Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk.

Abstract

Simple SummaryIn prostate cancer (PCa), the tumor microenvironment plays a crucial role in both the development and progression of the disease. We used Digital Spatial Profiling multiplex technology to assess the expression of 58 protein and 1825 RNA transcripts in traditionally challenging samples (i.e., archived FFPE samples) while maintaining the spatial context of expression. First, we identified differences in protein and RNA expression among multiple cell types (stromal, epithelial, and immune cells). Second, we used a PCa tissue microarray of 1547 cores (97 patients) to further validate the protein expression of selected genes in a larger cohort of prostate cancer patients. We also computed survival models testing the relationship between OX40L, CTLA4, and CD11c protein expression in both tumor and tumor-adjacent stroma samples with time to biochemical relapse.The tumor microenvironment plays a crucial role in both the development and progression of prostate cancer. Furthermore, identifying protein and gene expression differences between different regions is valuable for treatment development. We applied Digital Spatial Profiling multiplex analysis to formalin-fixed paraffin embedded prostatectomy tissue blocks to investigate protein and transcriptome differences between tumor, tumor-adjacent stroma (TAS), CD45+ tumor, and CD45+ TAS tissue. Differential expression of an immunology/oncology protein panel (n = 58) was measured. OX40L and CTLA4 were expressed at higher levels while 22 other proteins, including CD11c, were expressed at lower levels (FDR < 0.2 and p-value < 0.05) in TAS as compared to tumor epithelia. A tissue microarray analysis of 97 patients with 1547 cores found positive correlations between high expression of CD11c and increased time to recurrence in tumor and TAS, and inverse relationships for CTLA4 and OX40L, where higher expression in tumor correlated with lower time to recurrence, but higher time to recurrence in TAS. Spatial transcriptomic analysis using a Cancer Transcriptome Atlas panel (n = 1825 genes) identified 162 genes downregulated and 69 upregulated in TAS versus tumor, 26 downregulated and 6 upregulated in CD45+ TAS versus CD45+ tumor. We utilized CIBERSORTx to estimate the relative immune cell fractions using CD45+ gene expression and found higher average fractions for memory B, naïve B, and T cells in TAS. In summary, the combination of protein expression differences, immune cell fractions, and correlations of protein expression with time to recurrence suggest that closely examining the tumor microenvironment provides valuable data that can improve prognostication and treatment techniques.