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Using Big Data to Understand Immune Cells and Cancer
Name: Taylor Weiskittel
Hometown: Knoxville, Tennessee
Graduate track: Molecular Pharmacology and Experimental Therapeutics
Research mentor: Hu Li, Ph.D., Mayo Clinic in Rochester
What biomedical issue did you address in your research, and what did your studies find?
My Ph.D. research used computer modeling to provide insight into which transcription factors — the proteins that control DNA activity — determine a cell's identity. Our goal was to understand why cells behave and appear the way they do, and which transcription factors cause cells to acquire new features.
Using bioinformatics, data mining, and machine learning approaches, I developed mathematical models to study transcription factors by examining the RNA in cell samples and single cells. We applied the approach to immune cells, looking at how the cells develop and differentiate, and ovarian cancer cells as they develop drug resistance. Our mathematical modeling characterized how a type of immune cells called NK cells form in our bodies and determined how it might be possible to grow more active NK cells in the lab as an immune therapy for cancer. We also identified several mechanisms that lead to drug resistance in ovarian cancer cells.
What aspects of Mayo's culture helped you grow as a scientist and as a thinker?
I am graduating as an M.D.-Ph.D. student from Mayo Clinic's Medical Scientist Training Program. In addition, I completed a thesis-based master's in bioinformatics and computational biology at the University of Minnesota, which allowed me to develop foundational skills as a computer and artificial intelligence scientist that are synergistic with the medical and molecular biology training I obtained from Mayo Clinic.
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Taylor Weiskittel (center)
traveling in Arizona with two other M.D.-Ph.D. studentsThe collaborative culture at Mayo enabled me to participate in numerous scientific projects. One significant project evolved through conversations with a close friend, an M.D.-Ph.D. student studying brain cancer. I used mathematical modeling to link brain cancer biology and genetics to details that can be read in an MRI. Our project had many collaborators in neuroradiology, cancer biology, neurosurgery, and medical oncology. Our findings, in Nature Communications, may help physicians understand how a tumor changes as it invades remote areas of the brain and may inform strategies to treat it. Mayo's research and education environment inspires and encourages teamwork, shaping my approach to advancing cancer care.
What's next?
I will begin a residency in radiation oncology, a specialty that will enable me to treat cancer patients with radiation therapy and to engage my interests in math, physics, and novel technologies. As a researcher, I am passionate about the rigorous, efficient, and innovative use of translational big data in biomedicine. I aim to be a leader in stewarding sound computer science principles and advancing the use of molecular big data for precision medicine.