Abstract 6477: Tumor microenvironmental alterations and novel biomarkers in metastatic ovarian cancer identified with spatial transcriptomics

Abstract Background: Ovarian cancer (OC) is one of the most common causes of cancer in women in the developed world and causes many cancer-related deaths every year. Patients are frequently discovered at advanced stages of the disease, and metastasis can be detected in 30% of patients at the time of diagnosis, with an additional 30%-40% developing metastases few years later. Current therapy methods for ovarian cancer metastasis are typically ineffectual, and mortality rates for individuals with ovarian cancer metastases are consequently high. As a result, OC remains one of the most serious clinical disorders and a life-threatening risk to women. Investigations into the tumor microenvironment (TME) and biomarkers associated with ovarian cancer metastasis may give critical benefits in understanding the underlying molecular pathways and identifying new targets for clinical diagnosis and treatment. Methods: We investigated the TME between metastatic platinum sensitive recurrent ovarian cancer (PSROC) tissues and primary OC tissues using sc-RNAseq, spatial transcriptomics with 10x Visium spatial capture technology. The data were analyzed using R, DAVID and Cytoscape software to identify potential biomarkers. Results: By analyzing the spatial transcriptomic data of PSROC metastases and primary lesions, we identified significant changes in multiple energy metabolism signaling pathways and significantly different tumor-associated macrophage groups. Notably, the metastatic tissue showed fewer B cells, T cells, and macrophages differentiation and infiltration. On the other hand, the metastatic tissue showed more monocyte and DC cell proportion when compared to primary tissue. The spatial location of each cell type in the different tissues was investigated, showing great disparity in the cell arrangement within each tissue type. The average log2FC of the differentially expressed genes (DEG) between the two tissue types was determined, thus providing us with cell and type-specific gene sets. The cutoff values were set to avg_Log2FC >= |0.58| and p_val_adj (FDR) < 0.05. We obtained 1226 DEG ups and 1011 DEG downs. The DEGs were used for KEGG pathways analysis which gave 40 significantly enriched pathways, with notably oxidative phosphorylation, p53 signaling pathway, cell cycle and HIF-1 signaling pathways. By combining KEGG pathway data with PPI and hub-gene analysis we identified potential genes that may play significant role in ovarian cancer metastasis, energy reprogramming and immune escape; TOP2A, CCNB1, CDK1, CCNA2, AURKB, TP53, CD4, EGFR, JUN, HIF1A, FN1 and CTNNB1. These genes will be further investigated through in vitro and in vivo experiments. Conclusions: Those results provide a new theoretical basis for the diagnosis and treatment of PSROC, which will undoubtedly help in the prevention, molecular diagnosis and development of more precise therapeutic approaches. Citation Format: Serge Yannick Ouedraogo, Jinming Yu, Na Li, Xianquan Zhan. Tumor microenvironmental alterations and novel biomarkers in metastatic ovarian cancer identified with spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6477.