T-cell responses against CD19-targeted CAR T cells varnimcabtagene autoleucel (ARI-0001): implications for immune response and therapy outcomes
Bibliographic record
Abstract
Patients with B-cell malignancies receiving CD19-targeted chimeric antigen receptor (CAR) T-cell therapy may experience treatment failure due to various mechanisms. One significant factor is the host’s immune response against the CAR-T product, mainly triggered by the immunogenicity of non-human sequences present in the CAR construct. In the CART19-BE-01 clinical trial ( NCT03144583 , ClinicalTrials.gov), patients with relapsed/refractory B-cell malignancies received treatment with varnimcabtagene autoleucel (ARI-0001 cells), a CD19-targeted autologous CAR-T product. During follow-up, both humoral and cellular immune responses were assessed against the extracellular domain of ARI-0001 cells, which was derived from the murine monoclonal antibody A3B1. Here, we report the case of a patient with B-cell acute lymphoblastic leukemia who required two infusions of ARI-0001 cells. Between the first and second CAR-T19 infusions, human antimurine antibodies were detected, which in vitro diminished the cytotoxicity of ARI-0001 cells against the CD19-positive NALM6 cell line. Furthermore, CD4 + specific T cells against the CAR19 construct were isolated from the patient’s peripheral blood mononuclear cells, showing recognition of the murine single-chain variable fragment peptides presented by human leukocyte antigen class II. This case underscores the need to monitor immune responses in patients before considering subsequent CAR-T infusions to preserve treatment efficacy. Trial registration number NCT03144583 .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".