Multi-omics Perspective on the Tumor Microenvironment based on PD-L1 and CD8 T-Cell Infiltration in Urothelial Cancer

Abstract

Objectives: We carried out an integrated analysis based on multiple-dimensional types of data from cohorts of bladder cancer patients to identify multi-omics perspective (genomics and transcriptomics) on the tumor microenvironment on the bases of the programmed cell death 1 ligand (PD-L1) and CD8 T-cell infiltration in urothelial carcinoma. Methods: Multiple-dimensional types of data, including clinical, genomic and transcriptomic data of 408 bladder cancer patients were retrieved from the Cancer Genome Atlas database. Based on the median values of PD-L1 and CD8A, the tumor samples were grouped into four tumor microenvironment immune types (TMIT). The RNA sequencing profiles, somatic mutation and PD-L1 amplification data of bladder cancer were analyzed by different TMITs. Results: Our research demonstrated that 36.8% of the evaluated bladder cancer belonged to TMIT I (high PD-L1/high CD8A). TIMT subtypes were not significantly associated with overall survival or disease free survival in urothelial cancer. TMIT I facilitates CD8+ T-cell infiltration and activates T-effector and interferon gamma (IFN-γ) associated gene signature. The number of somatic mutations, cytolytic activity, IFN-γ mRNA expression and TIGIT mRNA expression in TMIT I was remarkably higher than those in other TMIT groups. Our results showed a high rate of C>T transversion and a high rate of transition/transversion (Ti/Tv) in TMIT I bladder tumors. The RB1 mutation was significantly associated with TMIT I bladder cancer and be significantly co-occurring with the TP53 mutation. However, FGFR3 mutation and TP53 mutation were mutually exclusive in TMIT II bladder tumors. More importantly, different amino acid changes by FGFR3/RB1 mutations were also found between TMIT I and TMIT II bladder cancer, such as amino acid changes in "Immunoglobulin I-set domain (260-356)"and "Protein tyrosine kinase (472-748)". We also detected 9 genes as significantly cancer-associated genes in TMIT I bladder cancer, of which, RAD51C has been reported to play an important role in DNA damage responses. Further analysis concentrated on the potential molecular mechanism found that TMIT I was significantly associated with anti-tumor immune-related signaling pathway, and kataegis was present on chromosome 21 in TMIT I bladder tumors. Conclusions: The classification of bladder cancer into four TMITs on the bases of the PD-L1 expression and the CD8+ CTLs statuses is an appropriate approach for bladder tumor immunotherapy. TMIT I (high PD-L1/high CD8A) is significantly correlated with more somatic mutation burden, and facilitates CD8+ T-cell infiltration and activates T-effector and IFN-γ associated gene signature. Alteration landscape for somatic variants was different between TMIT I and TMIT II (low PD-L1/low CD8A).

Keywords: TIGIT; Bladder cancer; Immunotherapy; PD-L1; TMIT; kataegis.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Fig 1

Landscape of TMIT, mutational signatures and mutations. (a) Scatter plot for the distribution of TMITs. (b) Comparation of somatic mutation burden between different TMITs. (c) Comparation of the proportion of TMIT I according to somatic mutation burden. (d) Boxplot for classification of somatic variants in 150 TMIT I bladder tumors. (e) Alteration landscape for 150 TMIT I bladder tumors and 150 TMIT II bladder tumors. TMIT, tumor microenvironment immune type; Ti, transition; Tv, transversion; ***, P < 0.001; **, P < 0.01; *, P < 0.05.

Fig 2

FGFR3 mutation and RB1 mutation in different TMITs of bladder tumors. (a) Scatter plot for the distribution of FGFR3 mutation between different TMITs. (b) Labelling points for the comparation of amino acid changes by FGFR3 mutation in TMIT I and TMIT II bladder tumors. (c) Scatter plot for the distribution of RB1 mutation between different TMITs. (d) Labelling points for the comparation of amino acid changes by RB1 mutation in TMIT I and TMIT II bladder tumors. TMIT, tumor microenvironment immune type; wt, wide type; mut, mutation; Ig, Immunoglobulin domain; I-set, Immunoglobulin I-set domain; Pkinase_Tyr, Protein tyrosine kinase.

Fig 3

Comparation of somatic interactions, cancer driver genes and genomic loci with localized hyper-mutations between TMIT I and TMIT II bladder tumors. (a) Heatmap of mutually exclusive or co-occurring set of genes in the mutation pattern of TMIT I and TMIT II bladder tumors. (b) Detecting cancer driver genes based on positional clustering in TMIT I and TMIT II bladder tumors. Each dot represents a gene and size of the dot represents number clusters (mentioned inside square brackets) within which, a fraction (X-axis) of total variants are accumulated. (c) Rainfall plots for the genomic loci with localized hyper-mutations by inter variant distance on a linear genomic scale. Each dot represented a single nucleotide variants (SNV) and were color coded according to six substitution classes. Arrowheads indicated clusters of hyper mutated genomic regions called as “kataegis”. TMIT, tumor microenvironment immune type.

Fig 4

TMIT I was significantly correlated with immune-associated gene signature. (a) Scatter plot for the distribution of IFN-γ mRNA expression between different TMITs. (b) Comparation of IFN-γ mRNA expression between different TMITs. (c) Comparation of the proportion of TMIT I according to IFN-γ mRNA expression. (d) Significant correlation between IFN-γ mRNA expression and CD8A mRNA expression. (e) Comparation of the proportion of TMIT I according to PD-L1 amplification. (f) Scatter plot for the distribution of PD-L1 amplification between different TMITs. (g) Comparation of cytolytic activity between different TMITs. (h) Comparation of the proportion of TMIT I according to cytolytic activity. (i) Scatter plot for the distribution of cytolytic activity between different TMITs. (j) Comparation of the proportion of TMIT I according to cytolytic activity. (k) Scatter plot for the distribution of TIGIT mRNA expression between different TMITs. (l) Comparation of TIGIT mRNA expression between different TMITs. Cytolytic activity is calculated by the geometric mean of granzyme and perforin1. TMIT, tumor microenvironment immune type; ***, P < 0.001; **, P < 0.01; *, P < 0.05.

Fig 5

GSEA revealed that TMIT I was significantly associated with some immune-related signaling pathway. The enrichment plot was used for providing a graphical view of the enrichment score for a gene set. The heatmap (Blue-Pink O' Gram) showed the expression of clustered-genes in the leading edge subsets, where the range of colors (red, pink, light blue, dark blue) showed the range of expression values (high, moderate, low, lowest). GSEA, gene set enrichment analysis; TMIT, tumor microenvironment immune type; NSE, normalized enrichment score; FDR, false discovery rate.