Correlative Analyses of Patient and Clinical Characteristics Associated with Efficacy in Tisagenlecleucel-Treated Relapsed/Refractory Diffuse Large B-Cell Lymphoma Patients in the Juliet Trial
Notice bibliographique
Résumé
Background: Tisagenlecleucel, an anti-CD19 chimeric antigen receptor (CAR)-T cell therapy, has demonstrated durable responses and a manageable safety profile in adult patients (pts) with relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL). We report the correlation of pre- and post-infusion factors and biomarkers with efficacy in tisagenlecleucel-treated pts with r/r DLBCL. Methods: Results from JULIET, a global, single-arm, pivotal, phase 2 trial of tisagenlecleucel in adult pts with r/r DLBCL, were analyzed to identify baseline disease and pt characteristics and serum biomarkers that may correlate with efficacy. The relationships between markers such as lactate dehydrogenase (LDH), baseline tumor volume (TV, within 30 days prior to infusion) using positron emission tomography/computed tomography, pre-treatment C-reactive protein (CRP) and thrombocytopenia, as well as cytokine release syndrome (CRS; Penn scale)/neurological events (NE; CTCAE v4.03) severity, and month 3 response (complete or partial response [CR, PR]), progression-free survival (PFS), and overall survival (OS) were assessed. Results: As of December 11, 2018, 115 pts were infused with tisagenlecleucel and evaluable for efficacy. Baseline TV did not correlate with month 3 response; we then examined additional pre-treatment candidate factors. Median LDH levels at enrollment, pre-lymphodepleting chemotherapy (LDC), and pre-infusion were higher in nonresponders (NRs) compared with pts achieving CR/PR. 15/16 pts with pre-infusion grade 3/4 thrombocytopenia (<50 x 109/L) were NRs. Pre-infusion CRP levels did not associate with month 3 response. Univariate and multivariate logistic regression analyses showed that high pre-infusion LDH levels (defined as higher than 2-fold the upper limit of normal [ULN]) were independently associated with NRs. Compared with pts with normal levels of LDH at pre-infusion, pts with LDH 1-2-fold above the ULN and >2-fold above the ULN had poorer PFS and OS (Figure 1). Similar separations of PFS and OS were observed with LDH levels at enrollment and pre-LDC. Pts with pre-infusion platelet levels <50 x 109/L also had significantly worse PFS and OS compared with pts with platelet levels ≥50 x 109/L. Pre-infusion high TV, high CRP, high ferritin, and low serum albumin associated with worse OS, but not PFS. Additional analyses including factors at enrollment and pre-LDC will be presented. Post-infusion, correlations between severe (grade 3/4) CRS/NE and efficacy were examined. All 13 pts with severe NE were NRs. 9/17 pts with grade 3 CRS and all 9 pts with grade 4 CRS were also NRs. In addition, pts with severe CRS had worse PFS and OS compared with pts with grade 0-2 CRS. Similarly, pts with severe NE had worse PFS and OS compared with pts with grade 0-2 NE. High pre-infusion LDH and pre-infusion grade 3/4 thrombocytopenia and severe NE were independently associated with poorer PFS in Cox regression analyses. Severe CRS did not correlate with PFS in a multivariate Cox model. Pts with severe CRS who were also NRs had higher median baseline TV compared with other pts. Notably, the highest levels of serum biomarkers (eg, ferritin, IL4, IL8, IL10), highest LDH, and lowest platelet counts within 1 month post-infusion were observed in pts with severe CRS who were also NRs, compared with pts with severe CRS who achieved CR/PR and pts with grade 0-2 CRS. Analyses of the impact of tocilizumab and steroids usage are ongoing. Conclusions: Multivariate analyses identified that high levels of pre-infusion LDH, a known marker of tumor burden, metabolic activity, and disease aggressiveness, was associated with NRs at month 3 as well as worse PFS and OS. Grade 3/4 thrombocytopenia at pre-infusion and grade 3/4 NE were also associated with poor efficacy outcomes. Furthermore, the highest serum biomarker profiles post-infusion appeared to associate with pts with severe CRS who were also NRs. Overall, these analyses suggest that a subset of pts with aggressive disease at infusion and/or pts with severe CRS/NE had poorer outcomes in the JULIET trial. These analyses may reinforce the rationale for current and future directions of using CAR-T cell therapy in an earlier line of therapy (during less aggressive/less advanced disease), optimizing pt care, and developing interventions to prevent severe CRS and/or NE. Clinical trial information: NCT02445248. Disclosures Westin: Genentech: Other: Advisory Board, Research Funding; Novartis: Other: Advisory Board, Research Funding; Curis: Other: Advisory Board, Research Funding; Celgene: Other: Advisory Board, Research Funding; 47 Inc: Research Funding; Juno: Other: Advisory Board; MorphoSys: Other: Advisory Board; Unum: Research Funding; Janssen: Other: Advisory Board, Research Funding; Kite: Other: Advisory Board, Research Funding. Tam:Roche: Honoraria; AbbVie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; BeiGene: Honoraria; Novartis: Honoraria. Jaeger:Novartis, Roche, Sandoz: Consultancy; Celgene, Roche, Janssen, Gilead, Novartis, MSD, AbbVie, Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen, AbbVie, Celgene, Eisai, Gilead, Janssen, Novartis, Roche, Takeda Millennium, MSD, BMS, Sanofi: Honoraria; AbbVie, Celgene, Gilead, Novartis, Roche, Takeda Millennium: Research Funding. McGuirk: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; Astellas: Research Funding; Fresenius Biotech: Research Funding; Novartis: Research Funding. Holte:Novartis: Honoraria, Other: Advisory board. Waller: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 ; Amgen: Consultancy; Kalytera: Consultancy. Jaglowski:Juno: Consultancy, Other: advisory board; Unum Therapeutics Inc.: Research Funding; Kite: Consultancy, Other: advisory board, Research Funding; Novartis: Consultancy, Other: advisory board, Research Funding. Bishop: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; CRISPR Therapeutics: 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; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Andreadis:Celgene: Research Funding; Juno: Research Funding; Novartis: Research Funding; Genentech: Consultancy, Employment; Kite: Consultancy; Gilead: Consultancy; Jazz Pharmaceuticals: Consultancy; Pharmacyclics: Research Funding; Merck: Research Funding; Roche: Equity Ownership. Foley:Janssen: Speakers Bureau; Amgen: Speakers Bureau; Celgene: Speakers Bureau. 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:EBMT/EHA: Other: Travel support; IACH: Other: Travel support; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; DGHO: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); ISCT: Other: Travel support. Teshima:Novartis: Honoraria, Research Funding. Schuster:AbbVie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Novartis: Honoraria, Patents & Royalties: Combination CAR-T and PD-1 Inhibitors, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Loxo Oncology: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Nordic Nanovector: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria, Research Funding; Acerta: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding. Bachanova:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Incyte: Research Funding; GT Biopharma: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Research Funding; Celgene: Research Funding. Maziarz:Kite: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Research Funding; Incyte: Consultancy, Honoraria; Celgen
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».