Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/3/829 |
| _version_ | 1797488653248757760 |
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| author | Ha-Yeon Shin Eun-ju Lee Wookyeom Yang Hyo Sun Kim Dawn Chung Hanbyoul Cho Jae-Hoon Kim |
| author_facet | Ha-Yeon Shin Eun-ju Lee Wookyeom Yang Hyo Sun Kim Dawn Chung Hanbyoul Cho Jae-Hoon Kim |
| author_sort | Ha-Yeon Shin |
| collection | DOAJ |
| description | 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 (<i>p</i> < 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. |
| first_indexed | 2024-03-10T00:05:23Z |
| format | Article |
| id | doaj.art-8276fdf1c14147528d399cf7937d9abe |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T00:05:23Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-8276fdf1c14147528d399cf7937d9abe2023-11-23T16:09:21ZengMDPI AGCancers2072-66942022-02-0114382910.3390/cancers14030829Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse ModelsHa-Yeon Shin0Eun-ju Lee1Wookyeom Yang2Hyo Sun Kim3Dawn Chung4Hanbyoul Cho5Jae-Hoon Kim6Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaSeverance Biomedical Science Institute, Yonsei University College of Medicine, Seoul 03722, KoreaDepartment of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaPatient-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 (<i>p</i> < 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.https://www.mdpi.com/2072-6694/14/3/829patient-derived xenograftgynecologic cancerprognostic markers |
| spellingShingle | Ha-Yeon Shin Eun-ju Lee Wookyeom Yang Hyo Sun Kim Dawn Chung Hanbyoul Cho Jae-Hoon Kim Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models Cancers patient-derived xenograft gynecologic cancer prognostic markers |
| title | Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models |
| title_full | Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models |
| title_fullStr | Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models |
| title_full_unstemmed | Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models |
| title_short | Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models |
| title_sort | identification of prognostic markers of gynecologic cancers utilizing patient derived xenograft mouse models |
| topic | patient-derived xenograft gynecologic cancer prognostic markers |
| url | https://www.mdpi.com/2072-6694/14/3/829 |
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