Cisplatin pharmacogenetics, DNA repair polymorphisms, and esophageal cancer outcomes
Bibliographic record
Abstract
OBJECTIVES: Genetic variations or polymorphisms within genes of the nucleotide excision repair (NER) pathway alter DNA repair capacity. Reduced DNA repair (NER) capacity may result in tumors that are more susceptible to cisplatin chemotherapy, which functions by causing DNA damage. We investigated the potential predictive significance of functional NER single nucleotide polymorphisms in esophageal cancer patients treated with (n = 262) or without (n = 108) cisplatin. METHODS: Four NER polymorphisms XPD Asp312Asn; XPD Lys751Gln, ERCC1 8092C/A, and ERCC1 codon 118C/T were each assessed in polymorphism-cisplatin treatment interactions for overall survival (OS), with progression-free survival (PFS) as a secondary endpoint. RESULTS: No associations with ERCC1 118 were found. Polymorphism-cisplatin interactions were highly significant in both OS (P = 0.002, P = 0.0001, and P < 0.0001) and PFS (P = 0.006, P = 0.008, and P = 0.0007) for XPD 312, XPD 751, and ERCC1 8092, respectively. In cisplatin-treated patients, variant alleles of XPD 312, XPD 751, and ERCC1 8092 were each associated with significantly improved OS (and PFS): adjusted hazard ratios of homozygous variants versus wild-type ranged from 0.22 [95% confidence interval (CI): 0.1-0.5] to 0.31 (95% CI: 0.1-0.7). In contrast, in patients who did not receive cisplatin, variant alleles of XPD 751 and ERCC1 8092 had significantly worse survival, with adjusted hazard ratios of homozygous variants ranging from 2.47 (95% CI: 1.1-5.5) to 3.73 (95% CI: 1.6-8.7). Haplotype analyses affirmed these results. CONCLUSION: DNA repair polymorphisms are associated with OS and PFS, and if validated may predict for benefit from cisplatin therapy in patients with esophageal cancer.
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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 | 0.001 | 0.002 |
| 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".