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Assessment of Maintenance Rituximab after First-Line Bendamustine-Rituximab in Patients with Follicular Lymphoma: An Analysis from the BRIGHT Trial

2017· article· en· W2782888072 on OpenAlexaffabout
Brad S. Kahl, John M. Burke, Richard van der Jagt, Julie Chang, Peter Wood, Tim E. Hawkins, David MacDonald, Judith Trotman, David Simpson, Kathryn S. Kolibaba, Samar Issa, Doreen M. Hallman, Ling Chen, Ian W. Flinn

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsQueen Elizabeth II Health Sciences CentreOttawa Hospital
Fundersnot available
KeywordsRituximabVincristineBendamustineMedicineMaintenance therapyInternal medicinePrednisoneFollicular lymphomaCyclophosphamideSurgeryOncologyLymphomaGastroenterologyChemotherapy

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.258
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations20
Published2017
Admission routes2
Has abstractyes

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