Assessment of Maintenance Rituximab after First-Line Bendamustine-Rituximab in Patients with Follicular Lymphoma: An Analysis from the BRIGHT Trial
Bibliographic record
Abstract
Introduction: Maintenance rituximab (R) has been shown to improve progression-free survival (PFS) in patients with follicular lymphoma (FL) after first-line R with cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) or R with cyclophosphamide, vincristine and prednisone (R-CVP) (PRIMA Trial). Whether a similar benefit is observed after first-line bendamustine-rituximab (BR) is unknown.The BRIGHT study investigated the safety and efficacy of BR versus R-CHOP or R-CVP in treatment-naive patients with indolent non-Hodgkin lymphoma or mantle cell lymphoma. Five-year follow-up data from this study has confirmed that duration of response and PFS were significantly improved in the BR treatment group. Overall survival (OS) was not statistically different between BR and R-CHOP/R-CVP. This ad hoc analysis examines the use of maintenance R in the BRIGHT study. Methods: The patient set used in this analysis consisted of 288 patients with FL who had a complete response (CR) or partial response (PR) based on the investigator9s assessment. The use of maintenance R was at the discretion of the investigator. Baseline characteristics were compared by treatment group for those patients receiving maintenance R versus those who did not receive maintenance R, as were the proportions of patients with CR and PR. Kaplan-Meier plots are presented for PFS and OS by treatment group subdivided by whether the patient received maintenance R. P values were determined by the log-rank test. Results: Among 144 patients with FL in the BR treatment group and 144 patients with FL in the R-CHOP/R-CVP treatment group, 81 (56%) and 83 (58%) received maintenance R, respectively. The baseline demographic and lymphoma characteristics of the patient groups are compared in the Table. In the BR treatment group, patients with B symptoms more commonly received maintenance R (38% vs 30%) while in the R-CHOP/R-CVP treatment group the opposite was true (29% vs 43%). In both treatment groups, patients with lactate dehydrogenase > 240 U/L and β2-microglobulin >3mg/L were less likely to receive maintenance R. In the BR treatment group, patients achieving CR to induction therapy were more likely to be assigned to no R maintenance (40% vs 22%; P= 0.0231). In the R-CHOP/R-CVP treatment group, the proportions with CR were similar in patients who did and did not receive maintenance R (19% vs 21%; P= 0.7636). Patients responding to BR (CR and PR) who received maintenance R had a significantly better PFS than responding patients who did not receive maintenance R; hazard ratio (HR) = 0.50 (95% confidence interval [CI] 0.26-0.94), P= 0.0295 (Figure). Patients responding to R-CHOP/R-CVP (CR or PR) who received maintenance R had a trend towards better PFS than responding patients who did not receive maintenance R; HR = 0.66 [95% CI 0.38-1.16], P= 0.1443. OS tended to be better in patients assigned to maintenance R (BR treatment group, HR = 0.39 [95% CI 0.14-1.05], P= 0.0537; R-CHOP/R-CVP group, HR = 0.32 (0.10-1.05; P= 0.0481). Conclusions: In this retrospective analysis of FL patients treated with BR induction therapy on the BRIGHT study, maintenance R significantly improved PFS with a trend towards improvement in OS, despite the fact that patients with CR were less likely to receive maintenance R. Maintenance R also showed a tendency towards improved outcomes after R-CHOP/R-CVP, consistent with data from randomized clinical trials (RCTs). Given that the application of maintenance R was based on investigator discretion, it is possible that the observed effect was due to confounding variables. However, the overall improvement in PFS in the maintenance R patients appears to be at least as great following BR as following R-CHOP/R-CVP and supports the notion of testing maintenance R after BR therapy in RCTs. Disclosures Kahl: Celgene: Consultancy; Gilead: Consultancy; ADC Therapeutics: Research Funding; Seattle Genetics: Consultancy; Genentech: Consultancy. Burke: Bayer: Consultancy; Celgene: Consultancy; Incyte: Consultancy; Gilead: Consultancy; Genentech: Consultancy. van der Jagt: Lundbeck, Teva: Consultancy; Teva: Research Funding. Wood: Bayer: Consultancy; Bayer, Boehringer Ingelheim, Bristol Myer Squibb: Honoraria. MacDonald: Lundbeck Canada, Roche Canada: Honoraria. Trotman: Janssen Cilag: Other: Funding facilitating research paid to third party (BioGrid Australia), Research Funding. Simpson: Amgen: Research Funding; Onyx: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Roche: Honoraria; Celgene: Honoraria, Other: travel expenses. Kolibaba: Celgene: Research Funding; Cell Therapeutics: Research Funding; Genentech: Research Funding; Gilead Sciences, Inc: Consultancy, Research Funding; Janssen: Research Funding; Novartis: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; TG Therapeutics: Honoraria, Research Funding; Acerta: Research Funding. Hallman: Teva Pharmaceuticals: Employment. Chen: Teva Pharmaceuticals: Employment. Flinn: Acerta: Research Funding; Novartis: Research Funding; Celgene: Research Funding; Pharmacyclics LLC: Research Funding; Trillium: Research Funding; Gilead: Research Funding; Pharmacyclics: Research Funding; Verastem: Research Funding; AbbVie Company: Research Funding; Merck: Research Funding; Janssen: Research Funding; Curis: Research Funding; Incyte: Research Funding; KITE: Research Funding; Constellation: Research Funding; Infinity: Research Funding; Agios: Research Funding; Beigene: Research Funding; Forty Seven: Research Funding; Genentech: Research Funding; Takeda: Research Funding; TG Therapeutics: Research Funding; Calithera: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Portola: Research Funding; Pfizer: 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".