| Christina Curtis | |
| Workplaces: | Stanford University University of Cambridge |
| Alma Mater: | University of California, Los Angeles Heidelberg University University of Southern California |
| Thesis Title: | Analysis of high-density oligonucleotide gene expression data for dissecting aging pathways |
| Thesis Url: | https://www.proquest.com/docview/304825898 |
| Fields: | Cancer genomics Tumor evolution Computational biology Early detection |
| Doctoral Advisor: | Simon Tavaré |
| Thesis Year: | 2007 |
Christina Curtis is an American scientist who is a Professor of Medicine, Genetics and Biomedical Data Science and an Endowed Scholar at Stanford University where her research investigates the evolution of tumors. She is director of Artificial Intelligence and Cancer Genomics at Stanford University School of Medicine and is on the board of directors of the American Association for Cancer Research.
Curtis decided that she wanted to work on cancer treatments when she was a teenager.[1] She was an undergraduate student at the University of California, Los Angeles and did a masters degree at Heidelberg University.[2] She moved to the University of Southern California for graduate studies, where she earned both a master's and a doctoral degree.[3] She completed her PhD in molecular and computational biology in 2007 supervised by Simon Tavaré.[4]
Curtis was a postdoctoral researcher at the University of Cambridge, where she spent three years before returning to the faculty at the University of Southern California.
Curtis has leveraged computational modeling to better understand breast cancer, providing insight into the evolution and metastasization of tumors.[5] She established the Cancer Computational and Systems Biology group.
Curtis uses computer simulations to understand genetic mutations in tumor samples.[6] She believes that breast cancer tumors have genetic differences that respond differently to treatments. In 2019, she combined molecular analysis and historical clinical data to create the largest breast cancer cohort. In this cohort she found four groups of tumors that occur later in life, up to 20 years after the initial cancer diagnosis. She also found a subset of breast cancer tumors that do not recur after five years.[7] To this end, Curtis believes that tumors with metastatic potential have this from the start – they are "born to be bad".
In 2022, Curtis was appointed director of Artificial Intelligence and Cancer Genomics at the Stanford Cancer Institute.