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Record W4379981977 · doi:10.1002/hon.3164_288

Indolent lymphoma: Bendamustine, rituximab and acalabrutinib in Waldenstroms Macroglobulinemia (BRAWM)

2023· article· en· W4379981977 on OpenAlexaffabout
Neil L. Berinstein, Kees-Peter de Roos, G. Klein, Rebecca F. McClure, Nicholas Forward, Mona Shafey, Alexandra Nikonova, David MacDonald, Diego Villa, Irwindeep Sandhu, Mohammed A. Aljama, Jean-François Larouche, KATHRYN MANGOFF

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversité LavalCanadian Red Cross SocietyHealth CanadaBaker Hughes (Canada)Queen Elizabeth II Health Sciences CentreHealth Sciences CentreSunnybrook HospitalSunnybrook Health Science CentreAgricultural Research Institute of OntarioHealth Sciences NorthJuravinski Cancer CentreMcGill University Health Centre
Fundersnot available
KeywordsBendamustineMedicineRituximabAdverse effectInternal medicineWaldenstrom macroglobulinemiaLymphoplasmacytic LymphomaClinical trialIbrutinibOncologyLymphomaLeukemiaChronic lymphocytic leukemia

Abstract

fetched live from OpenAlex

Background: Waldenström’s macroglobulinaemia (WM) is an uncommon lymphoproliferative disorder. Many options are available, however, an optimal first-line therapy for WM has not be defined. We postulated that combining bendamustine and rituximab (BR) with a next generation BTK inhibitor would result in deeper responses as measured by complete response (CR) and very good partial response (VGPR) rates, and provide a longer duration of response. Objectives: The primary objective of this trial is to document the CR and VGPR rates Methods: The BRAWM clinical trial combines BR with acalabrutinib in a fixed duration treatment course including six cycles of BR and 12 months of acalabrutinib. This trial is taking place at 8 clinical sites across Canada and 33 patients have been enrolled, with a recruitment goal of 59. Results: A pre-defined interim analysis of the first 30 enrolled patients showed; median age of patients was 66; 25 were male; two patients were low risk, 14, intermediate and 15 high risk. Seventeen patients completed combination therapy, 9 completed monotherapy and 2 were followed-up at 18 months (6 months post therapy). Clinical results to date: Two patients discontinued treatment early; 1 at cycle 7 (with a VGPR) but experienced an adverse event requiring treatment; 1 at cycle 3 with possible disease progression. There were 186 treatment related adverse events (TRAEs) among 25 of 30 participants; 5 participants did not experience a TRAE; 163 of these occurred during combination therapy; 23 during monotherapy in 7 of 17 participants, where 10 participants did not experience a TRAE. During combination therapy, 16 of the 163 TRAE’s were grade 3; neutropenia (n = 8), including 2 that were febrile neutropenia, n = 1 for each: atrial fibrillation, transaminitis, cellulitis, fatigue and pulmonary emphysema. An additional five were also considered serious and included febrile neutropenia (n = 2), and n = 1 for each fever, allergic reaction and bowel obstruction. During monotherapy, there were no serious TRAEs. There were 2 grade 3 events during monotherapy in two different patients; decreased neutrophil count, and syncope. There were 21 dose interruptions in 11 participants, all but one of whom returned to regular dosage. Of assessed patients, 20/20 have MyD88 mutations, 4/20 have a CXCR4 mutation, and none had a TP53 mutation. Minimal residual disease (MRD) analysis using next generation sequencing of the IgV regions will be reported. Conclusions: Bendamustine, rituximab and acalabrutinib front-line therapy for WM is safe and well tolerated and initial clinical results show that this treatment induces a high percentage of VGPRs. The research was funded by: AstraZeneca Keywords: Combination Therapies, Indolent non-Hodgkin lymphoma Conflicts of interests pertinent to the abstract. N. L. Berinstein Consultant or advisory role: AstraZeneca Research funding: AstraZeneca, Merck, IMV N. Forward Honoraria: AstraZeneca, AbbVie, BeiGene, Celgene/BMS, IMV, Kite, Janssen, Pfizer, Roche, Servier Research funding: Astellas, AstraZeneca, IMV, Merk, MorphoSys, Seattle Genetics, Roche Other remuneration: Speaker Fees: Pfizer, BeiGene, AstraZeneca M. Shafey Consultant or advisory role: Jansen, Roche Canada, Kite/Gilead, Novartis, BeiGene, Incyte, Abbvie, BMS, AstraZeneca A. Nikonova Consultant or advisory role: Forus, Janssen, Astra Zeneca, Apotex, Incyte Educational grants: Janssen D. MacDonald Honoraria: Abbvie, Astra Zeneca, Beigene, BMS, Incyte, Kite Gilead, Roche, and Seattle Genetics D. Villa Consultant or advisory role: AZ, BeiGene, Janssen, Roche, Kite/Gilead, Merck, BMS/Celgene, ONO Pharmaceuticals. Honoraria: AZ, BeiGene, Janssen, Roche, Kite/Gilead, Merck, BMS/Celgene, ONO Pharmaceuticals. Research funding: Roche, AstraZeneca I. Sandhu Honoraria: Celgene/BMS, Kite/Gilead, Janssen, Sanofi, FORUS, Pfizer M. Aljama Consultant or advisory role: Jansen, Sanofi, Pfizer, Beigene J. Larouche Consultant or advisory role: Incyte, Gilead Research funding: Incyte, Astra-Zeneca, Genmab

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.043
GPT teacher head0.370
Teacher spread0.327 · 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 teacher head, not a consensus.

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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Citations2
Published2023
Admission routes2
Has abstractyes

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