Effector and Regulatory T Cell Subsets in Follicular Lymphoma Tumors: Implications for Pathogenesis and Prognosis.

Abstract Abstract 2718 Gene expression profiling of follicular lymphoma (FL) tumors showed that genes attributable to infiltrating T cells were associated with improved survival (Dave et al., NEJM 2004; 351: 2109). However, the precise nature of the protective T cell subsets is unknown. Recent studies suggest that master transcription factors (TFs) such as T-bet, GATA-3, RORgt, and Bcl6 regulate the differentiation of effector T cells (Teffs) into TH1/TC1, TH2/TC2, TH17/TC17, and follicular helper T cell (TFH) subsets, respectively. These dominant TFs along with other TFs imprint specific cytokine and chemokine receptor expression in both CD4+ and CD8+ T-cell subsets. Based on these studies, identification of TH1/TC1, TH2/TC2, TH17/TC17, and TFH subsets using chemokine receptor expression pattern has been described in normal donors (Duhen et al., Blood 2012; 119: 4430). To determine whether this chemokine receptor expression pattern could be used for identification of Teff subsets in FL tumors, we FACSorted T cells from single cell suspensions of FL tumors and performed real-time PCR for master TFs and subset-specific cytokines. We found that T cell subsets in FL tumors have similar chemokine receptor expression pattern as peripheral blood T cells from normal donors: TH1/TC1 are CXCR3+, TH17/TC17 are CXCR3-CCR6+CCR4+, and TFH are CXCR5hiPD-1hi. A similar profile was also observed in tonsils, widely accepted as suitable controls for FL. Recent studies also suggest that the TFs essential for Teff differentiation are also involved in polarization of Foxp3+ regulatory T cells (Tregs) into specialized subsets that regulate the corresponding Teff subsets. Moreover, these Treg subsets express identical chemokine receptors as their target Teffs. Using Foxp3 and chemokine receptor expression pattern we found two major subsets of Tregs in FL and tonsils: Foxp3+CXCR3+ Tregs that regulate TH1/TC1 and Foxp3+CXCR5hiPD-1hi follicular regulatory T cells (TFR) that regulate TFH. Next, using 10-color, 12-parameter flow cytometry we analyzed single cell suspensions of FL tumors (n=41) and normal tonsils (n=11) and determined the percentages of various Teff and Treg subsets, B cells, NK cells, and macrophages of total live cells. We found that NK cells and macrophages were not significantly different between the two but the percentage of B cells was significantly lower in FL vs tonsils (p<0.01). In contrast, CD3+ and CD4+ T cells were significantly increased in FL vs. tonsils (p<0.01) but CD8+ T cells were not (p=0.4). However, activated T cells (CD45RO+) were significantly increased in both CD4+ (p<0.001) and CD8+ (p=0.016) T cells in FL tumors vs. tonsils with the increase predominantly from central memory T cells rather than effector memory T cells. Among the Teffs (identified by excluding Foxp3+ T cells), TH1 (p<0.000001), TH17 (p<0.01), TC1 (p<0.01), and TC17 (p<0.001) were significantly increased in FL tumors vs tonsils but TFH were not (p=0.8). Among the Foxp3+ Tregs, total Tregs (p<0.0001), CXCR3+ Tregs (p<0.0000001), and TFR (p<0.0001) were significantly increased in FL tumors vs tonsils. Comparison of the ratios of TH1:CXCR3+ Tregs, TC1:CXCR3+ Tregs, and TFH:TFR all showed that the ratios were significantly higher in tonsils vs FL tumors (p<0.001). Taken together, these results suggest that despite the increase in CD4+ and CD8+ Teff subsets in FL, the increase in the corresponding Treg subsets may offset their effects and facilitate immune evasion by the tumor. Based on the known functions, TH1/TC1 and TH17/TC17 are likely to have antitumor effects and TFH may have protumor effects in FL. Among Tregs, CXCR3+ Tregs may have protumor effects by inhibiting TH1/TC1 and TFR may have antitumor effects by inhibiting B cells and TFH. Therefore, the prognostic impact of tumor infiltrating T cells in FL may depend on the relative dominance of these various Teff and Treg subsets. The phenotypic profile described above provides a simple method to enumerate these subsets routinely in clinical flow cytometry laboratories and help us better understand their effect on the pathogenesis and prognosis in FL. Disclosures: No relevant conflicts of interest to declare.

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Altered circulating CCR6+and CXCR3+ T cell subsets are associated with poor renal prognosis in MPO-ANCA-associated vasculitis

Arthritis Research & Therapy ◽

10.1186/s13075-021-02576-x ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Zhonghua Liao ◽

Jiale Tang ◽

Liying Luo ◽

Shuanglinzi Deng ◽

Lisa Luo ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Chemokine Receptor ◽

Memory T Cells ◽

Receptor Expression ◽

T Cell Subsets ◽

Effector Memory ◽

Chemokine Receptor Expression ◽

Effector Memory T Cells ◽

Renal Prognosis

Abstract Background Effector memory T cells are pivotal effectors of adaptive immunity with enhanced migration characteristics and are involved in the pathogenesis of ANCA-associated vasculitis (AAV). The diversity of effector memory T cells in chemokine receptor expression has been well studied in proteinase 3 (PR3)-AAV. However, few studies have been conducted in myeloperoxidase (MPO)-AAV. Here, we characterized chemokine receptor expression on effector memory T cells from patients with active MPO-AAV. Methods Clinical data from newly diagnosed MPO-AAV patients and healthy subjects were collected and analyzed. Human peripheral blood mononuclear cells (PBMCs) isolated from patients with active MPO-AAV were analyzed by flow cytometry. The production of effector memory T cell-related chemokines in serum was assessed by ELISA. Results We observed decreased percentages of CD4+ and CD8+ T cells in the peripheral blood, accompanied by a significant decrease in CCR6-expressing T cells but an increase in CXCR3+ T cells, in active MPO-AAV. Furthermore, the decrease in CCR6 and increase in CXCR3 expression were mainly limited to effector memory T cells. Consistent with this finding, the serum level of CCL20 was increased. In addition, a decreasing trend in the TEM17 cell frequency, with concomitant increases in the frequencies of CD4+ TEM1 and CD4+ TEM17.1 cells, was observed when T cell functional subsets were defined by chemokine receptor expression. Moreover, the proportions of peripheral CD8+ T cells and CD4+ TEM subsets were correlated with renal prognosis and inflammatory markers. Conclusions Our data indicate that dysregulated chemokine receptor expression on CD4+ and CD8+ effector memory T cells and aberrant distribution of functional CD4+ T cell subsets in patients with active MPO-AAV have critical roles related to kidney survival.

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SLAMF7 and IL-6R define distinct cytotoxic versus helper memory CD8+ T cells

Nature Communications ◽

10.1038/s41467-020-19002-6 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Lucie Loyal ◽

Sarah Warth ◽

Karsten Jürchott ◽

Felix Mölder ◽

Christos Nikolaou ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Chemokine Receptor ◽

Memory T Cells ◽

Receptor Expression ◽

T Cell Subsets ◽

Target Cells ◽

Memory Cd8 T Cells ◽

Chemokine Receptor Expression ◽

Conventional View

AbstractThe prevailing ‘division of labor’ concept in cellular immunity is that CD8+ T cells primarily utilize cytotoxic functions to kill target cells, while CD4+ T cells exert helper/inducer functions. Multiple subsets of CD4+ memory T cells have been characterized by distinct chemokine receptor expression. Here, we demonstrate that analogous CD8+ memory T-cell subsets exist, characterized by identical chemokine receptor expression signatures and controlled by similar generic programs. Among them, Tc2, Tc17 and Tc22 cells, in contrast to Tc1 and Tc17 + 1 cells, express IL-6R but not SLAMF7, completely lack cytotoxicity and instead display helper functions including CD40L expression. CD8+ helper T cells exhibit a unique TCR repertoire, express genes related to skin resident memory T cells (TRM) and are altered in the inflammatory skin disease psoriasis. Our findings reveal that the conventional view of CD4+ and CD8+ T cell capabilities and functions in human health and disease needs to be revised.

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Flexible Programs of Chemokine Receptor Expression on Human Polarized T Helper 1 and 2 Lymphocytes

Journal of Experimental Medicine ◽

10.1084/jem.187.6.875 ◽

1998 ◽

Vol 187 (6) ◽

pp. 875-883 ◽

Cited By ~ 1122

Author(s):

Federica Sallusto ◽

Danielle Lenig ◽

Charles R. Mackay ◽

Antonio Lanzavecchia

Keyword(s):

T Cells ◽

T Cell ◽

Chemokine Receptor ◽

Receptor Expression ◽

T Cell Subsets ◽

T Helper ◽

Rnase Protection ◽

Chemokine Receptor Expression ◽

Ifn Γ ◽

Th1 And Th2

Chemokines and their receptors are important elements for the selective attraction of various subsets of leukocytes. To better understand the selective migration of functional subsets of T cells, chemokine receptor expression was analyzed using monoclonal antibodies, RNase protection assays, and the response to distinct chemokines. Naive T cells expressed only CXC chemokine receptor (CXCR)4, whereas the majority of memory/activated T cells expressed CXCR3, and a small proportion expressed CC chemokine receptor (CCR)3 and CCR5. When polarized T cell lines were analyzed, CXCR3 was found to be expressed at high levels on T helper cell (Th)0s and Th1s and at low levels on Th2s. In contrast, CCR3 and CCR4 were found on Th2s. This was confirmed by functional responses: only Th2s responded with an increase in [Ca2+]i to the CCR3 and CCR4 agonists eotaxin and thymus and activation regulated chemokine (TARC), whereas only Th0s and Th1s responded to low concentrations of the CXCR3 agonists IFN-γ–inducible protein 10 (IP-10) and monokine induced by IFN-γ (Mig). Although CCR5 was expressed on both Th1 and Th2 lines, it was absent in several Th2 clones and its expression was markedly influenced by interleukin 2. Chemokine receptor expression and association with Th1 and Th2 phenotypes was affected by other cytokines present during polarization. Transforming growth factor β inhibited CCR3, but enhanced CCR4 and CCR7 expression, whereas interferon α inhibited CCR3 but upregulated CXCR3 and CCR1. These results demonstrate that chemokine receptors are markers of naive and polarized T cell subsets and suggest that flexible programs of chemokine receptor gene expression may control tissue-specific migration of effector T cells.

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High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

Blood ◽

10.1182/blood-2020-134941 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 17-18

Author(s):

Jose C Villasboas ◽

Patrizia Mondello ◽

Angelo Fama ◽

Melissa C. Larson ◽

Andrew L. Feldman ◽

...

Keyword(s):

T Cells ◽

Spatial Distribution ◽

T Cell ◽

B Cells ◽

Research Funding ◽

Memory T Cells ◽

Cell Types ◽

T Cell Subsets ◽

Cell Contact ◽

Private Company

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

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