The association of epithelial expression of hENT1 and survival from adjuvant gemcitabine in pancreatic ductal adenocarcinoma (PDAC).
Bibliographic record
Abstract
296 Background: Human equilibrative nucleoside transporter 1 (hENT1) mediates the cellular uptake of gemcitabine. Low tumour hENT1 expression has been associated with reduced survival from gemcitabine in some studies. We analyzed the prognostic effect of hENT1 expression in a cohort of PDAC patients (pts) who underwent surgical resection. Methods: A tissue microarray (TMA) composed of duplicate 0.6 mm cores from 261 resected PDACs was constructed from all available cases derived from the archives of Vancouver General Hospital between 1988 - 2013. TMA sections (4 µm) were stained with an antibody for hENT1 (SP120- Spring Biosciences) and scored using a four-tiered scoring system (negative, weak, moderate and strong staining). The expression of hENT1 in the epithelial component of PDAC was quantified and patient samples were divided into low (negative and weak staining) and high (moderate and strong staining) hENT1-expressing groups. Treatment and survival data were collected. Survival differences were quantified with Kaplan-Meier method and log-rank statistic. Results: Our cohort included pts who received no adjuvant chemotherapy (CT) (n=182) or gemcitabine (n=59). Cases treated with other regimens (n=20) were excluded. There was no significant difference in the median overall survival (OS) between pts with low (14.0 months, n=167) and high hENT1 expression (15.2 months, n=15) in the absence of adjuvant CT. Gemcitabine treated patients with low hENT1 expression (n=51) had a median OS of 20.9 months. Median OS was not reached in the group of pts with high hENT1 expression (n=8) who received gemcitabine, with only 2 deaths at the 24 month time point and 6 censorings (p = 0.045). Pts who received gemcitabine had an increased median OS (24.8 months) compared to pts who did not receive adjuvant CT (14.0 months) regardless of hENT1 expression (p<0.0001). Conclusions: Consistent with the ESPAC-3 data, in our analysis adjuvant gemcitabine was associated with increased OS in PDAC pts with high epithelial hENT1 expression (determined using the SP120 antibody) compared to pts with low hENT1 expression. Prospective evaluation of the predictive value of hENT1 expression in this setting is needed.
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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 | 0.000 |
| 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".