Genetic Profiles Associated with Chemoresistance in Patient-Derived Xenograft Models of Ovarian Cancer
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Purpose Recurrence and chemoresistance (CR) are the leading causes of death in patients with highgrade 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 variables were 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.
Abstract
Purpose Recurrence and chemoresistance (CR) are the leading causes of death in patients with highgrade 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 variables were 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.
참고문헌 (29)
- Romero I, "Minireview : human ovarian cancer : biology, current management, and paths to personalizing therapy", Endocrinology, 153, pp.1593~1602 (2012)
- Holmes D., "The problem with platinum", Nature, 527, pp.S218~S219 (2015)
- Tredan O, "Drug resistance and the solid tumor microenvironment", J Natl Cancer Inst, 99, pp.1441~1454 (2007)
- Senthebane DA, "The role of tumor microenvironment in chemoresistance: to survive, keep your enemies closer", Int J Mol Sci, 18, pp.E1586~ (2017)
- Patch AM, "Whole-genome characterization of chemoresistant ovarian cancer", Nature, 521, pp.489~494 (2015)
- Bast RC Jr, "Personalizing therapy for ovarian cancer : BRCAness and beyond", J Clin Oncol, 28, pp.3545~3548 (2010)
- Dobbin ZC, "Using heterogeneity of the patient-derived xenograft model to identify the chemoresistant population in ovarian cancer", Oncotarget, 5, pp.8750~8764 (2014)
- Liu JF, "Establishment of patient-derived tumor xenograft models of epithelial ovarian cancer for preclinical evaluation of novel therapeutics", Clin Cancer Res, 23, pp.1263~1273 (2017)
- Etemadmoghadam D, "Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas", Clin Cancer Res, 15, pp.1417~1427 (2009)
- Li H, "Fast and accurate short read alignment with Burrows-Wheeler transform", Bioinformatics, 25, pp.1754~1760 (2009)
- McKenna A, "The Genome Analysis Toolkit : a Map Reduce framework for analyzing next-generation DNA sequencing data", Genome Res, 20, pp.1297~1303 (2010)
- Dobin A, "STAR : ultrafast universal RNA-seq aligner", Bioinformatics, 29, pp.15~21 (2013)
- Trapnell C, "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation", Nat Biotechnol, 28, pp.511~515 (2010)
- Rahman M, "Cancer metabolism and drug resistance", Metabolites, 5, pp.571~600 (2015)
- Ghazy AA, "Relevance of HLA-DP", DQ and ICAM-1 SNPs among ovarian cancer patients, 2016, pp.7, 202~
- Kubler K, "Genetic alterations of HLA-class II in ovarian cancer", Int J Cancer, 123, pp.1350~1356 (2008)
- Shiio Y, "Identification and characterization of SAP25, a novel component of the mSin3 corepressor complex", Mol Cell Biol, 26, pp.1386~1397 (2006)
- Burris HA 3rd, "Overcoming acquired resistance to anticancer therapy : focus on the PI3K", AKT, Cancer Chemother Pharmacol, pp.2013, 71[DOI]
- Shaw RJ, "Ras, PI(3)K and mTOR signalling controls tumour cell growth", Nature, 441, pp.424~430 (2006)
- Cancer Genome Atlas Research Network, "Integrated genomic analyses of ovarian carcinoma", Nature, 474, pp.609~615 (2011)
- Yuan TL, "PI3K pathway alterations in cancer : variations on a theme", Oncogene, 27, pp.5497~5510 (2008)
- Massacesi C, "PI3K inhibitors as new cancer therapeutics : implications for clinical trial design", Onco Targets Ther, 9, pp.203~210 (2016)
- Liby TA, "Akt3 controls vascular endothelial growth factor secretion and angiogenesis in ovarian cancer cells", Int J Cancer, 130, pp.532~543 (2012)
- Burris HA, "Safety, pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity of the oral AKT inhibitor GSK-2141795 (GSK795) in a phase I first-in-human study", J Clin Oncol, 29(15 Suppl), pp.3003~ (2011)
- Shen DW, "Elevated expression of TMEM205, a hypothetical membrane protein, is associated with cisplatin resistance", J Cell Physiol, 225, pp.822~828 (2010)
- Wang Y, "The association of transporter genes polymorphisms and lung cancer chemotherapy response", PLoS One, 9, pp.e91967~ (2014)
- Liu Y, "TP53loss creates therapeutic vulnerability in colorectal cancer", Nature, 520, pp.697~701 (2015)
- Francavilla C, "Phosphoproteomics of primary cells reveals druggable kinase signatures in ovarian cancer", Cell Rep, 18, pp.3242~3256 (2017)
- Yoo SS, "Association between polymorphisms in microRNA target sites and survival in early-stage non-small cell lung cancer", Thorac Cancer, 8, pp.682~686 (2017)
