High expression of nucleoside transporter hENT1 predicts a worse event-free survival in relapsed/refractory Hodgkin lymphoma patients treated with gemcitabine, vinorelbine, and liposomal doxorubicin—A CALGB 59804 correlative study
Bibliographic record
Abstract
7581 Background: CALGB 59804 was a phase I/II trial of gemcitabine, vinorelbine, and liposomal doxorubicin (GVD) for pts with relapsed Hodgkin lymphoma (HL). Overall response rate (RR) was 70% (manuscript in review). Plasma membrane nucleoside transporters such as hENT1 are important in transporting nucleoside analogs into cells to exert their pharmacologic effects. We hypothesized high hENT1 expression would predict better RR and outcome in pts treated with GVD. Methods: 58 of 91 pts enrolled in CALGB 59804 had sufficient tissue for study; 31 relapse biopsies and 27 initial diagnosis biopsies. Expression of hENT1 was evaluated by immunohistochemistry in formalin fixed tissue and scored independently by two hematopathologists (RL and EH) blinded to the clinical outcomes. Positivity for hENT1 was defined as >25% of Reed-Sternberg (RS) cells expressing hENT1. Expression was correlated with clinical factors, including IPS at relapse, as well as the overall (OS) and event-free survival (EFS). Results: Expression of hENT1 in RS cells was heterogeneous among cases. 28/58 cases (48%) were hENT1-positive. hENT1-expression was not associated with age, gender, stage, IPS (≤2 or >2), or maximum toxicity grade (≤2 or >2). Compared to hENT1-negative pts, hENT1-positive pts were less likely to have complete or partial response (19/30, 63% versus 22/28, 76%, P=0.20, chi square). hENT1 expression was not significantly associated with OS (P=0.18). Univariate log-rank analysis showed hENT1 positivity and IPS >2 correlated significantly with a lower EFS (P=0.05, and P=0.03, respectively). Multivariate Cox regression analysis confirmed that IPS >2 and hENT1 positivity were independent predictors of EFS (Hazard ratio 2.16, 95%CI 1.08–4.35, P=0.03; and Hazard ratio 2.10, 95% CI 1.06–4.19, P=0.03, respectively). P-values are two-sided. Conclusions: Contrary to our hypothesis, there is an inverse relationship between EFS and hENT1 expression in relapsed HL pts treated with GVD. High expression of hENT1 did not identify gemcitabine-sensitive disease, but may identify a biologically aggressive and/or treatment refractory subtype of HL. No significant financial relationships to disclose.
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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 | 0.001 |
| 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".