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- Title
Genetic Profiles Associated with Chemoresistance in Patient-Derived Xenograft Models of Ovarian Cancer.
- Authors
Lan Ying Li; Hee Jung Kim; Sun Ae Park; So Hyun Lee; Lee Kyung Kim; Jung Yun Lee; PSunghoon Kim; Young Tae Kim; Sang Wun Kim; Eun Ji Nam
- Abstract
Purpose Recurrence and chemoresistance (CR) are the leading causes of death in patients with high-grade serous carcinoma (HGSC) of the ovary. The aim of this study was to identify genetic changes associated with CR mechanisms using a patient-derived xenograft (PDX) mouse model and genetic sequencing. Materials and Methods To generate a CR HGSC PDX tumor, mice bearing subcutaneously implanted HGSC PDX tumors were treated with paclitaxel and carboplatin. We compared gene expression and mutations between chemosensitive (CS) and CR PDX tumors with whole exome and RNA sequencing and selected candidate genes. Correlations between candidate gene expression and clinicopathological variableswere explored using the Cancer Genome Atlas (TCGA) database and the Human Protein Atlas (THPA). Results Three CR and four CS HGSC PDX tumor models were successfully established. RNA sequencing analysis of the PDX tumors revealed that 146 genes were significantly up-regulated and 54 genes down-regulated in the CR group compared with the CS group. Whole exome sequencing analysis showed 39 mutation sites were identified which only occurred in CR group. Differential expression of SAP25, HLA-DPA1, AKT3, and PIK3R5 genes and mutation of TMEM205 and POLR2A may have important functions in the progression of ovarian cancer chemoresistance. According to TCGA data analysis, patients with high HLADPA1 expression were more resistant to initial chemotherapy (p=0.030; odds ratio, 1.845). Conclusion We successfully established CR ovarian cancer PDX mouse models. PDX-based genetic profiling study could be used to select some candidate genes that could be targeted to overcome chemoresistance of ovarian cancer.
- Subjects
RNA sequencing; OVARIAN cancer; GENETIC models; GENE expression; SEQUENCE analysis
- Publication
Cancer Research & Treatment, 2019, Vol 51, Issue 3, p1117
- ISSN
1598-2998
- Publication type
Article
- DOI
10.4143/crt.2018.405