DNA repair protein levels vis-à-vis anticancer drug resistance in the human tumor cell lines of the National Cancer Institute drug screening program
Bibliographic record
Abstract
Nucleotide excision repair (NER) is a multi-enzyme DNA repair pathway in eukaryotes. Several NER genes in this pathway including XPB, XPD, XPA and ERCC-1 have been implicated in anticancer drug resistance in human tumor cells. In this study, we assessed the levels of the above-mentioned proteins in the NCI panel of 60 human tumor cell lines in relation to the cytotoxicity patterns of 170 compounds that constitute the standard agent (SA) database. The database consists of drugs used in the clinic for which a mechanism of action has been at least partially defined. The ERCC-1, XPD and XPB protein expression patterns yielded significant negative Pearson correlations with 13, 32 and 17 out of the 170 compounds, respectively (using p<0.05). XPA produced a random assortment of negative and positive correlations, and did not appear to confer an overall resistance or sensitivity to these drugs. Protein expression was also compared with a pre-defined categorization of the standard agents into six mechanism-of-action groups resulting in an inverse association between XPD and alkylating agent sensitivity. Our present data demonstrate that XPD protein levels correlate with resistance to alkylating agents in human tumor cell lines suggesting that XPD is implicated in the development of this resistance. NER activity, using the in vitro cell-free system repair assay, revealed no correlation between NER activity and the level of XPD protein in four cell lines with widely varying XPD protein levels. This lack of correlation may be due to the contribution of XPD to other functions including interactions with the Rad51 repair pathway.
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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 | 0.001 | 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) | 0.001 | 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".