Landscape of infiltrating B cells and their clinical significance in human hepatocellular carcinoma

Abstract

As a major cellular component in tumor microenvironment, the distribution, frequency, and prognostic significance of infiltrating B cell subsets in hepatocellular carcinoma (HCC) remain controversial. Using tyramide signal amplification (TSA) based fluorescent multiplexed immunohistochemistry in situ, we evaluated the distribution and frequency of B cell subsets in two independent HCC cohorts (n = 619). The results were further confirmed by flow cytometry. Correlations of B cell subsets with clinicopathologic features and patient prognosis were analyzed. Five B cell subsets were defined by multiplexed immunohistochemistry and each subset was clearly separated by t-SNE dimension reduction analysis. Notably, the densities of all B cell subsets were significantly decreased in the tumor. The frequency of plasma cells within B cells was most abundant in the tumor. In training cohort (n = 258), high densities of tumor-infiltrating CD20⁺ B cells, naive B cells, IgM⁺ memory B cells, CD27^- isotype-switched memory B cells, and plasma cells were associated with superior survival. Multivariate analysis further identified CD20⁺ B cells, naive B cells, and CD27^- isotype-switched memory B cells as independent prognosticators for survival. Unsupervised cluster analysis confirmed increased B cell subsets harbored superior survival. In addition, high density of B cells was correlated with smaller tumor size and well differentiation. The results were validated in the independent cohort of 361 HCC patients. Intratumor infiltration of B cells is significantly impaired during HCC progression. High densities of tumor-infiltrating B cells imply a better clinical outcome. Therapies designed to target B cells may be a novel strategy in HCC.

Keywords: B cell subsets; Liver cancer; prognosis; tumor microenvironment (TME); tyramide signal amplification (TSA).

Figure 1.

B cell subsets are defined by six-color multiplexed immunohistochemistry in HCC. (a) Digital scanning displayed bright-field image and multispectral image (MSI) of one TMA core from HCC tissues. (b) B cell subsets and corresponding identification markers applied in this study. (c) The multiplexed images displayed co-localization of different markers. Scale bar: 200 μm. (d) The representative images of six-marker multiplex and phenotype classification. Scale bar: 50 μm.

Figure 2.

B cell subset distributions are compared between tumor and non-tumor liver tissues of HCC. (a and b) The acquired single-cell fluorescent pixel intensity data were visualized and analyzed by FCS Express 6 Plus v6.04.0034 (De Novo Software). Five distinct B cell subsets were gated, respectively, and represented as image plots of tumor (a) and non-tumor liver tissues (b). (c) The t-SNE analysis of B cells from tumor tissues and non-tumor liver tissues displayed the distinct classification of five distinct B cell subsets. (d) Comparisons of the B cell subset densities between tumor and non-tumor liver tissues in two independent cohorts. Statistical differences were determined by two-tailed student’s t test. NS: not significant, *P < 0.05, ***P < 0.001.

Figure 3.

Flow cytometry confirms the classification of B cell subsets in HCC. (a) The gating strategy of five distinct B cell subsets in HCC tumor tissues by flow cytometry. (b) Comparisons of the frequencies of five B cell subsets among tumor tissues, non-tumor liver tissues and PBMCs (n = 10). NS: not significant, *P < 0.05, **P < 0.01. (c) The proportions of different subsets among B cell compartment (CD20⁺ B cells plus PCs) were analyzed in tumor tissues.

Figure 4.

Tumor-infiltrating B cells significantly predict patient survival. (a and b) Kaplan-Meier analysis of OS for tumor-infiltrating B cell subsets based on cell density in the training and validation cohorts.

Figure 5.

Cluster analysis based on B cell density enables patient classification in HCC. (a and b) Heat maps described the result of unsupervised cluster analysis according to densities of B cell subsets in tumor tissues from two independent cohorts. (c) Kaplan-Meier analysis of OS stratified by clustering analysis in the training and validation cohorts.