Immune Phenotypes and Target Antigens of Clonally Expanded Bone Marrow T Cells in Treatment-Naïve Multiple Myeloma
Multiple myeloma is a hematologic malignancy of monoclonal plasma cells that accumulate in the bone marrow. Despite their clinical and pathophysiologic relevance, the roles of bone marrow-infiltrating T cells in treatment-naïve patients are incompletely understood. We investigated whether clonally expanded T cells (i) were detectable in multiple myeloma bone marrow, (ii) showed characteristic immune phenotypes, and (iii) whether dominant clones recognized antigens selectively presented on multiple myeloma cells. Single-cell index sorting and T-cell receptor (TCR) αβ sequencing of bone marrow T cells from 13 treatment-naïve patients showed dominant clonal expansion within CD8+ cytolytic effector compartments, and only a minority of expanded T-cell clones expressed the classic immune-checkpoint molecules PD-1, CTLA-4, or TIM-3. To identify their molecular targets, TCRs of 68 dominant bone marrow clones from five selected patients were reexpressed and incubated with multiple myeloma and non-multiple myeloma cells from corresponding patients. Only 1 of 68 TCRs recognized antigen presented on multiple myeloma cells. This TCR was HLA-C-restricted, self-peptide-specific and could be activated by multiple myeloma cells of multiple patients. The remaining dominant T-cell clones did not recognize multiple myeloma cells and were, in part, specific for antigens associated with chronic viral infections. In conclusion, we showed that dominant bone marrow T-cell clones in treatment-naïve patients rarely recognize antigens presented on multiple myeloma cells and exhibit low expression of classic immune-checkpoint molecules. Our data provide experimental context for experiences from clinical immune-checkpoint inhibition trials and will inform future T cell-dependent therapeutic strategies.
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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
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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