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Postmarketing safety profile of chimeric antigen receptor (CAR) T cell therapies in diffuse large B-cell lymphoma (DLBCL): Analysis of real-world (RW) AE reporting from the FDA Adverse Event Reporting System (FAERS).

2025· article· en· W4410821212 on OpenAlexaff
Matthew A. Lunning, Marcela V. Maus, Moataz Ellithi, Farah Toron, Robert Braun, Lin Wang, Pearl Wang, Maxwell Jones, Magdi Elsallab

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsChimeric antigen receptorMedicineSafety profileAdverse effectLymphomaAdverse Event Reporting SystemPostmarketing surveillanceDiffuse large B-cell lymphomaPharmacovigilanceOncologyAntigenInternal medicineCancer researchImmunologyCancerImmunotherapy

Abstract

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7028 Background: CAR T cell therapies have emerged as effective treatment options with deep and durable responses in patients (pt) with DLBCL. Although efficacy and safety data from clinical trials are usually used for drug approval, potentially relevant AEs may not be captured due to limited study follow-up and population. Surveillance databases like FAERS can further characterize safety of therapeutic biologics by capturing RW AEs. We aimed to characterize the safety profile of CAR T cell therapies in the DLBCL population using FAERS. Methods: FAERS was used to identify AEs in pt with DLBCL treated with 2 commercially available CAR T cell therapies, lisocabtagene maraleucel (liso-cel) or axicabtagene ciloleucel (axi-cel). AEs of interest were cytokine release syndrome (CRS), neurological events (NE), hemophagocytic lymphohistiocytosis (HLH), cytopenia, and infections. The primary analysis examined all case reports from Q4 2017 to Q3 2024, the latest available quarterly release. Two sensitivity analyses adjusting for differences in follow-up after FDA approvals were performed: (1) AEs reported any time after liso-cel FDA approval (02/05/2021), which is later, and (2) AEs reported within 2 years of FDA approval for each CAR T cell therapy. Disproportionality analysis compared relative frequency of AEs. Reporting odds ratios (ROR) and 95% CIs were used to identify significant differences in AEs between treatments (ie, 95% CI did not cross 1). An ROR > 1 indicated higher event frequency for axi-cel vs liso-cel. Results: From Q4 2017 to Q3 2024, 3251 AE reports in pt with DLBCL were associated with liso-cel (n = 232) or axi-cel (n = 3019). In disproportionality analysis, axi-cel had significantly higher ROR for CRS (1.48; 95% CI, 1.13–1.93), NE (1.61; 1.23–2.11), and cytopenia (2.45; 1.41–4.24) than liso-cel. Considering limitations of underreporting and incomplete information inherent in FAERS, no statistically significant difference can be inferred for infections (1.36; 95% CI, 0.75–2.48), seizures (1.36; 0.42–4.40), and HLH (1.18; 0.36–3.83). Observed trends were consistent in both sensitivity analyses, where reporting frequencies remained significantly higher with axi-cel vs liso-cel for CRS (1.60; 95% CI, 1.17–2.17 and 2.02; 1.37–2.98), NE (1.59; 1.18–2.15 and 2.04; 1.39–3.00), and cytopenia (2.00; 1.11–3.60 and 2.60; 1.24–5.46), after adjusting for differences in follow-up durations. Conclusions: This retrospective analysis of FAERS, using spontaneous safety reporting data after approval and broader population beyond clinical trials, demonstrated a favorable RW safety profile for liso-cel vs axi-cel for CRS, NE, and cytopenia. These findings provide valuable insights into the safety profile of CAR T cell therapies in DLBCL to inform clinical decision-making and pt management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.435
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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