Lymphopenia and EBER-Negativity Predict Poor Survival in Patients with Post-Transplant Lymphoproliferative Disorder (PTLD) Treated in the Rituximab Era
Bibliographic record
Abstract
Abstract Abstract 1645 Post-transplant lymphoproliferative disorders (PTLD) are an important complication of solid organ transplant (SOT) with a high mortality rate. PTLD represents a spectrum of disease thought to be driven by Epstein Barr virus (EBV) and ranges from indolent polyclonal lymphoproliferation to aggressive lymphoma. Therapeutic options include reduction of immunosuppression (RIS), antiviral therapy, anti-CD20 monoclonal antibody rituximab, and combination chemotherapy, but no previous studies have reliably determined a risk-stratified approach. Several prognostic factors have been reported previously but are not well-defined in patients treated with rituximab. The objective of this study was to analyze a cohort of patients diagnosed with PTLD in the rituximab era to determine clinical and laboratory prognostic factors and survival outcomes. We retrospectively reviewed outcomes of all adult patients who developed PTLD after 1998 following SOT at the University of Alberta (n=52). Prognostic factors for overall survival (OS) were analyzed using the Kaplan-Meier method and the logrank test. The median age at PTLD diagnosis was 52 years (range 19–74) and the median time from SOT to PTLD diagnosis was 4.7 years (109 days-20.9 years). Other patient characteristics are listed in Table 1. The majority of patients had PTLD over 1 year post-SOT (75%), monomorphic histology (69%) and were CD20 positive (63%). All patients were treated with RIS and antivirals if EBV viremia was detected by PCR. The majority of patients (n=34, 65%) received first-line rituximab at 375 mg/m2 IV with 4 weekly doses planned; 6 of these patients (12%) required further chemoimmunotherapy progressed or died before completing 4 doses, and 13 patients (38%) progressed and required further therapy after completing 4 doses. Sixteen patients (47%) achieved at least a partial response (PR) to rituximab monotherapy. Of the 18 patients who did not receive rituximab monotherapy, 3 received first-line chemoimmunotherapy, 5 received chemotherapy alone, and 10 had an unknown history of therapy. All patients who did not receive rituximab were CD20 negative.Table 1Patient characteristicsn=52Patient characteristicsn=52Organ transplantedPTLD ≤ 1 yr post-SOT13 Kidney19EBV serology mismatch12 Lung17CMV serology mismatch12 Liver9EBV viremia (at dx)10 Heart5EBER+28 Pancreas1CD20+33 >1 SOT pre-PTLD12Monomorphic histology36Tumour histologyIPI score 3–520 Diffuse large B cell lymphoma23Age >6015 B cell lymphoma4ECOG >216 Primary CNS lymphoma1>1 extranodal site21 Intravascular B cell lymphoma1LDH elevated18 Burkitt lymphoma1Hemoglobin <120 g/L29 Hodgkin lymphoma4Lymphs <1.2x109/L26 T cell lymphomas6Allograft involvement9 Polymorphic PTLD4CNS involvement6 Unknown8BM involvement6 The median OS of the whole cohort was 1.99 years (range 3 days to 12 years), with 44% of patients remaining alive at 5 years. Thirty patients have died, and 17 deaths were attributed to PTLD. The median follow up of the surviving patients was 4.7 years. The patient characteristics listed in Table 1, as well as rituximab monotherapy, were tested in univariate analyses for their potential to predict OS. The factors significantly associated with worse prognosis were EBER negativity (p=0.017, HR=2.76 [1.10 to 6.90]), no EBV serology mismatch at SOT (p=0.0499, HR=2.44 [1.12 to 5.26]), IPI 3–5 (p=0.009, HR=2.78 [1.15 to 6.72]), ECOG >2 (p=0.047, HR=2.26 [0.91 to 5.58]), lymphocyte count < 1.2 × 109/L (p=0.012, HR=5.05 [2.03 to 12.59]), and no front-line rituximab monotherapy (p=0.001, HR=3.33 [1.16 to 9.52]). In the cohort that received front-line rituximab monotherapy (n=34), EBER negativity (p=0.002, HR=4.61 [1.33 to 15.87]) and less than PR to rituximab monotherapy (p=0.006, HR=4.37 [1.47 to 12.99]) were significantly associated with worse survival. In conclusion, even in the rituximab era, the 5 year median OS for patients with PTLD was poor at 44%, indicating the need for improved therapeutic strategies. However, patients who received front-line rituximab and those who responded had significantly improved outcomes. We showed that EBER negativity and lack of EBV serostatus mismatch at the time of transplant as well as poor performance status predicted a worse prognosis. Finally, we demonstrated that lymphopenia, a novel prognostic marker, has a strong negative impact on survival in PTLD. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 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".