#252 Evolutionary study identifies poor prognosis HGSC patients and reveals clinically relevant targets at diagnosis and relapse

RefSeq Serous ovarian cancer
DOI: 10.1136/ijgc-2023-esgo.24 Publication Date: 2023-09-28T10:42:58Z
ABSTRACT
<h3>Introduction/Background</h3> Ovarian high-grade serous carcinoma (HGSC) is commonly diagnosed at an advanced stage, showing multiple genetically heterogeneous clones existing prior to therapeutic intervention. <h3>Methodology</h3> Clonal composition and topology were estimated from whole-genome sequencing data 510 samples of 148 patients in the prospective, longitudinal, multiregion DECIDER study. Detected subclones followed 152 longitudinal circulating tumour DNA (ctDNA) samples, by using targeted sequencing. Multiomics included H&amp;E-stained images RNA data. <h3>Results</h3> Based on phylogenies, stratified into three evolutionary states with distinct subclonal heterogeneity within between metastases: 1) Evolving state mostly monoclonal shows only some differences sampled sites. 2) Maintaining highly polyclonal sites, but similar, stable patterns 3) Adaptive has high metastases that show parallel evolution, making them most divergent. Tumours belonging maintaining had worst prognosis (p=0.008). Prognosis difference was independent homologous recombination (HRD), which found equally each states. States characterized molecular pathways morphological features. PI3K/AKT pathway enriched tumours, it shown be targetable alpelisib patient-derived organoids. After treatment, revealed heterogenic acquired mutations few cases underwent selection. Evolutionary modified changing selection pressure caused treatments. <h3>Conclusion</h3> Our results HGSC tumours are not all genomically unstable time diagnosis. We showed for grow mixtures remain similar metastases. These alterations, successfully monotherapy Multiple lines treatments altered diagnostic relapses, relapsed disease a moving target. <h3>Disclosures</h3> The study been funded European Union's Horizon 2020 Research Innovation Programme under Grant agreement no. 965193 (DECIDER).
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