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

Ha Yeon Shin, Eun Ju Lee, Wookyeom Yang, Hyo Sun Kim, Dawn Chung, Hanbyoul Cho, Jae Hoon Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number829
Issue number3
Publication statusPublished - 2022 Feb 1

Bibliographical note

Funding Information:
Funding: This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A6A3A13092928, NRF-2021R1A2B5B02001915). The research was performed with the support of The Health Fellowship Foundation.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research


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