Departmental Affiliation and City, State, Zip for All Authors

Jin Liu, Department of Pharmaceutical Sciences, Fort Worth, Texas, 76107; Hamed Hayatshahi, Department of Pharmaceutical Sciences, Fort Worth, Texas, 76107; Amin Morid, Department of Pharmaceutical Sciences, Fort Worth, Texas, 76107; Kenneth Fluker Jr., Alabama State University, Montgomery, Alabama, 36104; Charlene Radler, UNTHSC, Fort Worth, Texas, 76107

Scientific Abstract

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer. It cannot be treated with general breast cancer treatments because it does not express HER2, estrogen, and progesterone receptors. TNBC affects a wide variety of people, but African American women are the most susceptible to this form of cancer. The aim of our project is to identify key contributing genes of TNBC and use them to predict outcomes of TNBC. In order to accomplish this, we have to download patient data from the GDC database and create data sets using that data. In total, there are four data sets focusing on gene expression based on the three receptors that TNBC does not express, and the race of the patient (Either white or African American). Once the data sets have been created, we will use them to generate models that can predict the key contributing genes of TNBC. These models will be use to research possible ways to develop targetable treatments for TNBC patients.

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DATASET PREPARATION AND MODELING FOR PATIENTS WITH TRIPLE-NEGATIVE BREAST CANCER

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer. It cannot be treated with general breast cancer treatments because it does not express HER2, estrogen, and progesterone receptors. TNBC affects a wide variety of people, but African American women are the most susceptible to this form of cancer. The aim of our project is to identify key contributing genes of TNBC and use them to predict outcomes of TNBC. In order to accomplish this, we have to download patient data from the GDC database and create data sets using that data. In total, there are four data sets focusing on gene expression based on the three receptors that TNBC does not express, and the race of the patient (Either white or African American). Once the data sets have been created, we will use them to generate models that can predict the key contributing genes of TNBC. These models will be use to research possible ways to develop targetable treatments for TNBC patients.

Manuscript Number

1044