- Elucidating Tumor Evolutionary Patterns using High-Depth Molecular Data
- Project Year:
2018
- REU Student (s):
Caitlin Guccione | University of Rhode Island RI
- Student 1 Institution:
University of Rhode Island
- Project Mentor:
Hossein Khiabanian
- Project Mentor Area:
Rutgers Cancer Institute of New Jersey
- Project Abstract:
Cancer is the second leading cause of death in the United States and yet it only has two main treatments, radiation and chemotherapy. A more efficient way to eliminate cancerous cells is with a targeted approach. In order to create more effective precision medication, there exists a
need to understand how cancer develops and and to determine which cancerous mutations are most
frequent in patients. The optimal way to answer these questions is by sequencing cancer tumors and
tracking mutations over time with the help of mathematical trees. We use two genetic distances,
Hamming and Nei to help structure the trees. We conclude that Nei's distance does a better job of
accurately reflecting the changes in mutations over time and thus can be used in the future to track
the evolution of cancerous cells.
