The role of TP, TS, and DPD as potential predictors of outcome following capecitabine plus oxaliplatin (XELOX) versus bolus 5-fluorouracil/leucovorin (5-FU/LV) as adjuvant therapy for stage III colon cancer: Biomarker findings from study NO16968 (XELOXA).
Bibliographic record
Abstract
3578 Background: In NO16968, XELOX was superior in terms of disease-free survival (DFS) and overall survival (OS) to bolus 5-FU/LV as adjuvant therapy for stage III colon cancer (Schmoll et al. ASCO GI 2012). Three key enzymes appear to have the potential to predict efficacy and/or safety of fluoropyrimidine-based treatment: thymidine phosphorylase (TP), thymidylate synthase (TS), and dihydropyrimidine dehydrogenase (DPD). We evaluated the association between baseline TP, TS and DPD and outcome (DFS and OS). Methods: Pts with stage III colon cancer received either XELOX (8 cycles, 24w) or bolus 5-FU/LV (Mayo Clinic, 6 cycles, 24w; Roswell Park, 4 cycles, 32w). The primary study endpoint was DFS; secondary endpoints included OS. TP, TS and DPD expression levels were determined in formalin-fixed, paraffin-embedded tissues by RT-PCR, and the median used as a cut-off point: high (above median) vs. low (below median). Results: The biomarker population included 498 (26%) of 1886 pts entered (XELOX, n=242; 5-FU/LV, n=256). Baseline demographics, tumor characteristics, cancer history and efficacy (DFS and OS) were similar to those in the main study population. Cox regression analysis for DFS (Table). In the XELOX group pts with low DPD and TP levels and a high TP/DPD ratio appeared to have significantly better DFS; this effect was not observed with 5-FU/LV. Subgroup analysis shows that the difference between XELOX and 5-FU/LV was also higher in pts with low DPD levels. Conclusions: These exploratory findings suggest that tumor DPD and TP RNA levels could be used to predict outcomes of adjuvant treatment with fluoropyrimidine/oxaliplatin combinations, and should be validated prospectively. Analysis of the current dataset is ongoing and further details on potential biomarkers will be available. [Table: see text]
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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".