<div><p>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 <i>Tp53</i> and <i>Rb</i> with or without <i>Brca1</i> or <i>Brca2</i> that develop serous epithelial ovarian cancer (SEOC) closely resembling the human dise...
Despite increasing evidence that precision therapy targeted to the molecular drivers of a cancer has...
Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical sta...
Ovarian cancer is the most common cause of death from gynecological cancer. Understanding the biolog...
The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of e...
Serous epithelial ovarian cancer (SEOC) is the most lethal gynecological cancer in the United States...
For over five decades epithelial ovarian cancer (EOC) is identified as the leading cause of death fr...
Abstract The development of genetically engineered models (GEM) of epithelial ovarian ...
Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model hum...
PURPOSE: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Purpose: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model hum...
Purpose: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical sta...
Ovarian cancer is the most common cause of death from gynecological cancer. Understanding the biolog...
<div><p>Purpose</p><p>Preclinical models of epithelial ovarian cancer have not been exploited to eva...
Despite increasing evidence that precision therapy targeted to the molecular drivers of a cancer has...
Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical sta...
Ovarian cancer is the most common cause of death from gynecological cancer. Understanding the biolog...
The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of e...
Serous epithelial ovarian cancer (SEOC) is the most lethal gynecological cancer in the United States...
For over five decades epithelial ovarian cancer (EOC) is identified as the leading cause of death fr...
Abstract The development of genetically engineered models (GEM) of epithelial ovarian ...
Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model hum...
PURPOSE: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Purpose: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model hum...
Purpose: Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the cli...
Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical sta...
Ovarian cancer is the most common cause of death from gynecological cancer. Understanding the biolog...
<div><p>Purpose</p><p>Preclinical models of epithelial ovarian cancer have not been exploited to eva...
Despite increasing evidence that precision therapy targeted to the molecular drivers of a cancer has...
Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical sta...
Ovarian cancer is the most common cause of death from gynecological cancer. Understanding the biolog...