Data from hENT1 Expression Predicts Response to Gemcitabine and Nab-Paclitaxel in Advanced Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
AbstractPurpose: Modified FOLFIRINOX (mFFX) and gemcitabine/nab-paclitaxel (GnP) remain standard first-line options for patients with advanced pancreatic ductal adenocarcinoma (PDAC). Human equilibrative nucleoside transporter 1 (hENT1) was hypothesized to be a biomarker of gemcitabine in the adjuvant setting, with conflicting results. In this study, we explore hENT1 mRNA expression as a predictive biomarker in advanced PDAC. Experimental Design: COMPASS was a prospective observational trial of patients with advanced PDAC. A biopsy was required prior to initiating chemotherapy, as determined by treating physician. Biopsies underwent laser capture microdissection prior to whole genome and RNA sequencing. The cut-off thresholds for hENT1 expression were determined using the maximal χ2 statistic. Results: 253 patients were included in the analyses with a median follow-up of 32 months, with 138 patients receiving mFFX and 92 receiving GnP. In the intention to treat population, median overall survival (OS) was 10.0 months in hENT1high versus 7.9 months in hENT1low (P = 0.02). In patients receiving mFFX, there was no difference in overall response rate (ORR; 35% vs. 28%, P = 0.56) or median OS (10.6 vs. 10.5 months, P = 0.45). However, in patients treated with GnP, the ORR was significantly higher in hENT1high compared with hENT1low tumors (43% vs. 21%, P = 0.038). Median OS in this GnP-treated cohort was 10.6 months in hENT1high versus 6.7 months hENT1low (P < 0.001). In an interaction analysis, hENT1 was predictive of treatment response to GnP (interaction P = 0.002). Conclusions: In advanced PDAC, hENT1 mRNA expression predicts ORR and OS in patients receiving GnP.
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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 | 0.001 | 0.002 |
| 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".