Pre-CAR-T GTE Scoring of Electroencephalogram Abnormalities As Predictive Biomarkers for Icans

Chimeric Antigen Receptor (CAR) T-cell therapy targeting CD19 has emerged as a promising treatment for refractory and relapsed B-cell lymphomas. However, this therapy is associated with significant adverse events such as Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS). While CRS is well-understood and attributed to cytokine release, the pathogenesis of ICANS remains unclear, necessitating the identification of reliable predictive biomarkers to improve patient outcomes. This study explores the novel use of pre-treatment electroencephalogram (EEG) abnormalities, quantified using the Grand Total EEG (GTE) scoring system, as predictive biomarkers for ICANS in patients undergoing CAR-T cell therapy.

We retrospectively analyzed 55 patients who underwent CAR-T cell therapy at Kyushu University Hospital between November 2022 and December 2023. The evaluations of CRS and ICANS were conducted following the ASTCT consensus guidelines. The GTE score, developed for neurodegenerative disease assessment, was adapted to evaluate pre-treatment EEGs in this novel context. It scores six items (rhythmic background activity, diffuse slow-wave activity, reactivity, paroxysmal activity, focal abnormalities, and sharp-wave activity) on a six-point scale, providing a comprehensive measure of EEG abnormalities.

The median age of the patients was 63 years (range 18-77), with 22 female patients. Diffuse large B-cell lymphoma was the most common primary disease, affecting 67% of patients. CAR-T cell products administered included axicabtagene ciloleucel (axi-cel), tisagenlecleucel (tisa-cel), and lisocabtagene maraleucel (liso-cel). Pre-treatment complete remission (CR) was achieved in 21 patients, and 28 patients had a history of central nervous system (CNS) involvement treatment.

In this study, 95% of patients developed CRS, and 29% developed ICANS. The incidence of severe cases (grade 3 or higher) was less than 10%. We investigated the risk factors for developing ICANS, including CRS presence, disease status, CNS-targeted treatment history, and CAR-T cell product type. ICANS incidence was higher in patients with CRS (30.8%) compared to those without CRS (0%). The incidence of ICANS was not significantly different between patients with and without CNS-targeted treatment history or between those who achieved CR and those who did not. Among the CAR-T cell products, axi-cel was associated with the highest ICANS incidence (50%).

Furthermore, we found that pre-treatment GTE scores were significantly higher in patients who developed ICANS compared to those who did not. The mean GTE score for the ICANS group was 4.94, significantly higher than the non-ICANS group (2.44). This significant difference underscores the utility of the GTE score as a novel and objective tool for identifying patients at high risk for ICANS. Multivariate analysis identified high GTE scores, the use of axi-cel, and higher CRS grades as significant risk factors for ICANS, with odds ratios of 1.79, 8.06, and 4.42, respectively. Other factors such as age and EASIX score did not show significant differences.

Our findings suggest that the GTE scoring system can objectively identify patients at high risk for ICANS prior to CAR-T cell therapy by evaluating EEG abnormalities. This study marks the first significant application of the GTE scoring system in the context of CAR-T therapy, demonstrating its potential as a valuable predictive tool for ICANS. The ability to identify high-risk patients using pre-treatment EEGs could lead to early detection and targeted intervention strategies, ultimately improving patient management and outcomes.

In conclusion, our study demonstrates that higher pre-treatment GTE scores are significantly associated with the development of ICANS in CAR-T cell therapy patients. The novel application of the GTE scoring system provides a new avenue for risk stratification and highlights the importance of EEG abnormalities as predictive biomarkers for ICANS. This knowledge can help identify high-risk patients and improve management strategies, ultimately enhancing patient outcomes in CAR-T cell therapy.