Expression analysis of murine primary and derived orthotopic SEOC tumors

We previously generated genetically engineered mouse (GEM) models based on perturbation of Tp53, Rb with or without Brca1 or Brca2 that develop serous epithelial ovarian cancer (SEOC) closely resembling the human disease on histologic and molecular levels. We have adapted these GEM models to orthotopic allografts that uniformly develop tumors with short latency in immunocompetent recipients and are ideally suited for routine preclinical studies. To monitor passaged tumors at the molecular level, we analyzed transcriptional profiles of a set of primary SEOC and matching derived passaged tumors. We have merged this dataset with previously published ( doi: 10.1158/0008-5472.CAN-11-3834; PMID 22617326) dataset of murine primary ovarian tumors from our GEM models (GSE46169) and merged and compared them to expression profiles of human dataset published previously (doi: 10.1038/nature10166). The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of effective treatment. Despite enormous effort to develop better targeted therapies, platinum-based chemotherapy still remains the standard of care for ovarian cancer patients, and resistance occurs at a high rate. One of the rate limiting factors for translation of new drug discoveries into clinical treatments has been the lack of suitable preclinical cancer models with high predictive value. We previously generated genetically engineered mouse (GEM) models based on perturbation of Tp53, Rb with or without Brca1 or Brca2 that develop serous epithelial ovarian cancer (SEOC) closely resembling the human disease on histologic and molecular levels. Here, we describe an adaptation of these GEM models to orthotopic allografts that uniformly develop tumors with short latency and are ideally suited for routine preclinical studies. RNA was isolated from flash frozen ovarian tumors using Trizol and Qiagen RNeasy columns