Plasma Epstein-Barr virus DNA predicts outcome in advanced Hodgkin lymphoma: correlative analysis from a large North American cooperative group trial
Bibliographic record
Abstract
Epstein-Barr virus (EBV) is associated with Hodgkin lymphoma (HL) and can be detected by in situ hybridization (ISH) of viral nucleic acid (EBER) in tumor cells. We sought to determine whether plasma EBV-DNA could serve as a surrogate for EBER-ISH and to explore its prognostic utility in HL. Specimens from the Cancer Cooperative Intergroup Trial E2496 were used to compare pretreatment plasma EBV-DNA quantification with EBV tumor status by EBER-ISH. A cutoff of >60 viral copies/100 µL plasma yielded 96% concordance with EBER-ISH. Pretreatment and month 6 plasma specimens were designated EBV(-) or EBV(+) by this cutoff. Patients with pretreatment EBV(+) plasma (n = 54) had inferior failure-free survival (FFS) compared with those with pretreatment EBV(-) plasma (n = 274), log-rank P = .009. By contrast, no difference in FFS was observed when patients were stratified by EBER-ISH. Pretreatment plasma EBV positivity was an independent predictor of treatment failure on multivariate analyses. At month 6, plasma EBV(+) patients (n = 7) had inferior FFS compared with plasma EBV(-) patients (n = 125), log-rank P = .007. These results confirm that plasma EBV-DNA is highly concordant with EBER-ISH in HL and suggest that it may have prognostic utility both at baseline and after therapy. This trial was registered at www.clinicaltrials.gov as #NCT00003389.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".