Feasibility and efficacy of CD19-targeted CAR T cells with concurrent ibrutinib for CLL after ibrutinib failure
Bibliographic record
Abstract
We previously reported durable responses in relapsed or refractory (R/R) chronic lymphocytic leukemia (CLL) patients treated with CD19-targeted chimeric antigen receptor-engineered (CD19 CAR) T-cell immunotherapy after ibrutinib failure. Because preclinical studies showed that ibrutinib could improve CAR T cell-antitumor efficacy and reduce cytokine release syndrome (CRS), we conducted a pilot study to evaluate the safety and feasibility of administering ibrutinib concurrently with CD19 CAR T-cell immunotherapy. Nineteen CLL patients were included. The median number of prior therapies was 5, and 17 patients (89%) had high-risk cytogenetics (17p deletion and/or complex karyotype). Ibrutinib was scheduled to begin ≥2 weeks before leukapheresis and continue for ≥3 months after CAR T-cell infusion. CD19 CAR T-cell therapy with concurrent ibrutinib was well tolerated; 13 patients (68%) received ibrutinib as planned without dose reduction. The 4-week overall response rate using 2018 International Workshop on CLL (iwCLL) criteria was 83%, and 61% achieved a minimal residual disease (MRD)-negative marrow response by IGH sequencing. In this subset, the 1-year overall survival and progression-free survival (PFS) probabilities were 86% and 59%, respectively. Compared with CLL patients treated with CAR T cells without ibrutinib, CAR T cells with concurrent ibrutinib were associated with lower CRS severity and lower serum concentrations of CRS-associated cytokines, despite equivalent in vivo CAR T-cell expansion. The 1-year PFS probabilities in all evaluable patients were 38% and 50% after CD19 CAR T-cell therapy, with and without concurrent ibrutinib, respectively (P = .91). CD19 CAR T cells with concurrent ibrutinib for R/R CLL were well tolerated, with low CRS severity, and led to high rates of MRD-negative response by IGH sequencing.
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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 | 0.000 | 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".