Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Abstract

T-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+TCF7-but not PD1+TCF7+cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

High-dimensional single-cell definition of CLL T cells identifies Galectin-9 as novel immunotherapy target

“…More specifically, both CD4 + and CD8 + naïve (CD4 TN and CD8 T N ) T cells were defined based on the expression of marker genes such as SELL, CCR7 and IL7R; regulatory CD4 + T cells (T REG ) were identified by high expression of FOXP3, IL2RA and IKZF2; a heterogeneous cluster of cells containing mainly follicular helper CD4 + T cells (T FH ) was defined by the expression of CXCR5, CD200, TOX2 and TOX (Suppl. Figure 5C, D), the small proportion of CD8 + T cells in this cluster is in line with a shared transcriptional program of exhausted CD8 + T cells with T FH (Im et al, 2016); and conventional CD4 + T cells with a central memory phenotype were identified by the high expression of IL7R, CD40LG, KLRB1 as well as CD69 (CD4 T H CD69) or ITGB1 and KLF2 (CD4 T H KLF2) in accordance with published data of other B cell lymphoma (Roider et al, 2022). A heterogeneous cluster mostly comprising CD4 + T cells expressed memory marker and effector molecule genes such as GZMK , resembling the CD4 T EM GZMK subset that shares features with T R 1-like cells, identified by mass cytometry (Figure 1B).…”

“…Figure 5C, D), the small proportion of CD8 + T cells in this cluster is in line with a shared transcriptional program of exhausted CD8 + T cells with T FH (Im et al, 2016). Conventional CD4 + T cells with a central memory phenotype were identified by the high expression of IL7R, CD40LG, KLRB1 as well as CD69 (CD4 T H CD69) or ITGB1 and KLF2 (CD4 T H KLF2) in accordance with published data of other B cell lymphoma (Roider et al, 2022). A heterogeneous cluster mainly comprising CD4 + T cells expressed memory marker and effector molecule genes such as GZMK , resembling the CD4 T EM GZMK subset that shares features with T R 1-like cells, identified by mass cytometry (Figure 1B).…”

A single-cell multi-omic and spatial atlas of B-cell lymphomas reveals differentiation drives intratumor heterogeneity

“…Varying patterns of T-cell infiltration have been observed across B-cell lymphomas 41 . We observe that immune cell infiltration patterns also vary between intratumor maturation states, including enrichment of cytotoxic T-cells among plasma tumor cells.…”

“…We characterized the spatial distribution of cell types with CODEX 21 (52 features, Supplementary Table 2) for 19 samples (29 slides) in the CITE-Seq cohort. Microenvironmental cell types were defined according to previously published nodal cell type marker profiles 41 . Using logistic regression, we transferred B-cell maturation state labels in the CITE-Seq dataset samplewise to B-cells in the CODEX dataset via the shared protein features (n=28) between both datasets (Supplementary Table 2).…”

Tunable PhenoCycler Imaging of the Murine Pre-Clinical Tumour Microenvironments

“…Reporters are complementary oligonucleotides to the unique barcodes, and are tagged with either fluorophores ATTO550 AF647, or AF750. As of this writing, PhenoCycler has been used to image up to 101 different markers in single tissue (15, 16), and has been used to spatially profile human cancers such as cutaneous T cell lymphoma (17), follicular lymphoma (18), diffuse large B cell lymphoma (19), Hodgkin’s lymphoma (20), bladder cancer (21), colorectal cancer (22), basal cell carcinoma (23), glioblastoma (24), breast cancer (25), and head and neck squamous cell carcinoma (26), and human non-cancerous conditions such as ulcerative colitis (27), diabetic nephropathy (28), functional dyspepsia (29), vitiligo (30), and Alzheimer’s disease (31).…”