Abstract
Ovarian cancers with similar histopathologic but diverse immunogenomic profiles may require different treatment regimens to achieve the best clinical outcomes. Development of optimal treatment regimens will thus require an understanding of the main ways in which ovarian cancers differ with respect to their immunogenomic profiles. We used a multi-modal latent variable model to enable an integrated analysis of somatic mutation, mRNA expression, and multiplex immunofluorescence data, to uncover the principal drivers of inter-tumour heterogeneity across 197 ovarian cancer patients. We found that the majority of the immunogenomic inter-tumour heterogeneity was driven by gene expression and immune infiltration in the tumour core and invasive margin. Moreover, much of this heterogeneity could not be explained by histological subtype; somatic mutation patterns explained much of the difference between high-grade serous and other subtypes. Clustering of samples according to their positions in the latent space revealed a distinct subgroup of Endometrioid Ovarian cancer tumours, characterised by increased CST2 expression and improved prognosis, among other immunogenomic features. We identified a group of collagen-related genes, whose expression in all subtypes was inversely correlated with the density of proliferating tumour cells in the tumour core and associated with increased levels of inflammatory fibroblasts, independent of tumour purity. In summary, our findings advocate for a paradigm shift in how we classify and approach the treatment of ovarian cancer. By moving beyond the constraints of histological subtypes and delving into the molecular intricacies of patient-specific tumour microenvironments, we unveil a new opportunity for targeted immuno-oncological treatment of a subset of ovarian cancer patients.

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