Development and validation of an efficacy assessment platform utilizing patient-derived xenograft (PDX) models in ovarian cancer research.

Abstract

e17564 Background: Ovarian cancer predominantly undergoes treatment through surgical and chemotherapeutic interventions. However, with an increasing rate of recurrence, there's a need for systemic chemotherapy. A paradigm shift towards personalized treatment is crucial with concerns escalating about chemotherapy resistance post PARP inhibitor treatment. This paper emphasizes the significance of preclinical drug evaluation platforms using Patient-Derived Xenograft (PDX) models, which resonate with the genuine characteristics of ovarian cancer tissues. Methods: The 112 epithelial ovarian patients' tumors sectioned to approximately 3 mm 2 were subcutaneously transplanted into at least two female 5-week-old BALB/c nude mice. The xenograft tumors engrafted into mice from patient-derived tumors were defined as P1. After that, xenograft tumors serially engrafted into the following mice were termed P2 to P3. We collected and banked small pieces of tumor tissue from each passage of PDX tissue for performing Whole Exome Sequencing (WES) sequencing and RNA Sequencing. Results: We established 54 epithelial ovarian cancer PDX models that are stably growing. They show a ratio of 70.37% Serous, 11.11% clear cell, and 3.7% endometriod. The stage is 14.81% I/II, 61.11% III/IV, and 24.07% Recurrent. BRCA status consists of 59.26% wild type, 9.26% BRCA 1 mutation, 5.56% BRCA 2 mutation, and 11.11% BRAC VUS. We have obtained a variety of ovarian cancer PDX models and confirmed retained the histological characteristics of patients by H&E staining. We validated the similarity of genomic information in patients and PDX models via WES and RNA sequencing. Conclusions: We have a stable and growing PDX model that accurately reflects the actual tissue characteristics of ovarian cancer. In the future, we plan to characterize drug response further and build new models resistant to existing anti-cancer drugs. Based on these, we will validate the efficacy of new drugs and suggest strategies to overcome cancer resistance.

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