Real-World Insights into CAR T-Cell Therapy: Efficacy and Safety of Kymriah in B-ALL and NHL
Bibliographic record
Abstract
Background: Chimeric Antigen Receptor (CAR) T-cell therapy has emerged as a revolutionary treatment for certain cancers, particularly B-cell malignancies. This study focused on the real-world outcomes of Kymriah (tisagenlecleucel), a CAR T-cell therapy targeting CD19, by analyzing data from the Cellular Immunotherapy Data Resource (CIDR). A total of 410 patients with relapsed or refractory B-cell Acute Lymphoblastic Leukemia (B-ALL) and Non-Hodgkin Lymphoma (NHL) were included. Methods: Data were collected from 73 treatment centers across the U.S. and Canada. Key outcomes measured included Cytokine Release Syndrome (CRS), Immune-effector Cell-Associated Neurotoxicity Syndrome (ICANS), overall response rate (ORR), duration of response (DOR), and survival rates. Statistical analyses were performed using descriptive statistics, Kaplan-Meier methods, and logistic regression. Results: CRS was observed in 54.3% of B-ALL patients, with severe cases in 16.1%. The complete remission rate in B-ALL patients was 85.5%, with a 12-month overall survival rate of 76.4%. In NHL patients, the overall response rate was 62.4%, with 38.2% achieving complete remission. Severe CRS and ICANS were less frequent in NHL patients. Conclusion: Kymriah demonstrated high efficacy in both B-ALL and NHL, with manageable side effects. However, ongoing monitoring is essential to optimize outcomes and reduce adverse events. This study provides valuable real-world data, contributing to the growing understanding of CAR T-cell therapy’s potential in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".