Secondary Malignancies Following CAR T‐Cell Therapy for B‐Cell Malignancies: A Retrospective Analysis
Bibliographic record
Abstract
Introduction: Chimeric antigen receptor T-cell (CART) therapy has shown clinical efficacy in relapsed and refractory large B-cell malignancies. There is emerging data on the long-term complications including risk of secondary malignancies. We aimed to describe the incidence and characteristics of secondary malignancies following CART therapy. Methods: We performed a single-center retrospective analysis of a prospectively collected cohort of 87 patients who received CART therapy for relapsed/refractory B-cell malignancies between January 2020 and August 2023. Results: Seven patients (8.0%) developed a secondary malignancy, with a median age of 57 years (40-77) and mean time to onset of 16.9 months (3-34.5 months). Two patients were diagnosed with MDS and five with AML. Six patients had cytogenetic abnormalities at diagnosis of MDS/AML. Five patients received hypomethylating agents and two received an allogeneic stem cell transplant as treatment for their myeloid malignancy. Two patients were alive at 12 months after diagnosis of their myeloid malignancy. The 12-month cumulative incidence function (CIF) was 2.3% (95% CI, 0.4%-7.3%) and the 24-month CIF was 6.9% (95% CI, 2.4%-14.4%). Conclusion: : The authors have confirmed clinical trial registration is not needed for this submission.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".