Mass cytometry defines distinct immune profile in germinal center B-cell lymphomas

Abstract

Tumor-associated macrophage and T-cell subsets are implicated in the pathogenesis of diffuse large B-cell lymphoma, follicular lymphoma, and classical Hodgkin lymphoma. Macrophages provide essential mechanisms of tumor immune evasion through checkpoint ligand expression and secretion of suppressive cytokines. However, normal and tumor-associated macrophage phenotypes are less well characterized than those of tumor-infiltrating T-cell subsets, and it would be especially valuable to know whether the polarization state of macrophages differs across lymphoma tumor microenvironments. Here, an established mass cytometry panel designed to characterize myeloid-derived suppressor cells and known macrophage maturation and polarization states was applied to characterize B-lymphoma tumors and non-malignant human tissue. High-dimensional single-cell analyses were performed using dimensionality reduction and clustering tools. Phenotypically distinct intra-tumor macrophage subsets were identified based on abnormal marker expression profiles that were associated with lymphoma tumor types. While it had been proposed that measurement of CD163 and CD68 might be sufficient to reveal macrophage subsets in tumors, results here indicated that S100A9, CCR2, CD36, Slan, and CD32 should also be measured to effectively characterize lymphoma-specific tumor macrophages. Additionally, the presence of phenotypically distinct, abnormal macrophage populations was closely linked to the phenotype of intra-tumor T-cell populations, including PD-1 expressing T cells. These results further support the close links between macrophage polarization and T-cell functional state, as well as the rationale for targeting tumor-associated macrophages in cancer immunotherapies.

Keywords: Germinal center; Lymphoma; Mass cytometry; Tumor-associated macrophages.

Fig. 1

Myeloid (My), T, and NK cells are detected by mass cytometry within tumor and reactive tissues. a A viSNE is shown for one representative DLBCL tissue. Density plot, CD3, CD8, CD45RA, CD16, CD19, CD14, CD123, CD11c, and HLA-DR are shown. b Cell subsets frequencies among all viable cells are shown for DLBCL (n = 7, red), FL (n = 2, green), HL (n = 7, blue), and HD (n = 6, black)

Fig. 2

The lymphoma myeloid compartment was heterogeneous and related to the cancer subtype. a Myeloid cells from lymphomas and reactive tissues were analyzed by hierarchical clustering. Each row corresponds to a SPADE node. Relative normalized transformed mean intensity is presented. Module separation was based on the unsupervised clustering (Figure S3c). b Distribution of macrophages (Mac) and dendritic cells (cDC and pDC) among lymphomas (DLBCL, FL, and HL) and reactive tissue (HD). Mac denotes the sum of all macrophage subsets (Mac 1–6). c Distribution of myeloid modules among lymphomas (DLBCL, FL, and HL) and HD. *p < 0.05

Fig. 3

Myeloid subsets exhibit specific inflammatory and/or suppressive phenotypes in the lymphoma TME. a Marker expression for Mac modules in DLBCL and HL. b mIHC staining for DLBCL and HL. Bottom right of each panel: Dot plot showing the coexpression of markers for Mac modules defined in Fig. 2. c Top: counts of cell from the mIHC staining for DLBCL (n = 3) and HL (n = 3) and bottom: percentage of coexpression. In each panel, expression of markers defined by mass cytometry for various Mac subsets. d Heat map after hierarchical clustering for Mac clusters involved in lymphoma (in red) and polarized macrophages (in black). Polarized macrophage signatures were obtained previously under various stimuli (M_IL4, M_IL10, and M_TPP) [22]

Fig. 4

T-lymphocyte subsets are specific to lymphoma subtypes. a Cell subset identification by SPADE analysis. A representative SPADE is shown. T-cell subsets were defined as follows: N (naïve, CD45RA^posCCR7^pos), CM (central memory, CD45RA^negCCR7^pos), EM (effector memory, CD45RA^negCCR7^neg), EMRA (effector memory expressing CD45RA, CD45RA^posCCR7^neg), and Tregs (CD3^posCD8^negCD25^posCD 127^low). b Cell subset frequencies among CD4 or CD8 cells are shown for DLBCL (n = 7, red), FL (n = 2, green), HL (n = 7, blue), and HD (n = 6, black). c Percentage of T-cell subsets expressing PD-1 among CD4 (EM, CM, and Treg) or CD8 (EM and CM) subsets are shown for DLBCL (n = 7, red), FL (n = 2, green), HL (n = 7, blue), and HD (n = 6, black). PD-1 expression on CM and EM is shown for a representative sample. *p < 0.05, **p < 0.01

Fig. 5

Immune cell subsets correlate with each other across lymphoma subtypes. Heatmaps showing Spearman correlation of immune cell population frequencies for DLBCL, HL, or HD. Each identified immune cell subset was correlated with each other defined immune subset infiltrating either DLBCL (top) or HL (bottom). High significant positive correlations are shown in red, while highly significant negative correlations are represented in blue