Improved Survival of Patients with HIV-Related Systemic B-Cell Non-Hodgkin’s Lymphoma (NHL) Expressing the Epstein-Barr Virus (EBV) in Malignant Cells Provided Patients Received HAART with Lymphoma Therapy.
Bibliographic record
Abstract
Abstract EBV infection of B-lymphocytes is known to promote cell proliferation and survival. In contrast to NHL in HIV-negative patients (pts), in whom ≤5% are EBV-positive (EBV+), up to 50% are EBV+ in systemic HIV-related NHL. Highly active antiretroviral therapy (HAART) has improved the outcome of AIDS-related lymphoma (ARL) pts and allows intensification of lymphoma therapy. To determine whether EBV+ systemic ARL might behave more aggressively and require intensification of therapy, we performed a retrospective clinical-pathological correlation in pts seen at St. Paul’s Hospital in Vancouver, Canada, and focused on 39 B-cell lymphoma (BCL) since 1992. Clinical and HIV-related data were abstracted by chart review and from the CFE data-base. Stored biopsy material diagnostic of BCL was sectioned and stained for EBV by EBER. Median age was 40 (range 20–64) years and 37 pts were male. 16 pts had diffuse large B-cell lymphoma (DLBCL), 17 other B-cell NHL and 6 Hodgkin’s Lymphoma (HL); 29 were advanced stage. HIV risk was sexual in 25 and injection drug use (IDU) in 14. Median CD4 count at diagnosis was 140 (10–770) cells/ul and 23 pts received HAART. Lymphoma therapy was administered according to era and histology: CHOP-like ± Rituximab (R) n=28; ABVD-like n=6; CODOX-M/IVAC-R n=1; HAART alone n=1; resection n=1; declined therapy n=2. 9 pts received added R and 3 added radiation therapy. 11 received therapy directed against other herpes viruses (HVT) at some point in ARL treatment. 25 were EBV+ and 14 EBV−; no baseline HIV or lymphoma related characteristics differed significantly according to EBV status nor did receipt of HVT. In a Kaplan-Meier analysis, EBV expression was significant for overall survival (OS); median survival (MS) for pts with EBV+ ARL was 38 (2.5–74.3) months (mo) vs. 6.1 (0–18.5) mo for EBV− (p<0.02). However, OS for HL (6 of 6 EBV+) was 100% at 11–66 mo vs. 6.5 (2.5–48) mo for DLBCL and 7.4 (0–74) mo for other NHL (p<0.03); when HL were removed from the analysis there was no difference in OS or progression-free survival (PFS) either by EBV status or according to use of HVT (OS by EBV p<0.14, OS by HVT p<0.1, PFS by EBV p<0.1, PFS by HVT p<0.2). Although there was no difference in PFS by EBV status and HAART use, OS was significantly improved in pts with EBV+ NHL receiving HAART; MS 11 (2–74) mo for pts with EBV+ NHL receiving HAART vs. 4 (3–6) mo for pts with EBV+ NHL not receiving HAART (p<0.04); for EBV− with HAART MS 7 (2–18) mo vs. 6 (2–9) mo without HAART (p=NS). In conclusion, in a retrospective analysis, OS appeared to be significantly improved in pts with EBV+ NHL receiving HAART with lymphoma therapy, underscoring the importance of HAART in the outcome of ARL.
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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".