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Record W4379981707 · doi:10.1002/hon.3164_398

Neurotoxicity in patients with CNS lymphomas treated with CAR‐T cell therapy. A LOC network study

2023· article· en· W4379981707 on OpenAlexaboutno aff
H Tost, Nicolas Weiss, Sylvain Choquet, Monica Ribeiro, Cristina Birzu, Loïc Le Guennec, Natalia Shor, Véronique Morel, Sebastian Spataro Solorzano, Laëtitia Souchet, Ines Boussen, Damien Roos‐Weil, Valérie Friser, Nuno Miranda, Magali Le Garff‐Tavernier, Carole Soussain, Khê Hoang‐Xuan, Dimitri Psimaras, Caroline Houillier

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeurotoxicityLymphomaEncephalopathyInternal medicineToxicityGastroenterologyPediatricsOncology

Abstract

fetched live from OpenAlex

Introduction: CAR-T cell therapy represents one of the major progress of the last years in aggressive B-cell lymphomas. Its use in central nervous system (CNS) lymphomas has been limited due to the fear of neurotoxicity but several recent studies have showed promising efficacy in this setting. The aim of this study was to focus on neurotoxicity in CNS lymphoma patients treated with CAR-T cells. Methods: Patients with isolated CNS relapse of DLBCL treated with commercial CAR-T cells at Pitié-Salpêtrière hospital were retrospectively selected. We considered only neurological deterioration for which other causes than CAR-T cell toxicity had been reasonably ruled out. Results: Thirty patients (median age: 65, 17 women, 19 primary, 11 secondary CNS lymphomas, median of 3 previous lines) received CAR-T cells (Tisa-cel: N = 23, axi-cel, N = 7) between May 2020 and December 2022. At the time of CAR-T, 15 (50%) had stable or progressive disease, median Karnofsky Performance Status (KPS) was 70, median Montreal Cognitive Assessment (MoCA) score was 22 and median immune effector cell-associated encephalopathy (ICE) score was 10. Twelve (40%) patients didn't experience any neurotoxicity after CAR-T, whereas 9 (30%), 3 (10%), 2 (7%), 4 (13%) patients experienced grade 1, 2, 3 or 4 toxicity according to ASTCT classification. The signs of neurotoxicity began at a median of 5 days after CAR-T. The most common symptoms were cognitive disorders (N = 18), motor deficit (N = 4), balance disorders (N = 8), consciousness disorders (N = 5), seizures (N = 3) and movement disorders (N = 3). Among cognitive symptoms, there was predominantly a worsening of pre-existing symptoms regarding memory impairment (12/15 patients) or dysexecutive syndrome (11/13), while dysgraphia or dyscalculia were most frequently new symptoms (in 14/15 and 7/11 patients). Brain MRI showed pseudo-progression in 4 cases. On lumbar puncture, median IL-6 level and median cellularity were higher in patients with grade 2 to 4 neurotoxicity compared to other patients (243 vs. 41 pg/ml, 15 vs. 3 cells/mm3). We found no association between risk of neurotoxicity and age, gender, pre-CART KPS, MoCA or ICE score, primary vs. secondary CNS lymphoma or type of CAR-T. Twelve patients (40%) received steroids, for a median of 10 days, leading to an improvement in a median of 5 days; one received anakinra. Median duration with neurological impairment was 13 days, but was >3 months in 3 patients, including one who finally died from neurotoxicity. Conclusions: The majority of CNS lymphoma patients didn't experience severe neurotoxicity following CAR-T cells, so that this treatment shouldn't be contraindicated in this population. However, the percentage of severe and prolonged neurotoxicity is probably higher than in systemic lymphomas and a close neurological follow-up is warranted in these complex situations. Further studies are needed to better predict severe neurotoxicity. Keyword: Cellular therapies Conflicts of interests pertinent to the abstract. S. Choquet Consultant or advisory role: Kite-Gilead, Novartis

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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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.322
Teacher spread0.280 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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