Single-Cell Transcriptomics Reveals Immune Reconstitution in Patients with R/R T-ALL/LBL Treated with Donor-Derived CD7 CAR-T Therapy
Purpose: CD7 chimeric antigen receptor T (CAR-T) therapy has potent antitumor activity against relapsed/refractory (R/R) T-cell acute lymphoblastic leukemia/lymphoma (T-ALL/LBL), however, immune reconstitution after CAR-T remains largely unknown. Patients and methods: An open-label phase I clinical trial (ChiCTR2200058969) was initiated to evaluate safety and efficacy of donor-derived CD7 CAR-T cells in 7 R/R T-ALL/LBL patients. CAR-T cells were detected by flow cytometry and PCR. Cytokine levels were quantified by cytometric bead arrays. Single-cell RNA sequencing (scRNA-seq) was adopted to profile immune reconstitution. Results: Optimal complete remission (CR) was 100% on day 28, and median followed-up time was 4 months. Leukopenia, thrombocytopenia, and neutropenia were observed in 6 patients, and infections occurred in 5 patients. Two patients died of serious infection and one died of a brain hemorrhage. CAR-T cells expanded efficiently in all patients. CD7+ T cells were eliminated in peripheral blood on day 11 after infusion, and CD7- T cells dramatically expanded in all patients. scRNA-seq suggested that immunologic activities of CD7- T cells were stronger than those of T cells before infusion due to higher expression levels of T-cell function-related pathways, and major characters of such CD7- T cells were activation of autoimmune-related pathways. Monocyte loss was found in 2 patients who died of serious infections, indicating the main cause of the infections after infusion. S100A8 and S100A9 were identified as potential relapse markers due to their notable upregulation in leukocyte lineage in relapsed patients versus non-relapse controls. Conclusions: Our data revealed cellular level dynamics of immune homeostasis of CD7 CAR-T therapy, which is valuable for optimizing the treatment of R/R T-ALL/LBL.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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