Oehler, M. K. | Search bioRxiv

bioRxiv2026

Ascites-Driven Modulation of Cell Phenotypes and Proteomes: Implications for Cancer Progression

BackgroundMore than 90% of advanced ovarian cancer patients develop malignant ascites, which describes a buildup of fluid in the peritoneal cavity caused by increased vascular permeability and obstructed lymphatic drainage. Malignant ascites contains spheroidal tumor cell clusters that contain stromal cells, cancer-associated fibroblasts, and blood cells. These spheroids promote peritoneal metastasis and treatment resistance, yet the phenotypic and proteomic changes of cells caused by the ascites environment remain poorly understood, as does its influence on ex vivo responses to chemotherapeutics in personalized medicine approaches. MethodsUsing mass spectrometry, we compared the proteome profiles of cell-free ascites to serum from ovarian cancer patients. We then analyzed the proteomes of immortalized cancer cells grown as monolayers or spheroids in either malignant ascites or standard cell culture medium. The effects of this fluid on the phenotype, molecular composition, and ex vivo chemotherapy responses of cancer cells were also investigated. ResultsProteome analysis revealed that cell-free ascites had higher levels of extracellular, secreted, and membrane proteins compared to serum. Ascites enhanced cell viability and spheroid formation in immortalized ovarian cancer cell lines more effectively than standard cell culture medium. Despite this altered baseline viability, growth of spheroids in ascites versus cell culture medium did not hinder chemotherapy response assessments, indicating the appropriateness of standard cell culture medium in ex vivo applications. The observed phenotypic changes of cells grown in ascites could not be recapitulated by adding chemokines or periostin to the cell culture medium, suggesting that additional factors are required. Notably, elevated levels of transglutaminase 2 were identified in SKOV-3 cells grown in ascites, indicating that ascites directly influences protein expression in cancer cells.

cancer biology↗

bioRxiv2025

DOMINO: diffusion-optimised graph learning identifies domain structures with enhanced accuracy and scalability

Spatial transcriptomics enables in situ molecular profiling, allowing to measure the cellular transcriptional output within the tissue. As the tissue architecture is conserved, spatial domains with specific transcriptional patterns can be identified, facilitating the discovery and understanding of functional tissue compartments. Thus, several methods to uncover and identify these spatial domains have been developed. However, most of these existing methods do not scale to rapidly increasing data sizes and focus only on local structure while missing the global view of the tissue. Here, we present DOMINO, a diffusion-optimised contrastive learning framework for spatial domain detection. DOMINO utilises graph diffusion convolution to propagate information beyond immediate neighbours and jointly optimises local and, importantly, global graph structure via contrastive learning. This novel framework yields biologically interpretable domains with clearer boundaries and scales to large datasets, outperforming state-of-the-art methods across healthy and malignant benchmark datasets. We apply DOMINO to a newly generated spatial transcriptomic dataset of endometriosis-associated ovarian cancers, which could not be processed by existing domain detection methods owing to its size. We uncovered conserved proliferative and non-proliferative tumour states that recurred across these tumours and were independently validated in an external clear cell ovarian cancer spatial transcriptomic dataset. Proliferative domains were characterised by elevated expression of EIF4A1 and HSPA8, increased cell cycle activity, reduced mast cell abundance, and coordinated stromal remodelling, including altered fibroblast states and spatial organisation. In parallel, integrative analysis across tumours revealed subtype-specific multicellular ecosystems associated with either endometrioid or clear cell ovarian carcinomas, together with a tumour-excluded stromal domain that could only be resolved through the integration of spatial and transcriptional information. These findings demonstrate how well DOMINO scales up and that it uncovers biologically meaningful spatial programs spanning tumour intrinsic states, tumour microenvironment interactions, and subtype-specific tissue architecture that are not recovered by conventional expression-based clustering approaches.

bioinformatics↗