A Three-Arm Randomized Phase II Study of Bendamustine/Rituximab with Bortezomib Induction or Lenalidomide Continuation in Untreated Follicular Lymphoma: ECOG-ACRIN E2408
Bibliographic record
Abstract
Abstract Purpose: We sought to improve upon frontline bendamustine/rituximab (BR) induction therapy followed by rituximab maintenance in untreated high-risk follicular lymphoma (FL). Patients and Methods: Patients were randomized to BR induction followed by 2-year rituximab maintenance (BR-R), BR with bortezomib and rituximab maintenance (BVR-R), or BR followed by lenalidomide (1 year) with rituximab maintenance (BR-LR). Dual primary objectives were complete remission (CR) rate and 1-year disease-free survival (DFS); 289 patients enrolled (NCT01216683). Results: For induction, 92%, 87%, and 86% of patients randomized to BR-R, BVR-R, or BR-LR received six cycles, respectively. CR rate with BR versus BVR induction was 62% versus 75%, respectively (P = 0.04). One-year DFS rates with BR-R versus BR-LR were 85% versus 67%, respectively (P = 0.0009). This was due to an imbalance in CR rates post-BR induction and discontinuation due to adverse events (AEs). The most common grade 3–4 AEs for BVR versus BR were neutropenia and sensory neuropathy (12% vs <1%); 83% of the latter occurred with intravenous bortezomib. The most common grade 3–4 AEs related to LR versus rituximab maintenance were neutropenia 66% versus 21%, respectively (P < 0.0001), and febrile neutropenia 10% versus 2%, respectively (P = 0.05). The overall treatment-related mortality was 1.4%. With 5-year median follow-up, 3-year PFS rates for BR-R, BVR-R, and BR-LR were 77%, 82%, and 76%, respectively (P = 0.36) with OS rates of 87%, 90%, and 84%, respectively (P = 0.79). For prognostication, CR rate and POD-24 were associated with survival. Conclusions: Altogether, neither bortezomib added to BR induction nor lenalidomide added to rituximab maintenance immediately post-BR induction is recommended in untreated FL.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".