CAR T-Related Toxicities Based on Dynamic Proteomic Profiles Identifies Risk Factors for Cytokine Release Syndrome (CRS) and Immune Effector Cell -Associated Neurotoxicity Syndrome (ICANS)
Notice bibliographique
Résumé
TK and SM are Co-first authors INTRODUCTION Treatment with chimeric antigen receptor (CAR) T-cells significantly improved outcomes in relapsed/refractory non-Hodgkin lymphoma (NHL) and multiple myeloma (MM). CAR-T activation and anti-tumor cytotoxicity are associated with bystander inflammatory reactions resulting in CRS and/or ICANS. Due to complex cytokine profiles, disease heterogeneity, and variability between commercial CAR-T products, identification of risk factors associated with CRS and/or ICANS has been challenging. In this study, we used plasma proteomic profiling at different timepoints to identify possible inflammatory mediators associated with CRS and ICANS METHODS We prospectively collected plasma samples from patients who received CAR-T cells therapy between 9/2021 to 12/2022 at several time points - before lymphodepletion chemotherapy on day -5 (relative to CAR-T cell infusion), prior to CAR T-cell infusion on day 0, and post CAR T-cell therapy on days 1, 2, 3, and 7. Protein profiling analyses were conducted at Eve Technologies (Calgary, Alberta, Canada) using an assay measuring 71 total cytokines and chemokines. Proteins levels were compared across different time points used Wilcoxon rank test, while features associated with CRS/ICANS were identified using logistic regression. Receiver operating characteristic (ROC) analysis used to identify variables predictive for CRS. Area under the curve (AUC) of at least 0.8 was used and best cutoffs were determined according to Youden index. P-values <0.05 were considered statistically significant. This study was supported in part by The Frederick A. Deluca Foundation. RESULTS Overall, 56 patients with available cytokine assays at all time points were included. The median age was 65 years (IQR: 57-74) and 70% were men. Of all patients, 26 (46%) had diffuse large B-cell lymphoma (DLBCL), 23 (41%) MM, 4 (7%) mantle cell lymphoma, and 3 (6) follicular lymphoma. Ide-cel (39%), liso-cel (36%), and axi-cel (17%) were the most used CAR-T cell products. All patients received lymphodepleting chemotherapy with fludarabine/cyclophosphamide. In total, 35 (63%) patients developed CRS (grade 1, 89%; grade 2, 8%; grade 3, 3%) and 18 (32%) patients developed ICANS (grade 1, 72%; grade 2, 22%; grade 3, 6%). Compared to patients who did not develop CRS, patients with CRS had lower median absolute lymphocyte counts at day -5 (0.02 x10 9/L vs. 0.05, p=0.0146), higher baseline CRP (13 vs. 4 mg/L, p=0.0005), and higher ferritin (914 vs. 442 mg/L, p=0.048). No differences in the type of CAR-T products (p=0.090), percentages of DLBCL or MM (p=0.270) were observed between CRS and no CRS cohorts ( Panel-A). First, we investigated the proteomic profiles at baseline for CRS odds. Hemoglobin (odd ratio [OR]: 0.6, 95%CI: 0.4-0.8) was associated with lower odds for CRS while IL6 (2.0, 1.2-3.3) and stem cell factor (scf 2.2, 1.2-4.2) were associated with higher odds of CRS. We then analyzed the differences in cytokine levels between day 0 and day 3 to select cytokines with significant changes for further analysis ( Panel-B). At day 3, groa (1.9, 1.1-3.3), IL3 (1.6, 1.2-2.1), IL5 (1.5, 1.2-1.9), IL6 (1.7, 1.3-2.3), IL10 (2.0, 1.3-3.0), TNFα (2.0, 1.1-3.6), and mcp2 (2.5, 1.2-5.3) were all associated with higher odds for CRS. Based on ROC analysis at day 3, best cutoff points to estimate CRS (value, sensitivity/specificity) for IL3 (3, 80%/90%), IL5 (197, 74%/85%), IL6 (11, 70%/85%), and IL10 (53, 74%/85%) were identified. Based on that, elevated IL3 (OR:24, 95%CI: 6-105), IL5 (11, 3-40), IL6 (21, 5-95), and IL10 (12, 3-46) were associated with higher odds for CRS. For ICANS, day 3 IL3 (1.5, 1.2-1.9), IL6 (1.2, 1.1-1.5), IL8 (2.1, 1.4-3.3), and IL10 (1.7, 1.3-2.4) were associated with higher odds for ICANS. Best cutoff points to estimate ICANS at day 3 (value, sensitivity/specificity) for IL3 (5, 78%/76%), IL6 (115, 78%/78%), IL10 (130, 81%/80%), and IL8 (21, 83%/81%) were identified. Based on that, elevated IL3 (OR:10, 95%CI: 3-37), IL6 (11, 3-43), IL10 (13, 3-51), and IL8 (19, 4-81) were associated with higher odds for ICANS. CONCLUSIONS In our comprehensive plasma proteomic profiles analysis, we identified cutoffs for IL3, IL6, IL5 and IL10 that may be predictive for CRS and ICANS regardless of CAR-T cell product. Our results are clinically applicable and may be used to recognize patients at risk for CRS and/or ICANS who may be eligible for prophylactic therapies.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».