Spatial Transcriptomic Landscape of Canine Oral Squamous Cell Carcinoma

Abstract

Canine oral squamous cell carcinoma (COSCC) is the second most common oral tumor in dogs and the most relevant for comparative human trials as a spontaneous large animal model of disease. Historical genomic work has focused primarily on bulk sequencing. The present study describes the complete transcriptomic landscape of COSCC with spatial distinction between the surface tumor, deep invasive tumor, peritumoral dysplastic epithelium, and tumor microenvironment compared to matched normal oral samples. Each region demonstrated distinct molecular signatures. Genes related to epithelial growth factor (EGFR) and epithelial-mesenchymal transformation (EMT) were upregulated in both peritumoral dysplasia and surface cancer. Additionally, the KRAS gene set, KRT17, and SSP1 were enriched in cancer. We identified five genes that represent dysplastic lesion with high potential for malignant transformation (FZD4, GAS1, HACD2, NOG, and SLC39A6). Also, three genes, SFRP4, FZD1, and IL34 represented a specific signature of the invasive portion of the COSCC that should be explored for prognostic value as a biomarker of malignancy. Lastly, we verified the immunomodulatory tumor microenvironment detecting an increase in macrophages and an abundance of IL-10 secretion. The other predominant leukocytes were T-cells, with CD4+ T-cells being the most prevalent. CD4+ T cells expressed transcripts for both stimulatory (Inducible T-cell Co-Stimulator (ICOS) and inhibitory molecules (CTLA4). The observed high CTLA4 suggests that this inhibitory signal may be preventing a robust antitumor immune response. Taken together, this study identified multiple targets to be explored for biomarkers of malignancy, prediction of tumor behavior, and potential targets for development of novel therapies.

Keywords: head and neck squamous cell carcinoma; oral cancer; oral dysplasia; oral squamous cell carcinoma; transcriptomics.

Figure 1

ROI selection utilizing visual markers. Representative immunofluorescently‐labeled tissue section encompassing the normal oral epithelium, surface tumor, invasive neoplastic epithelium, and TME (peritumoral mesenchymal stroma). For each tissue phenotype, four regions of interest (ROI) were specified (including on average 300 cells) followed by the gene expressing analyses in these particular ROIs. Blue: nuclear stain (syto 13), green: panCK, yellow: CD45.

Figure 2

Differentially expressed genes between surface cancer and normal tissue. Principal component analysis and heat maps showing ROI relationships/variations and clustering patterns of differentially expressed genes between surface cancer versus normal (A), peri‐tumoral dysplasia versus normal (B), and surface cancer versus dysplasia (C).

Figure 3

Differentially expressed genes that distinguish surface cancer, dysplasia, and normal tissue. Volcano plots depict the upregulated (red), downregulated (blue), and nonsignificant (grey) differentially expressed genes (LMM, p < 0.05) from group comparisons between surface cancer versus normal (A), peri‐tumoral dysplasia versus normal (B), and surface cancer vs. dysplasia (C). The log2(fold change) and −log10(p value) are indicated on the x‐ and y‐axis, respectively. The 30 most significant DEGs are labeled. Intersection analysis was conducted and the number of differentially expressed genes that overlap between surface cancer, dysplasia, and normal phenotypes is depicted in the Venn diagram (D).

Figure 4

Relatedness and differentially expressed genes between deep invasive and surface cancer. PCA projection, volcano plot, and clustering heatmap showing the differentially expressed genes between deep invasive and surface cancer.

Figure 5

Functional enrichment analyses. Differential expression analyses were performed for the indicate comparisons as described above and in Materials and Methods. GSEA was performed using the MSigDB Hallmark and C6 oncogenic gene signature collections. Scatterplots depict normalized enrichment scores (NES) from GSEA conducted with MSigDB Hallmark and C6 oncogenic signature gene sets across the indicated comparisons of (1) dysplasia compared to surface normal and (2) surface cancer compared to surface normal (upper panels) and (3) dysplasia compared to surface normal and (4) surface cancer compared to surface dysplasia (lower panels). Gene sets exhibiting statistical differential enrichment or depletion (NES FDR q‐value < 0.05) for the comparisons on the x‐axis are shown in red.

Figure 6

Immune cell composition of COSCC tumor microenvironment. (A) Mixed cell deconvolution analysis was conducted on the gene expression data for ROIs selected from the TME and surface normal tissue. Results are shown as stacked bar charts of proportion of fitted stromal and immune cells in each phenotype (tumor stroma; normal). (B) Principal component analysis of the scaled immune scores per regions of the tumor and normal tissue. (C and D) The abundance of macrophages (C) and neutrophils (D) in each patient in the tumor microenvironment compared to the deep invasive tumor and normal tissue, where each point represents the average value of three ROIs per patient. * p < 0.05; ** p < 0.001; ns, nonsignificant.

Figure 7

Immune cell genes and relationship with T cells and Macrophages in COSCC tumor microenvironment, deep tumor, and normal tissue. (A) CD4+ memory T cell frequency, (B) ICOS gene expression, and (C) CTLA4 gene expression in deep invasive tumor, normal tissue, and the TME, where each point represents the average value of three ROIs per patient. (D) Relative counts of clinically impactful immune genes in the TME compared normal epithelium, again averaging across the ROIs per patient. * p < 0.05; ** p < 0.001; ns, nonsignificant. (E and F) Macrophage score correlation with epithelial‐mesenchymal transformation (EMT), showing macrophages are negatively correlated with epithelial marker E‐cadherin (CHD‐1 expression) and positively correlated with the mesenchymal marker vimentin (VIM expression). * p < 0.05; ** p < 0.001; *** p < 0.0001; ns, nonsignificant.