Endometrial cancer is the most common malignancy of the female reproductive tract and the fourth most common cancer in women, and its incidence is increasing. Although surgery is effective in early stage cancers, the median survival of women with advanced endometrial cancer is less than one year, and in the past 30 years there have been no major improvements in the treatment of advanced disease. Neither chemotherapy nor radiation therapy significantly increases long?term survival, and there are no therapies (targeted or conventional) effective against metastatic disease. Endometrial cancer research has lagged behind other cancers in grant allocation and progress relative to its clinical impact. More recently, systematic genome?wide sequencing efforts, such as the National Cancer Institute?s The Cancer Genome Atlas (TCGA), have brought the endometrial cancer genetic landscape into much clearer focus. These studies have yielded many previously unsuspected endometrial cancer tumor suppressor genes (such as FBXW7) altered in a large percentage of cases, and thus of obvious clinical and research significance. Cancers are complex amalgamations of various cell types that interact in myriad ways not only with one another, but also with the host organism. Hence, many features of cancer cannot be modelled in vitro. Consequently, in vivo genetic animal models are needed to further our understanding of these newly?discovered genetic driver events in endometrial cancer. Genetically?engineered mouse models of endometrial cancer have lagged considerably relative to other types of cancer, and such models remain to be generated for most of the frequent endometrial cancer driver events. One significant limitation has been the lack of fully optimized in vivo genetic models and the genetic tools to generate such models. We propose to build upon prior efforts and employ a general, validated strategy to build a suite of genetic tools useful for the generation and characterization of diverse mouse models of invasive and lethal endometrial cancer. The wide availability of this fully?credentialed suite of genetic tools will bring new investigators into the field and greatly catalyze the development and use of clinically?relevant mouse models of endometrial cancer. These models and the diverse array of translational investigations they will foster will lead to new insights into the biology of these endometrial cancer driver genes and also aid in the identification of potential Achilles? heels that can be exploited for treatment.

Public Health Relevance

Cancer of the endometrium (the inner lining of the uterus) is the most common cancer of the female reproductive tract. However, relatively little is known about the steps that promote the formation of malignant and lethal endometrial cancers. In this project we propose to develop and utilize new genetic animal model systems to understand these critical steps and their impact on tumor development. These models will also accelerate efforts to test and validate new therapies to treat endometrial cancer. These studies will thus lead to insights into the biological and genetic basis of endometrial cancer, create significant opportunities to develop improved diagnostic tests, and may some day lead to the development of improved, targeted therapies to treat or prevent endometrial cancer formation and spread..

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA211339-03
Application #
9646342
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sathyamoorthy, Neeraja
Project Start
2017-03-03
Project End
2021-02-28
Budget Start
2019-03-01
Budget End
2021-02-28
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Texas Sw Medical Center Dallas
Department
Pathology
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Li, Hao-Dong; Cuevas, Ileana; Zhang, Musi et al. (2018) Polymerase-mediated ultramutagenesis in mice produces diverse cancers with high mutational load. J Clin Invest 128:4179-4191