Immune profiling of premalignant lesions in patients with Peutz-Jeghers syndrome

Abstract

Background: Peutz-Jeghers syndrome (PJS), is a rare autosomal dominant hereditary disease characterized by an elevated risk of various cancers. Serine/Threonine Kinase 11 (STK11) gene is a major tumor suppressor crucial for immune evasion with and beyond tumorigenic cells. It has garnered increasing attention in the realm of oncology treatment, particularly in the context of immunotherapy development.

Objective: This study aimed to assess the suitability of polyps obtained from individuals with PJS, resulting from germline STK11 deficiency, for immunotherapy. Additionally, we seek to identify potential shared mechanisms related to immune evasion between PJS polyps and cancers. To achieve this, we examined PJS polyps alongside familial adenomatous polyposis (FAP) and sporadic polyps.

Methods: Polyps were compared among themselves and with either the paracancerous tissues or colon cancers. Pathological and gene expression profiling approaches were employed to characterize infiltrating immune cells and assess the expression of immune checkpoint genes.

Results: Our findings revealed that PJS polyps exhibited a closer resemblance to cancer tissues than other polyps in terms of their immune microenvironment. Notably, PJS polyps displayed heightened expression of the immune checkpoint gene CD80 and an accumulation of myeloid cells, particularly myeloid-derived suppressor cells (MDSCs).

Conclusion: The findings suggest an immunobiological foundation for the increased cancer susceptibility in PJS patients, paving the way for potential immune therapy applications in this population. Furthermore, utilizing PJS as a model may facilitate the exploration of immune evasion mechanisms, benefiting both PJS and cancer patients.

Keywords: CD80; CRC; MDSC; PJS; STK11; colorectal cancer; genetic; immunogenicity; polyposis; premalignant lesion.

FIGURE 1

Genome instability in PJS polyps. The transcriptomes from paracancerous tissues (ConA), sporadic polys (ConB), FAP polyps (FAP) and PJS polyps (PJS) were analyzed and visualized by PCA (a), t‐SNE (b) and UMAP (c) analysis. Their mutation rates of single base pair (d) and indel (e) were calculated and compared (mean ± s.d.).

FIGURE 2

Differential RNA expression of immune‐related molecules between PJS and FAP polyps. (a) The volcano figure of DEGs between PJS and FAP polyps. (b) The top 10 GO analysis results of the upregulated and downregulated DEGs between PJS and FAP polyps separately. (c) GSEA analysis results based on hallmark gene sets of the DEGs between PJS and FAP polyps. The representative figure of the hallmark inflammatory response gene set is shown (d).

FIGURE 3

Differential RNA expression of immune‐related molecules between PJS and sporadic colorectal polyps. (a) The volcano figure of DEGs between PJS and sporadic colorectal polyps. (b) The top 10 GO analysis results of the upregulated and downregulated DEGs between PJS and sporadic polyps separately. (c) GSEA analysis results based on hallmark gene sets of the DEGs between PJS and sporadic polyps. The representative figure of the hallmark inflammatory response gene set is shown (d).

FIGURE 4

Immune cell profiling of PJS polyps. Clustering analysis was performed based on the immune cell population estimated by transcriptome data from TCGA COADREAD cohort (COADREAD), PJS polys (PJS), FAP polyps (FAP), paracancerous tissues (ConA) and sporadic polys (ConB) (a). The immune cells include B cell (B_cell), CD4 T cell (CD4_T), CD8 T cell (CD8_T), naive CD4 T cell (CD4_naive), naive CD8 T cell (CD8_naive), cytotoxic T cell (Cytotoxic), exhausted T cell (Exhausted), type 1 regulatory T cell (Tr1), natural regulatory T cell (nTreg), induced regulatory T cell (iTreg), T helper 1 cell (Th1), T helper 2 cell (Th2), T helper 17 cell (Th17), follicular helper T cell (Tfh), gamma‐delta T cell (Gamma_delta), central memory T cell (Central_memory), effector and memory T cell (Effector_memory), natural killer cell (NK), natural kill T cell (NKT), mucosa‐associated invariant T cell (MAIT), dendritic cell (DC) et al. The infiltration scores calculated from transcriptomic data were compared among the TCGA‐COADREAD cohort and our own groups (b) (mean ± s.d.).

FIGURE 5

The accumulation of MDSCs in PJS polyps. The heatmap of the mRNA expression of various immune cell biomarkers in PJS polys (PJS), FAP polyps (FAP), paracancerous tissues (ConA) and sporadic polys (ConB) are shown (a). The ITGAM (b) and iNOS (c) positive immune cells were measured by IHC staining and compared in paracancerous tissue (Con), FAP polyps (FAP) and PJS polyps (PJS) (mean ± s.d.). Quantification of signal‐positive immune cells was conducted using the 100× images, while the 400× images were included to demonstrate the cellular localization of the signal.

FIGURE 6

Immune checkpoint in PJS polyps. The heatmap of the mRNA expression of the immune checkpoint related genes in paracancerous tissues (ConA), sporadic polys (ConB), FAP polyps (FAP) and PJS polyps (PJS) is shown (a). IHC staining was performed to quantify and compare the PD‐1 (b), PD‐L1 (c) and CD80 (d) positive immune cells in paracancerous tissue (Con), FAP polyps (FAP) and PJS polyps (PJS) (mean ± s.d.). Quantification of signal‐positive immune cells was conducted using the 100× images, while the 400× images were included to demonstrate the cellular localization of the signal.