Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models

SIMPLE SUMMARY: As a preclinical model for personalized cancer therapy, the patient-derived xenograft (PDX) model is suitable because it provides the best representation of the original tumor, but it still has many limitations. We analyzed gynecologic cancer PDX models with their clinical information and gene expression profiling, and found that the success rate of PDX correlated with the patient’s tumor grade and prognosis. Moreover, we showed that the faster the tumor progressed in PDXs, the poorer the prognosis in ovarian cancer patients. We confirmed that the differentially expressed genes (DEGs) selected according to PDX engraftment status could be a prognosis marker of ovarian clear cell cancer patients. This study paves the way for a better application of the PDX in gynecologic cancer. ABSTRACT: Patient-derived xenografts (PDXs) are important in vivo models for the development of precision medicine. However, challenges exist regarding genetic alterations and relapse after primary treatment. Thus, PDX models are required as a new approach for preclinical and clinical studies. We established PDX models of gynecologic cancers and analyzed their clinical information. We subcutaneously transplanted 207 tumor tissues from patients with gynecologic cancer into nude mice from 2014 to 2019. The successful engraftment rate of ovarian, cervical, and uterine cancer was 47%, 64%, and 56%, respectively. The subsequent passages (P2 and P3) showed higher success and faster growth rates than the first passage (P1). Using gynecologic cancer PDX models, the tumor grade is a common clinical factor affecting PDX establishment. We found that the PDX success rate correlated with the patient’s prognosis, and also that ovarian cancer patients with a poor prognosis had a faster PDX growth rate (p < 0.0001). Next, the gene sets associated with inflammation and immune responses were shown in high-ranking successful PDX engraftment through gene set enrichment analysis and RNA sequencing. Up-regulated genes in successful engraftment were found to correlate with ovarian clear cell cancer patient outcomes via Gene Expression Omnibus dataset analysis.