Outcomes in Relapsed/Refractory Burkitt Lymphoma: A Multi-Centre Canadian Experience
Bibliographic record
Abstract
Abstract Introduction: Burkitt lymphoma (BL) is an aggressive B cell lymphoma with a distinct morphology, immunophenotype and characteristic C-MYC gene rearrangement. When treated with intensive chemotherapy, outcomes are excellent with a reported overall survival of greater than 80% at 3 years. A small subset of patients have primary refractory or relapsed disease, but there have been few reports of treatment and outcomes of these patients due to the rarity of this lymphoma and its otherwise good prognosis. Objective: The objective of this study was to review the characteristics, treatments and outcomes of patients with relapsed/refractory BL. Methods: We included patients 18 years or older with a pathologically confirmed BL diagnosed between 2003 and 2018 at eight Canadian centres who received curative intent frontline chemotherapy and who had primary refractory or relapsed disease. Data were retrospectively reviewed at each site independently. Staging and response assessment was based on computed tomography (CT). Descriptive statistics were used for baseline characteristics and treatment regimens. Kaplan Meier Survival Analysis was used to estimate overall survival (OS) which was calculated from time of relapse. Results: A total of 74 patients were included in the study. Median age was 48 years (IQR 32-61) and 81% were male. Nine patients (12%) were known to be HIV positive. Most patients had advanced disease with stage III/IV (n=47, 64%), bone marrow involvement (n=32, 43%) and at least one extranodal site (n=67, 91%). Nineteen patients (26%) had central nervous system (CNS) involvement at diagnosis. The most common induction regimen was CODOX-M-IVAC (n=43, 58%) followed by CHOP or EPOCH (n=14, 19%) and hyperCVAD (n=7, 9%). Forty five (61%) patients received rituximab with their first line treatment. The median time to relapse from diagnosis was 5 months with a majority of cases being primary refractory (n=57, 77%). Patients had systemic relapse (n=44, 59%), isolated CNS relapse (n=19, 26%) or both (n=11, 15%). Forty three (58%) patients received second-line salvage chemotherapy while 28 (39%) were treated with palliative oral chemotherapy and/or radiation. A variety of salvage regimens were used including systemic and CNS-directed second line regimens: most common GDP (n=7, 9%), hyperCVAD (n=5, 7%) and DHAP (n=5, 7%). Rituximab was given at relapse to 23 patients (31%). Progressive disease during or after salvage was noted in 23 (31%) patients. Twenty patients received a second-line transplant (15 autologous, 5 allogeneic). The median OS of the whole cohort was 3.2 months and 2 year OS was 17.2% (95% CI 9.4-26.9). Median OS for patients receiving salvage was 5.4 months compared to 1.3 months for those who received palliative therapy (p<0.05). Amongst the patients who died (n=62, 84%), the most common cause of death was disease progression (n=57, 77%), while 2 died from treatment-related toxicity (3%) and 3 from second malignancies (4%). Patients with both systemic and CNS involvement at the time of relapse had a worse prognosis than those with isolated systemic or isolated CNS relapse (median OS 1.9 months vs 3.4 months, p=0.03). Nine of 11 surviving patients received either an autologous (n=8) or allogeneic (n=1) stem cell transplant. One patient was lost to follow up shortly after progressing. Median follow up for survivors was 50 months (range 2-201 months). Conclusion: Relapsed/refractory BL has very poor prognosis, even in the rituximab era. There is no standard approach to salvage treatments in this population. Despite advances in novel agents and cellular therapies in aggressive lymphoma, patients with BL are often excluded from these clinical trials. This study highlights the need for inclusion of this population in trials evaluating novel therapies for aggressive B cell lymphomas. Figure 1 Figure 1. Disclosures Gerrie: Astrazeneca: Honoraria, Research Funding; Sandoz: Honoraria; Roche: Research Funding; AbbVie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Chua: Merck: Honoraria; Pfizer: Honoraria; Eisai: Honoraria; Gilead: Honoraria. Stewart: Roche: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Gilead: Honoraria; Celgene: Honoraria; Novartis: Honoraria; AstraZeneca: Honoraria; Amgen: Honoraria; Sandoz: Honoraria; Teva: Honoraria. Kuruvilla: Seattle Genetics: Honoraria; TG Therapeutics: Honoraria; Medison Ventures: Honoraria; Amgen: Honoraria; Karyopharm: Honoraria, Other: Data and Safety Monitoring Board; Gilead: Honoraria; Pfizer: Honoraria; Incyte: Honoraria; AstraZeneca: Honoraria, Research Funding; AbbVie: Honoraria; Antengene: Honoraria; Merck: Honoraria; Novartis: Honoraria; Roche: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; BMS: Honoraria. Crump: Novartis: Membership on an entity's Board of Directors or advisory committees; Kyte/Gilead: Membership on an entity's Board of Directors or advisory committees; Epizyme: Research Funding; Roche: Research Funding.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".