The Absence of Human Equilibrative Nucleoside Transporter 1 Is Associated with Reduced Survival in Patients With Gemcitabine-Treated Pancreas Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: Gemcitabine monotherapy is the standard palliative chemotherapy for pancreatic adenocarcinoma. Gemcitabine requires plasma membrane nucleoside transporter proteins to efficiently enter cells and exert it cytotoxicity. In vitro studies have demonstrated that deficiency of human equilibrative nucleoside transporter 1 (hENT1), the most widely abundant and distributed nucleoside transporter in human cells, confers resistance to gemcitabine toxicity, but the distribution and abundance of nucleoside transporters in normal and malignant pancreatic tissue is unknown. EXPERIMENTAL DESIGN: We studied tumor blocks from normal pancreas and 21 Alberta patients with gemcitabine-treated pancreatic cancer. Immunohistochemistry on the formalin-fixed, paraffin-embedded tissues was performed with specific hENT1 and human Concentrative Nucleoside Transporter 3 monoclonal antibodies and scored by a pathologist blinded to clinical outcomes. RESULTS: hENT1 was detected in normal Langerhan cells and lymphocytes but not in normal glandular elements. Patients in whom all adenocarcinoma cells had detectable hENT1 had significantly longer median survivals from gemcitabine initiation than those for whom hENT1 was absent in a proportion (10 to 100%) of adenocarcinoma cells (median survival, 13 versus 4 months, P = 0.01). Immunohistochemistry for human Concentrative Nucleoside Transporter 3 revealed moderate to high-intensity staining in all adenocarcinoma tissue samples. CONCLUSIONS: Patients with pancreatic adenocarcinoma with uniformly detectable hENT1 immunostaining have a significantly longer survival after gemcitabine chemotherapy than tumors without detectable hENT1. Immunohistochemistry for hENT1 shows promise as a molecular predictive assay to appropriately select patients for palliative gemcitabine chemotherapy but requires formal validation in prospective, randomized trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".