Immune classification of clear cell renal cell carcinoma

Abstract

Since the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of macrophages and CD8+ T-cells and and a slightly smaller number of CD4+ T cells than CD8+ T cells, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value [Formula: see text]). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value [Formula: see text]), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value [Formula: see text]). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value [Formula: see text]) compared to the patients with tumors for all groups of tumors except group 3.

Figure 1

Immune pattern of ccRCC. Sub-figure (A) (created using PowerPoint v16.44) represents the algorithm of digital cytometry and clustering applied on TCGA data. Sub-figures (B) and (C), respectively show the estimated percentage of each immune cell by mass cytometry analysis of 73 ccRCC patients done by Chevrier et al. (B) and digital cytometry on 526 TCGA ccRCC tumors (C). Sub-figures (D) and (E) indicate the correlation map of estimated immune cell frequencies in 73 ccRCC tumors (D) and TCGA ccRCC tumors (E), respectively. Sub-figures (F) and (G) show the cluster heat map of immune cell frequencies in 73 ccRCC tumors (F) and TCGA ccRCC tumors (G). Sub-figures (H) and (I) respectively show a box plot format of the immune cell percentages in 73 ccRCC tumors (H) and TCGA ccRCC tumors (I). Sub-figure (J) shows 4 distinct immune patterns of ccRCC tumors obtained by K-mean clustering of cell frequencies of TCGA ccRCC tumors. Sub-figures (B)–(I) have been created using TumorDecon (https://pypi.org/project/TumorDecon/).

Figure 2

Clinical features of each ccRCC tumor cluster. Sub-figures (A)–(F) show the percentage of patients with grade 1–4 (A), stage T1–T4 (B), with tumors or without tumors (C), alive or dead at the last time of follow up (D), female or male (E), and primary tumors in left or right kidney (F) for each cluster of ccRCC tumors. Sub-figures (G)–(I) and (J)–(L) respectively show the overall survival months and age of diagnosis of the patients in each cluster as a function of tumor status (G,J), gender (H,K), and the location of the primary tumor (I,L); the size of markers indicates the grade of tumors.

Figure 3

Percentage of mast cells, monocytes and CD8+ T-cells in ccRCC tumors as a function of grade and TNM staging. Sub-figures (A)–(C) show the percentages of mast cells (A), monocytes (B), and CD8+ T-cells (C) in primary tumors as a function of stage of tumors. Sub-figures (D)–(F) represent the percentage of mast cells (D), monocytes (E), and CD8+ T-cells (F) in primary tumors as functions of the grade of tumors.

Figure 4

Frequency of NK cells and mast cells in ccRCC. Sub-figure (A) shows that patients who were tumor free at the last time of follow up have higher percentage of NK cells than patients with tumor at the last time. Sub-figures (B) and (C) respectively indicate the percentage of NK cells and mast cells in primary tumors in each cluster.

Figure 5

Expression levels of genes encoding PD-1, PD-L1, PD-L2, and IFN$\gamma$. Sub-figures (A)–(D) indicate the expression levels of INFG, PDCD1LG2, PDCD1 and CD274 in each cluster as a function of tumor status, respectively. Sub-figure (E) represents the correlations and distributions of INFG, PDCD1LG2, PDCD1, CD274 expression levels and CD8+ T-cells; color coded based on the clusters.

Figure 6

RGS5 expression level in ccRCC tumors. Sub-figure (A) shows the expression level of RGS5 in ccRCC tumors in each cluster as a function of tumor status. Sub-figures (B), (C) and (D) indicate the relation between the level of RGS5 and the percentages of NK cells, monocytes, and mast cells in ccRCC tumors, respectively.