Abstract 5540: Deconstructing the lethality of recurrence in ovarian cancer through multi-modal spatiotemporal analysis

Abstract Epithelial ovarian cancer (EOC) represents the world’s most lethal gynecological malignancy and is commonly associated with chemo-resistance and recurrence. Strikingly, up to 70% of EOC patients will relapse where subsequent care becomes palliative. Owing to a lack of model systems and a scarcity of matched treatment-naïve and recurrent clinical samples, few studies have addressed the complex relationship between first-onset EOC and its respective relapse. Identification of in situ tumor phenotypes that resisted initial chemotherapy treatment and initiate recurrent tumors, will allow us to understand which cellular features lead to relapse. This will allow us to better predict tumor recurrence and to ultimately suggest novel therapies. Here we analyze primary and recurrent tumor pairs using highly multiplexed imaging technologies. Through integration of spatial transcriptomics and imaging mass cytometry (IMC), we identify and classify resistance phenotypes in over 320 EOC patients, identifying phenotypes that persist in recurrent tumors. We leverage these technologies to assign novel proteo-transcriptomic resistance modules that allow us to independently observe these phenotypes across multiple single-cell modalities. To uncover phenotype-specific drug sensitivities, we utilized scRNA-seq data to predict in silico drug-response and validated these findings through large-scale image-based ex vivo drug screening. These screens revealed significant multi-drug resistance (MDR) properties of persistent phenotypes, but unveiled sensitivity to alternate FDA-approved drug classes. Together, our work represents the first-of-its-kind high-dimensional map of resistant and recurrent EOC, offering tangible therapeutic solutions for the treatment and prevention of recurrence. Citation Format: James M. Whipman, Francis Jacob, Viola Heinzelmann, Bernd Bodenmiller. Deconstructing the lethality of recurrence in ovarian cancer through multi-modal spatiotemporal analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5540.