea polyphenol (-)-epigallocatechin-3-gallate (EGCG) as a potent inhibitor of ERCC1/XPF activity with an IC50 (half maximal inhibitory concentration) in the nanomolar range in biochemical assays. Using DNA repair assays and clonogenic survival assays, we show that EGCG can inhibit DNA repair and enhance cisplatin sensitivity in human cancer cells. Finally, we show that a prodrug of EGCG, Pro-EGCG (EGCG octaacetate), can enhance response to platinum-based chemotherapy in vivo. Together these data support a novel target of EGCG in cancer cells, namely ERCC1/XPF. Our studies also corroborate previous observations that EGCG enhances sensitivity to cisplatin in multiple cancer types. Thus, EGCG or its prodrug makes an ideal candidate for further pharmacological development with the goal of enhancing cisplatin response in human tumors.
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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".