The Impact of Chimeric Antigen Receptor (CAR) T Cell Therapy: Its Potential to Reshape Rheumatology Practice
Bibliographic record
Abstract
In recent years, genetically modified T cell therapy, using chimeric antigen receptor (CAR)‑engineered T cells, has revolutionized the field of rheumatology. While CAR T cell therapy is approved by government agencies, including Health Canada, as a standard treatment for B cell lymphoproliferative malignancies, it has also shown remarkable efficacy in refractory cases of rheumatic diseases, including systemic lupus erythematosus, systemic sclerosis, idiopathic inflammatory myopathies, ANCA-associated vasculitis, and rheumatoid arthritis. A single infusion of CAR T cells has demonstrated the potential to induce long-term drug-free remission in most cases. This therapy achieves profound B cell depletion in both blood and tissues—an effect not typically observed with conventional antibody-based B cell-target therapies. Despite its transformative potential, several challenges remain, including questions around long-term safety, high costs, limited accessibility, and the absence of standardized guidelines, which complicate its broader application. Rheumatologists face practical uncertainties, such as determining the optimal timing for treatment, selecting suitable patients, and identifying which diseases might benefit the most from this therapy. This editorial explores the fundamental principles of CAR T cell therapy, highlights the unresolved challenges, and provides insights into how rheumatologists can optimize its use for managing rheumatic diseases. (Please note that this manuscript was written in April 2025. Given the rapid advancements and emerging evidence in this field, there may be updates by the time this article is published.)
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".