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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".