Translational study identifies XPF and MUS81 as predictive biomarkers for oxaliplatin-based peri-operative chemotherapy in patients with esophageal adenocarcinoma
Bibliographic record
Abstract
Oxaliplatin-based chemotherapy is used to treat patients with esophageal adenocarcinoma (EAC), but no biomarkers are currently available for patient selection. We performed a prospective, clinical trial to identify potential biomarkers associated with clinical outcomes. Tumor tissue was obtained from 38 patients with resectable EAC before and after 2 cycles of oxaliplatin-fluorouracil chemotherapy. Pre-treatment mRNA expression of 280 DNA repair (DNAR) genes was tested for association with histopathological regression at surgery, disease-free survival (DFS) and overall survival (OS). High expression of 13 DNA damage repair genes was associated with DFS less than one year (P < 0.05); expression of 11 DNAR genes were associated with worse OS (P < 0.05). From clinical associations with outcomes, two genes, ERCC1 and EME1, were identified as candidate biomarkers. In cell lines in vitro, we showed the mechanism of action related to repair of oxaliplatin-induced DNA damage by depletion and knockout of protein binding partners of the candidate biomarkers, XPF and MUS81 respectively. In clinical samples from the clinical trial, pre-treatment XPF protein levels were associated with pathological response, and MUS81 protein was associated with 1-year DFS. XPF and MUS81 merit further validation in prospective clinical trials as biomarkers that may predict clinical response of EAC to oxaliplatin-based chemotherapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".