Smart Start: Rituximab, Lenalidomide, and Ibrutinib in Patients With Newly Diagnosed Large B-Cell Lymphoma
Bibliographic record
Abstract
PURPOSE Chemoimmunotherapy for patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) is largely unchanged for decades. Both preclinical models and clinical data suggest the combination of lenalidomide and ibrutinib may have synergy in DLBCL, particularly in the non–germinal center B-cell-like subset. METHODS We enrolled 60 patients with newly diagnosed non–germinal center B-cell-like DLBCL in this investigator-initiated, single-arm phase II trial of rituximab, lenalidomide, and ibrutinib (RLI) with the sequential addition of chemotherapy (ClinicalTrials.gov identifier: NCT02636322 ). Patients were treated with rituximab 375 mg/m2 intravenous once on day 1, lenalidomide 25 mg once per day on days 1-10, and ibrutinib 560 mg once daily continuously of each 21-day cycle (RLI). After two cycles, standard chemotherapy was added to RLI for six additional cycles. The primary end points were overall response rate (ORR) after two cycles of RLI alone and complete response rate after completion of RLI with chemotherapy. In evaluable samples, circulating tumor DNA and DLBCL90 assays were performed. RESULTS The median age was 63.5 years (range, 29-83 years) with 28% age 70 years or older. The revised international prognostic index identified 42% as high risk, and 62% were double expressor of MYC and BCL2 protein. The ORR after two cycles of RLI was 86.2%, and the complete response rate at the end of RLI-chemotherapy was 94.5%. With a median follow-up of 31 months, the progression-free survival and overall survival were at 91.3% and 96.6% at 2 years, respectively. CONCLUSION Smart Start is the first study, to our knowledge, to treat newly diagnosed DLBCL with a targeted therapy combination before chemotherapy. RLI produced a high ORR, and RLI with chemotherapy resulted in durable responses. This establishes the potential for developing biologically driven and noncytotoxic first-line therapies for DLBCL.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".