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Enregistrement W2983803118 · doi:10.1182/blood-2019-128302

Impact of Tisagenlecleucel Chimeric Antigen Receptor (CAR)-T Cell Therapy Product Attributes on Clinical Outcomes in Adults with Relapsed or Refractory Diffuse Large B-Cell Lymphoma (r/r DLBCL)

2019· article· en· W2983803118 sur OpenAlexaff
Veronika Bachanová, Constantine S. Tam, Peter Borchmann, Ulrich Jaeger, Joseph P. McGuirk, Harald Holte, Edmund K. Waller, Samantha Jaglowski, Michael Bishop, Charalambos Andreadis, S.R. Foley, Jason R. Westin, Isabelle Fleury, P. Joy Ho, Stephan Mielke, Takanori Teshima, Gilles Salles, Stephen J. Schuster, Richard T. Maziarz, Koen van Besien, Koji Izutsu, Marie José Kersten, John Magenau, Nina D. Wagner‐Johnston, Koji Kato, Paolo Corradini, Jufen Chu, Irina Gershgorin, Therese Choquette, Lida Pacaud, Margit Jeschke

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensUniversité de MontréalHôpital Maisonneuve-RosemontHamilton Health Sciences
Organismes subventionnairesnon disponible
Mots-clésChimeric antigen receptorDiffuse large B-cell lymphomaMedicineRefractory (planetary science)LymphomaCytokine release syndromeInternal medicineOncologyCancer researchImmunotherapyImmunologyBiologyCancer

Résumé

récupéré en direct d'OpenAlex

Background: In the phase 2 JULIET trial, tisagenlecleucel, an anti-CD19 CAR-T cell therapy, demonstrated durable responses and manageable safety in adult patients (pts) with r/r DLBCL. Here, we examine the impact of key product cellular attributes of tisagenlecleucel on clinical outcomes. Methods: JULIET is a single-arm, global, phase 2 trial of tisagenlecleucel in adult pts with r/r DLBCL. Samples from 115 tisagenlecleucel individual products were examined at the end of manufacturing at the batch release testing for various product attributes (Table). Additional detailed immunophenotyping for 66 attributes was conducted on previously frozen product samples via flow cytometry (FC). For each cell population of CAR+ T cells, the percentage and absolute number of the subpopulation were analyzed. Univariate and multivariate analyses were performed to evaluate effects of product attributes and CAR+ T-cell phenotypes on efficacy (Month 3 response [M3R], duration of response [DOR], progression-free survival [PFS], overall survival [OS]) and safety (cytokine release syndrome [CRS] and neurological events [NE], grade 0-2 [low] vs 3-4 [severe]). Several exploratory approaches, including machine learning methods (eg, elastic net and random forest), were pursued in conjunction with logistic regression to identify a set of variables associated with clinical outcomes. We included clinically relevant characteristics evaluated at baseline per protocol (LDH, CRP, and tumor volume) with the product attributes in multivariate modeling. Logistic regression was used to model M3R, CRS, and NE. Cox regression was used to model DOR, PFS, and OS. Results: As of December 11, 2018, 115 pts were infused and evaluable. The median T-cell transduction efficiency by FC was 28% (range, 5.3-63.2%); no relationship of these attributes with efficacy (M3R, DOR, PFS, or OS) or safety (severe CRS or NE) was observed. The percentage of viable cells had no impact on efficacy or safety outcomes; this was anticipated since tisagenlecleucel dose is formulated based on the number of viable CAR+ T cells. Tisagenlecleucel demonstrated in vitro functional activity upon CD19-specific stimulation, as evidenced by IFNγ release, with a wide range among different batches (range, 23.7-938 fg/CAR+ cell). Durable responses were observed across the entire range of IFNγ release; high IFNγ release was not associated with severe CRS or NE. The median ratio of CAR+ CD4+ to CD8+ cells was 3.70 (range, 0.26-65.3); no relationship with clinical outcomes was observed (Figure). CAR+ T cells showed variability in T-cell phenotypes, with central memory (CM) cells as the predominant subpopulation of both CD4+ and CD8+ CAR+ T cells (Figure). The majority of CAR+ T cells were highly activated (co-expressing HLA-DR and CD38), as measured by FC. Relative and absolute number of less mature T cells (naive and CM T cells) in the product did not correlate with efficacy. There was no significant correlation between cell populations and efficacy on multivariate analyses. For CRS, the total number of certain CD4+ T cells expressing activation markers (HLA-DR+, CD25+, or HLA-DR+CD38+) and CM cells showed trends of correlation with more severe CRS, but none of these were significant after p-value adjustment; in a multivariate regression model adjusted for LDH and other clinically relevant factors, HLA-DR+ CD38+CD4+ T cells showed a correlation with severe CRS. Correlation analyses did not reveal product attributes significantly related to severe NE. Conclusions: In JULIET, tisagenlecleucel CAR-T cell product attributes had no significant impact on efficacy or NE; the total number of activated CD4+ cells infused positively correlated with higher-grade CRS. There is great variability in the product attributes, especially with respect to T-cell phenotypes, though this variability appears to play a minor role on efficacy. Additional analyses with larger data sets are required to confirm these findings. ClinicalTrials.gov Identifier: NCT02445248. Disclosures Bachanova: Novartis: Research Funding; Gamida Cell: Research Funding; GT Biopharma: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Incyte: Research Funding. Tam:BeiGene: Honoraria; Janssen: Honoraria, Research Funding; Roche: Honoraria; Novartis: Honoraria; AbbVie: Honoraria, Research Funding. Jaeger:Novartis, Roche, Sandoz: Consultancy; AbbVie, Celgene, Gilead, Novartis, Roche, Takeda Millennium: Research Funding; Amgen, AbbVie, Celgene, Eisai, Gilead, Janssen, Novartis, Roche, Takeda Millennium, MSD, BMS, Sanofi: Honoraria; Celgene, Roche, Janssen, Gilead, Novartis, MSD, AbbVie, Sanofi: Membership on an entity's Board of Directors or advisory committees. McGuirk:Novartis: Research Funding; Fresenius Biotech: Research Funding; Astellas: Research Funding; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; ArticulateScience LLC: Other: Assistance with manuscript preparation; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Holte:Novartis: Honoraria, Other: Advisory board. Waller:Amgen: Consultancy; Kalytera: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Other: Travel expenses, Research Funding; Cerus Corporation: Other: Stock, Patents & Royalties; Chimerix: Other: Stock; Cambium Oncology: Patents & Royalties: Patents, royalties or other intellectual property . Jaglowski:Juno: Consultancy, Other: advisory board; Kite: Consultancy, Other: advisory board, Research Funding; Novartis: Consultancy, Other: advisory board, Research Funding; Unum Therapeutics Inc.: Research Funding. Bishop:CRISPR Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Andreadis:Celgene: Research Funding; Novartis: Research Funding; Jazz Pharmaceuticals: Consultancy; Roche: Equity Ownership; Pharmacyclics: Research Funding; Merck: Research Funding; Gilead: Consultancy; Kite: Consultancy; Genentech: Consultancy, Employment; Juno: Research Funding. Foley:Celgene: Speakers Bureau; Amgen: Speakers Bureau; Janssen: Speakers Bureau. Westin:Unum: Research Funding; Genentech: Other: Advisory Board, Research Funding; Novartis: Other: Advisory Board, Research Funding; Janssen: Other: Advisory Board, Research Funding; Juno: Other: Advisory Board; Kite: Other: Advisory Board, Research Funding; 47 Inc: Research Funding; Curis: Other: Advisory Board, Research Funding; MorphoSys: Other: Advisory Board; Celgene: Other: Advisory Board, Research Funding. Fleury:Gilead: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; AstraZeneca: Consultancy. Ho:Janssen: Other: Trial Investigator meeting travel costs; Celgene: Other: Trial Investigator meeting travel costs; La Jolla: Other: Trial Investigator meeting travel costs; Novartis: Other: Trial Investigator meeting travel costs. Mielke:Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; DGHO: Other: Travel support; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; EBMT/EHA: Other: Travel support; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau; ISCT: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; IACH: Other: Travel support. Teshima:Novartis: Honoraria, Research Funding. Salles:Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria. Schuster:Celgene: Consultancy, Honoraria, Research Funding; Acerta: Consultancy, Honoraria, Research Funding; Loxo Oncology: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria, Research Funding; Merck: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Nordic Nano

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,032
Tête enseignante GPT0,335
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2019
Routes d'admission1
Résumé présentoui

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