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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".