Ofatumumab plus HyperCVAD/HD‐MA induction leads to high rates of minimal residual disease negativity in patients with newly diagnosed mantle cell lymphoma: Results of a phase 2 study
Bibliographic record
Abstract
BACKGROUND: Ofatumumab is a humanized type 1 anti-CD20 monoclonal antibody. Preclinical studies show improved complement-mediated cytotoxicity (CMC) compared to rituximab in mantle cell lymphoma (MCL). This study evaluates the safety and efficacy of combining ofatumumab with HyperCVAD/MA (O-HyperCVAD) in newly diagnosed MCL. METHODS: In this single-arm phase 2 study, 37 patients were treated with the combination of O-HyperCVAD for 4 or 6 cycles, followed by high dose chemotherapy and autologous stem cell transplant. Primary objectives were overall response rate (ORR) and complete response (CR) rate at the end of therapy. Secondary objectives included minimal residual disease (MRD) negativity, progression-free survival (PFS), and overall survival (OS). RESULTS: Median age was 60 years; ORR was 86% and 73% achieved a CR by modified Cheson criteria. The MRD negativity rate was 78% after 2 cycles of therapy, increasing to 96% at the end of induction; median PFS and OS were 45.5 months and 56 months, respectively. Achieving a post-induction CR by both imaging and flow cytometry was associated with improved PFS and OS. Early MRD negativity (post-2 cycles) was also associated with an improved PFS but not OS. There were 3 deaths while on therapy, and grades 3 and 4 adverse events (AEs) were observed in 22% and 68% of the patients. CONCLUSION: The addition of ofatumumab to HyperCVAD/HD-MA led to high rates of MRD negativity by flow cytometry in patients with newly diagnosed MCL. Achieving a CR post-induction by both imaging and flow cytometry is associated with improved overall survival.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".