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Purpose: Aurora kinase A (Aurora-A) is known to regulate genomic instability and tumorigenesis in multiple human cancers. The underlying mechanism, however, is not fully understood. We examined the molecular mechanism of Aurora-A regulation in human ovarian cancer.Experimental Design: Retrovirus-mediated small hairpin RNA (shRNA) was used to silence the expression of Aurora-A in the ovarian cancer cell lines SKOV3, OVCA432, and OVCA433. Immunofluorescence, Western blotting, flow cytometry, cytogenetic analysis, and animal assay were used to test centrosome amplification, cell cycle alteration, apoptosis, DNA damage response, tumor growth, and genomic instability. Immunostaining of BRCA2 and Aurora-A was done in ovarian, pancreatic, breast, and colon cancer samples.Results: Knockdown of Aurora-A reduced centrosome amplification, malformation of mitotic spindles, and chromosome aberration, leading to decreased tumor growth. Silencing Aurora-A attenuated cell cycle progression and enhanced apoptosis and DNA damage response by restoring p21, pRb, and BRCA2 expression. Aurora-A was inversely correlated with BRCA2 in high-grade ovarian serous carcinoma, breast cancer, and pancreatic cancer. In high-grade ovarian serous carcinoma, positive expression of BRCA2 predicted increased overall and disease-free survival, whereas positive expression of Aurora-A predicted poor overall and disease-free survival (P < 0.05). Moreover, an increased Aurora-A to BRCA2 expression ratio predicted poor overall survival (P = 0.047) compared with a decreased Aurora-A to BRCA2 expression ratio.Conclusion: Aurora-A regulates genomic instability and tumorigenesis through cell cycle dysregulation and BRCA2 suppression. The negative correlation between Aurora-A and BRCA2 exists in multiple cancers, whereas the expression ratio of Aurora-A to BRCA2 predicts ovarian cancer patient outcome. Clin Cancer Res; 16(12); 3171–81. ©2010 AACR.

Ovarian cancer is the most lethal gynecological malignancy. The majority of ovarian cancer patients are diagnosed at an advanced stage of disease, and the lack of good early screening and diagnostic tests is one of the reasons for the low overall survival rate. Moreover, there is no good marker to predict patients' response to chemotherapy. Patient-derived xenografts (PDXs) have become the most promising preclinical model for gynecologic malignances. We have successfully established 22 PDX models derived from ovarian cancer patients' surgical specimens implanted into severe combined immunodeficient (SCID) mice, non-obese diabetic (NOD)-SCID or Nude mice. The majority of the PDX models were generated from patients' samples diagnosed with high-grade serous carcinoma including 3 with BRCA1/2 mutations, as well as less common types of ovarian cancers such as low-grade serous, endometrioid, mucinous carcinomas, and carcinosarcoma. PDXs have been established from solid masses as well as from ascites. We have optimized protocol for establishing ovarian PDXs including implantation and collection methods, tumor type (solid versus ascites), and type of mouse strains used. Using SCID mice, our success rate exceeded 80% and recent data indicated that the success rate in NOD/SCID mice was higher. The link between the pathology and PDX core, the “triage protocol” and ongoing PARP inhibitor trials will provide an important array of PDXs including pre- and post-treatment PDXs for exploration for biomarkers as well as for co-clinical trials to validate the utility of rational drug combinations. All models were validated using short tandem repeats (STR) and compared to the H&E and immunohistochemistry stains from the matching patient's tissue. We also examined the expression of B7-H4, a novel member of the B7 family immune checkpoint regulators, in 9 patient-derived xenografts (PDXs) of high-grade ovarian serous carcinoma. Our data showed that B7-H4 proteins were expressed in 8 of 9 PDX models of high-grade serous carcinoma, suggesting that PDXs may be useful for studying immunotherapy. Citation Format: Li Liang, Imelda Mercado-Uribe, Na Niu, Yi Jiang, Wenjun Cheng, Russell Broaddus, Robert Bast, Gordon Mills, Anil K. Sood, Jinsong Liu. Establishment of Patient-Derived Xenograft (PDX) Models of Ovarian Cancer. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr B22.