Acalabrutinib in combination with rituximab and lenalidomide in patients with relapsed or refractory follicular lymphoma: Results of the phase 1b open‐label study ( <scp>ACE</scp> ‐ <scp>LY</scp> ‐003)
Bibliographic record
Abstract
Summary Patients with relapsed/refractory (R/R) follicular lymphoma (FL) have limited effective treatment options. Bruton tyrosine kinase inhibitors (BTKis) increase the anti‐tumoural phenotype of tumour‐associated macrophages, providing rationale to combine them with rituximab and lenalidomide (R 2 ). Acalabrutinib, a second‐generation BTKi, has potential to improve R 2 efficacy without increasing T‐cell–mediated toxicity due to its lack of interleukin‐2–inducible T‐cell kinase inhibition. Here, we report safety and efficacy from a phase 1b dose‐finding study (NCT02180711) evaluating acalabrutinib plus R 2 in patients with R/R FL. Overall, 29 patients received acalabrutinib plus R 2 (lenalidomide 15 mg, n = 8; lenalidomide 20 mg, n = 21). At a median acalabrutinib exposure of 21 months, the most common grade ≥3 treatment‐emergent adverse event (TEAE) was neutropenia (37.9%). The incidence of grade ≥3 serious TEAEs was 37.5% and 52.4% in the lenalidomide 15‐mg and 20‐mg cohorts, respectively; overall, the most common were COVID‐19 pneumonia, COVID‐19 infection and pneumonia. Earlier treatment withholdings/reductions were observed in the 20‐mg cohort. With a median follow‐up of 34.1 months, the overall response rate was 75.9%. The complete response rate was 25.0% and 42.9% in the lenalidomide 15‐ and 20‐mg cohorts, respectively. Due to acceptable toxicity and preliminary efficacy, the lenalidomide 20‐mg dose was selected for further investigation.
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 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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".