Evaluating Tumor Immune Microenvironment in Bladder Cancer

Immunological characteristics of the TME in BLCA include the expression of immunomodulators, activity of the cancer immunity cycle, infiltration level of TIICs, and the expression of inhibitory immune checkpoints. We first collected information on 122 immunomodulators including MHC, receptors, chemokines, and immune stimulators from the study of Charoentong et al. (Table S2) 28 (link). The cancer immunity cycle reflects the anticancer immune response and comprises seven steps: release of cancer cell antigens (Step 1), cancer antigen presentation (Step 2), priming and activation (Step 3), trafficking of immune cells to tumors (Step 4), infiltration of immune cells into tumors (Step 5), recognition of cancer cells by T cells (Step 6), and killing of cancer cells (Step 7) (Table S3) 29 (link). The activities of these steps determine the fate of the tumor cells. Xu et al. evaluated the activities of these steps using a single sample gene set enrichment analysis (ssGSEA) based on the gene expression of individual samples 30 (link). Thereafter, several algorithms were developed to calculate the infiltration level of TIICs in TME using bulk RNA-seq data. Different algorithms and marker gene sets of TIICs initiate calculation errors. To avoid these errors, we comprehensively calculated the infiltration level of TIICs using seven independent algorithms: Cibersort-ABS, MCP-counter, quanTIseq, TIMER, xCell, TIP, and TISIDB (Table S4) 30 (link)-36 (link). We also identified the effector genes of TIICs from previous studies (Table S5). Finally, we collected 22 inhibitory immune checkpoints with therapeutic potential from Auslander's study (Table S6) 37 (link).
Ayers et al. developed and validated a pan-cancer T cell-inflamed score, which could define pre-existing cancer immunity, as well as predict the clinical response of ICB 38 (link). The eighteen genes included in the T cell-inflamed score algorithm and their coefficients are shown in Table S7. Here, we computed the T cell inflamed score as a weighted linear combination of the scores from the 18 genes. Hyperprogression is an adverse event associated with ICB. We summarized several predictors of hyperprogression (Table S8) 39 -41 . The amplification and high mRNA expression of MDM2, MDM4, DNMT3A, CCND1, FGF19, FGF4, and FGF3 are positively correlated with hyperprogression. In addition, the deletion and low mRNA expression of CDKN2A and CDKN2B are also positively correlated with hyperprogression.
To confirm the role of Siglec15 in modulating cancer immunity in BLCA, we analyzed the correlation between Siglec15 and the immunological characteristics of TME with respect to the above aspects. The findings from this study were validated in three independent external cohorts, including GSE31684, GSE32894, and IMvigor210.