hENT1 gene expression as a predictor of response to gemcitabine and nab-paclitaxel in advanced pancreatic cancer.
Bibliographic record
Abstract
4011 Background: Human equilibrative nucleoside transporter 1 (hENT1) belongs to a family of nucleoside transporters critical to entry of gemcitabine into cells. The prognostic and predictive characteristics of this biomarker in pancreatic ductal adenocarcinoma (PDAC) have primarily been evaluated by immunohistochemistry, with conflicting results. We explored the impact of hENT1 gene expression in the Comprehensive Molecular Characterization of Advanced Ductal Pancreas Adenocarcinoma for Better Treatment Selection (COMPASS) trial. Methods: Patients were enrolled on COMPASS from December 2015 to June 2020 and underwent a biopsy for whole genome sequencing (WGS) and RNA sequencing prior to first chemotherapy in the advanced setting. Biopsies underwent laser capture microdissection to enrich for tumour epithelium. Chemotherapy regimen was determined based on clinician preference. The cut-off thresholds for hENT1 expression were determined using the maximal chi-squared statistic. Response rates and overall survival (OS) were computed based on hENT1 expression and chemotherapy regimen. Results: 254 patients were included in the analyses with a median follow-up time of 18 months. 146 patients were treated with modified FOLFIRINOX (FFX), 104 with gemcitabine and nab-paclitaxel (GnP), and 16 received no systemic therapy. Based on gene expression levels, 133 patients were classified as hENT1 high and 121 as hENT1 low. hENT1 expression was significantly associated with the modified Moffitt classifier with higher expression seen in classical tumours (p < 0.001). In the entire cohort, median OS was 10.0 months in hENT1 high vs. 8.3 months in hENT1 low (adjusted HR 0.78, 95% confidence interval 0.59 - 1.03, p = 0.08). In patients receiving modified FFX, there was no difference in response rates (32% vs. 31%, p = 1.00) or OS (10.6 vs. 10.6 months, p = 0.94) between the hENT1 high and hENT1 low groups, respectively. However, in patient treated with GnP, response rates were significantly higher in hENT1 high patients compared to those with hENT1 low tumors (45% vs. 21%, p = 0.035). Median OS in this GnP treated cohort was 9.8 months in hENT1 high vs. 6.1 months hENT1 low (p = 0.003). In an interaction analysis, hENT was predictive of treatment response to GnP (p = 0.0002). Conclusions: Biomarkers predictive of response to GnP and FFX are urgently needed. Here we demonstrate that hENT1 gene expression is a predictor of response to GnP in advanced PDAC.
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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".