People diagnosed with triple negative breast cancer (TNBC) face poor outcomes due to the limited number of effective treatment options. Chemotherapy is the most common way to treat TNBC; however, many people develop resistance or stop responding to the treatment, which can lead to a tumor recurrence (return). As Assistant Professor of Pathology and Laboratory Medicine at the University of Pennsylvania, Dr. Faryabi will use cutting-edge science to identify new treatment strategies for people with TNBC that could improve survival by limiting treatment resistance and tumor recurrence.
Dr. Faryabi’s work focuses on a protein called Notch which is often turned ‘on’ in TNBC, leading to tumor growth and chemotherapy resistance. Although drugs that block Notch exist, they have not been well tested in the treatment of TNBC, in part due to limited understanding of how these drugs work. With Komen funding, Dr. Faryabi will study how these drugs may turn on crucial genes that affect TNBC growth. He will use big data approaches - the latest in epigenomics, gene editing, 3D genome organization, and machine learning - to investigate factors beyond the gene themselves to determine how these factors affect tumor growth. This information should not only reveal how well Notch drugs stop tumor growth but should also help us understand how some tumor cells are able to survive the effects of the drug.
Through this work, Dr. Faryabi hopes to understand how Notch contributes to TNBC and identify proteins that work with Notch to help TNBC grow and spread so that he can find ways to reverse drug-resistance. This information will provide a big picture view of how TNBC can be successfully treated.
- Dr. Faryabi attended college in Iran, where he was first introduced to the use of computers to advance science.
- He worked in the southern part of Iran and in Texas, exploring ways to make renewable plastics.
- Dr. Faryabi uses Software programs called inteGREAT and HeatleTup in his research.
- He is also trying to find better ways to treat acute myeloid leukemia using Artificial Intelligence.