High-grade serous ovarian carcinoma (HGSOC) is characterised by ubiquitous TP53 mutation, genomic structural variation, and extreme chromosomal instability (CIN)1. Classification approaches thus far have been hindered by the genomic complexity of this disease, presenting a major challenge for the development of precision medicine to treat patients with HGSOC. Deconvoluting the mechanisms of chromosomal instability which drive genomic complexity will act as the basis for improved diagnostic and therapeutic approaches, advancing precision medicine and providing more options for women with this lethal disease.
Copy number signatures—associated with biological mechanisms of pathogenesis—provide an opportunity to investigate the genomic complexity of HGSOC and target therapeutics accordingly2. To interrogate copy number signatures as biomarkers of therapeutic vulnerabilities, I have generated and characterised a panel of 3D organoid models derived from HGSOC patient-derived xenografts (PDX). These PDX models have been confirmed to be representative of homologous-recombination deficient or homologous-recombination proficient HGSOC based on their response to poly ADP ribose polymerase (PARP) inhibitor treatment in vivo. Morphological characterisation of the organoids demonstrated differing levels of circularity and cellular cohesiveness within organoid structures, while immunohistochemistry confirmed both the organoids and the PDX models were histologically similar to the primary patient sample. Organoid growth kinetics were also determined in preparation for high-throughput organoid-based compound screening. Copy number signature analyses are currently being undertaken to determine if copy number signatures assigned in patient samples are accurately represented in these models.
Drug screening will be carried out on this panel of HGSOC organoid models to identify therapeutic vulnerabilities associated with specific copy number signatures. As such, this panel of HGSOC organoid and PDX models provide important tools for understanding patterns of copy number aberration and will contribute to achieving a broad mechanistic understanding of this disease.