Abstract B026: Leveraging patient-derived models to identify novel treatment options in high-grade serous ovarian cancer

Abstract Ovarian cancer (OC) is a leading cause of gynecological cancers deaths in women. Despite high histological and genetic heterogeneity of OC, the standard treatment remains largely unchanged, consisting of cytoreductive surgery followed by platinum-taxol chemotherapy. The introduction of PARP inhibitor in OC treatment shows some advancements, but high relapse rates and acquired resistance to secondary treatment continue to contribute to poor 5-year survival rate, highlighting the need for improved and personalized treatment strategies. Hence in this study, we applied a functional precision medicine approach to systematically profile patients at molecular and drug-response levels, aiming to identify novel candidate biomarkers and potential therapeutic strategies. We developed a collection of patient-derived models (PDMs) from 22 OC patients, comprising of 21 cancer and 14 fibroblasts models, covering 7 OC subtypes, predominantly representing high grade serous OC. Cancer models were successfully validated by matching genetic profile with original tumor samples, proving to recapitulate the key genetic aberrations. These models were compatible with high-throughput drug screening assay and were further profiled against library of 116-528 oncology compounds and small molecules. Drug screening revealed patient-specific drug response profile. Overall, around 85% of all drugs demonstrated no systemic toxicity based on bone marrow cell drug response, indicating their potential for anti-cancer activity. Our data identified EGFR/ERBB2, mTORC1 inhibitors, and BH3 mimetics as drug subclasses showing selective efficacy against cancer cells. One of the drugs, Bcl-xL inhibitor A-1331852, exhibited differential sensitivity and resistance in several patients. To better understand the underlying mechanisms of differential response, we used mass-spectrometry based proteomics. Our data detected several differentially enriched pathways which are currently being tested as combination drug treatments to combat the resistance to A-1331852. In conclusion, our study demonstrates the potential of PDMs to identify novel drug response patterns that hold potential as future treatment options. These findings highlight the importance of functional and molecular characteristics of individual patients to effectively target relevant disease vulnerabilities. Citation Format: Greta Gudoityte, Olena Berkovska, Lukas Orre, Elisabeth Moussaud-Lemoderie, Josefin Fernebro, Ulrika Joneborg, Olli Kallioniemi, Brinton Seashore-Ludlow. Leveraging patient-derived models to identify novel treatment options in high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B026.