Precision Medicine and Pancreatic Cancer
Bibliographic record
Abstract
OBJECTIVES: There is a need for validated predictive markers of gemcitabine response to guide precision medicine treatment in pancreatic cancer. We previously validated human equilibrative nucleoside transporter 1 as a predictive marker of gemcitabine treatment response using Radiation Therapy Oncology Group 9704. Controversy exists about the predictive value of gemcitabine metabolism pathway biomarkers: deoxycytidine kinase (DCK), ribonucleotide reductase 1 (RRM1), RRM2, and p53R2. METHODS: Radiation Therapy Oncology Group 9704 prospectively randomized 538 patients after pancreatic resection to receive either 5-fluorouracil or gemcitabine. Tumor DCK, RRM1, RRM2, and p53R protein expressions were analyzed using a tissue microarray and immunohistochemistry and correlated with treatment outcome (overall survival and disease-free survival) by unconditional logistic regression analysis. RESULTS: There were 229 patients eligible for analysis from both the 5-fluorouracil and gemcitabine arms. Only RRM2 protein expression, and not DCK, RRM1, or p53R2 protein expression, was associated with survival in the gemcitabine treatment arm. CONCLUSIONS: Despite limited data from other nonrandomized treatment data, our data do not support the predictive value of DCK, RRM1, or p53R2. Efforts should focus on human equilibrative nucleoside transporter 1 and possibly RRM2 as valid predictive markers of the treatment response of gemcitabine in pancreatic cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".