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Late relapse after CAR-T cell therapy for adult patients with hematologic malignancies: A definite evidence from systematic review and meta-analysis on individual data

医学 套细胞淋巴瘤 淋巴瘤 滤泡性淋巴瘤 汽车T细胞治疗 人口 来那度胺 多发性骨髓瘤 肿瘤科 细胞因子释放综合征 免疫学 内科学 癌症 免疫疗法 嵌合抗原受体 环境卫生
作者
Alessia Zinzi,Mario Gaio,Valerio Liguori,Cecilia Cagnotta,Donatella Paolino,Giuseppe Paolisso,Giuseppe Castaldo,Gianfranco Nicoletti,Francesco Rossi,Francesco Rossi,Concetta Rafaniello
出处
期刊:Pharmacological Research [Elsevier]
卷期号:190: 106742-106742
标识
DOI:10.1016/j.phrs.2023.106742
摘要

Chimeric Antigen Receptor (CAR)-modified T lymphocytes represent one of the most innovative and promising approaches to treating hematologic malignancies. CAR-T cell therapy is currently being used for the treatment of relapsed/refractory (r/r) B-cell malignancies including Acute Lymphoblastic Leukemia, Large B-Cell Lymphoma, Follicular Lymphoma, Multiple Myeloma and Mantle Cell Lymphoma. Despite the unprecedented clinical success, one of the major issues of the approved CAR-T cell therapy - tisagenlecleucel, axicabtagene, lisocabtagene, idecabtagene, ciltacabtagene and brexucabtagene - is the uncertainty about its persistence which in turn could lead to weak or no response to therapy with malignancy recurrence. Here we show that the prognosis of patients who do not respond to CAR-T cell therapy is still an unmet medical need. We performed a systematic review and meta-analysis collecting individual data on Duration of Response from at least 12-month follow-up studies. We found that the pooled prevalence of relapse within the first 12 months after CAR-T infusion was 61% (95% CI, 43%-78%); moreover, one year after the infusion, the analysis highlighted a pooled prevalence of relapse of 24% (95% CI, 11%-42%). Our results suggest that identifying potential predictive biomarkers of response to CAR-T therapy, especially for patients affected by the advanced stage of blood malignancies, could lead to stratification of the eligible population to that therapy, recognizing which patients will benefit and which will not, helping regulators to make decision in that way.
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