Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

Abstract

Background: High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.

Methods: Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.

Results: By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.

Conclusions: This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Keywords: High-grade serous ovarian cancer; Radiogenomics; Spatial transcriptomics; Tumor microenvironment.

Fig. 1

Characteristics of HGSOC using spatial transcriptomics. A Pathological annotation results of hematoxylin and eosin-stained tissue sections were conducted by a pathologist. B Gene expression-based clusters of tissue sections were identified by the UMAP-based dimensional reduction technique. C Cell type enrichment plot demonstrates the degree of enrichment of tumor and stromal cells. D Heatmap demonstrates cell type enrichment scores by the gene expression-based cluster. The x-axis represents clusters by gene expression pattern, and the y-axis represents the degree of cell type enrichment. E Organizational tissue sections separated by tumor and stromal regions. F Organizational tissue sections separated by tumor, stromal, and immune regions. ST = Spatial transcriptomics, SC = Single-cell RNA sequencing

Fig. 2

Heterogeneity associated with HGSOC. The heat map demonstrates the top 100 differentially expressed genes according to recurrence (adjusted p-value < 0.05 and log2FC > 2.0 or < -2.0). Columns represent the top 100 differentially expressed genes by tissue regions of (A) tumor, (B) stroma, and (C) immune. The enrichment plot indicates the top 5 hallmark pathways by tissue regions of (A) tumor, (B) stroma, and (C) immune

Fig. 3

Distinct copy number variation patterns between spatial clusters. A Organizational tissue sections separated by copy number variation clusters according to the recurrent and non-recurrent patients. B The heat map indicates the rate of copy number variation in each recurrent and non-recurrent HGSOC sample. Columns represent the genomic region. The color key indicates the degree of score of copy number variation in direction to gain (red) or loss (blue). Each heat map was separated according to the tissue regions of the tumor, stroma, and immune using the color bars. Arrows indicate genomic regions representing different copy number variation profiles in the tumor, stromal, and immune regions

Fig. 4

Differences of ligand-receptor crosstalk underlying HGSOC pattern. A The dot plot represents putative ligand–receptor interactions between spatial regions where each cell population is enriched. The size of the dot indicates the statistical significance of the indicated interactions. The color of the dot represents the means of the average expression level of the ligand and receptor from each interaction. B UMAP and spatial feature plots indicate the expression of genes specifically noted in the recurrent and non-recurrent patients, respectively (R3 and NR2). C The average expression of genes in the significant ligand–receptor pairs in both the recurrent and non-recurrent groups. D Violin plots represent the expression of genes, which are included in significant ligand–receptor crosstalk results and differentially expressed in recurrent or non-recurrent groups

Fig. 5

Radiogenomics profiling between CT phenotypes and spatial transcriptomic patterns. Patterns of the case and control CT phenotypes are shown: (A) bilaterality of ovarian mass (case; red bar) and unilaterality of ovarian mass (control; blue bar), (B) higher number of location (≥ 3) (case; red bar) and lower number of location (< 3) (control; blue bar). The heat map demonstrates the top 100 differentially expressed genes according to each CT phenotype from eight internal HGSOC samples (adjusted p-value < 0.05 and log2FC > 2.0 or <  − 2.0). Columns represent the top 100 differentially expressed genes. The color key indicates the degree of differential gene expression in either direction to upregulation (yellow) or downregulation (purple). Violin plots represent differentially expressed genes relevant to the CT phenotypes of both eight internal HGSOC and 40 public HGSOC TCIA and TCGA samples. The CT examples with a red border indicates case and the CT example with a blue border indicates control of each CT phenotypes. A Bilateral ovarian mass (arrows) in case example. Unilateral ovarian mass (arrows) in control example. B Three locations of seeding metastases (arrows) at right upper quadrant, left upper quadrant, and omentum in case example. Single location of seeding metastasis at pelvic cavity (arrows) in control example