Understanding the spatial determinants of the Oxford Classic prognostic signature for high-grade serous ovarian cancer

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BACKGROUND: The Oxford Classic (OxC) prognostic signature classifies high-grade serous ovarian cancer (HGSOC) into five transcriptional programs, with epithelial-to-mesenchymal transition (EMT) marking poor prognosis. While successful in bulk transcriptomics, the spatial organisation of these programs within the tumour microenvironment remains unexplored. METHODS: We developed the Signature-guided Zero-inflated Beta Variational Autoencoder (Sig-ZIB-VAE), a deep learning deconvolution method tailored for spatial transcriptomics data, and applied it to a large-scale HGSOC cohort comprising 94 tumours to quantify spatial cellular organisation. Prognostic significance was assessed using penalised Cox proportional hazards regression integrating clinical, molecular, and spatial features. RESULTS: Here we show that EMT cells form dense homotypic clusters broadly depleted from stromal and immune neighbourhoods, yet maintain selective monocyte co-localisation at cluster boundaries. EMT-high tumours display enhanced spatial reorganisation characterised by increased clustering and connectivity, forming locally concentrated mesenchymal-rich domains. Survival analysis confirms EMT-high status as an adverse prognostic factor. CONCLUSIONS: Critically, spatial metrics of immune cell organisation-particularly monocyte connectivity and clustering-provide substantially stronger prognostic discrimination than EMT proportion alone, demonstrating that tumour microenvironment architecture supersedes cellular composition in determining clinical outcomes in HGSOC.

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