Purpose:Hepatosplenic T-cell lymphoma (HSTCL), mostly derived from γδ T cells, is a rare but very aggressive lymphoma with poor outcomes. In this study, we generated the first single cell landscape for this rare disease and characterized the molecular pathogenesis underlying the disease progression.
Methods:We performed paired single cell RNA-seq and T cell receptor (TCR) sequencing on biopsies from a HSTCL patient pre- and post- chemotherapy treatments. Following by a series of bioinformatics analysis, we investigated the gene expression profile of γδ HSTCS as well as its tumor microenvironment (TME).
Results:We characterized the unique gene expressing signatures of malignant γδ T cells with a set of marker genes were newly identified in HSTCL (AREG, PLEKHA5, VCAM1 etc.). Although the malignant γδ T cells were expanded from a single TCR clonotype, they evolved into two transcriptionally distinct tumor subtypes during the disease progression. The Tumor_1 subtype was dominant in pre-treatment samples with highly aggressive phenotypes. While the Tumor_2 had relative mild cancer hallmark signatures but expressed genes associated with tumor survival signal and drug resistance (IL32, TOX2, AIF1, AKAP12, CD38 etc.), and eventually became the main tumor subtype post-treatment. We further dissected the tumor microenvironment and discovered the dynamically rewiring cell-cell interaction networks during the treatment. The tumor cells had reduced communications with the microenvironment post-treatment.
Conclusions:Our study reveals heterogenous and dynamic tumor and microenvironment underlying pathogenesis of HSTCL and may contribute to identify novel targets for diagnosis and treatment of HSTCL in the future.
Keywords:Hepatosplenic T-cell lymphoma; Single cell RNA-seq; Single cell TCR-seq; γδ T cells.
Single cell profiling of γδ hepatosplenic T-cell lymphoma unravels tumor cell heterogeneity associated with disease progression
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
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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
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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