Dr. Christina Curtis, PhD, MSc is an Endowed Professor of Medicine and Genetics at Stanford University where she leads the Cancer Computational and Systems Biology group. Dr. Curtis also serves as the Director of Breast Cancer Translational Research and Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. Dr. Curtis’s laboratory leverages computational modeling, high-throughput molecular profiling and experimentation to develop new ways to prevent, diagnose and treat cancer. Her research has helped to redefine the molecular map of breast cancer and led to new paradigms in understanding how human tumors evolve and metastasize. Dr. Curtis is the recipient of numerous awards, including those from the V Foundation for Cancer Research, STOP Cancer and the American Association for Cancer Research (AACR). She received the National Institutes of Health Director's Pioneer Award in 2018, the Stanford Prize in Population Genetics and Society (2020) and was named an In vivo Rising Leader in the Life Sciences (2021) and the Julius B. Kahn Visiting Professor in the Dept of Pharmacology, at Northwestern University (2020). In 2022 she received the AACR Award for Outstanding Achievement in Basic Science. Dr. Curtis is also Kavli Fellow of the National Academy of Sciences, a Susan G. Komen Scholar and a Chan Zuckerberg Biohub Investigator. Dr. Curtis serves as a scientific advisor to multiple academic institutes and biotech as is a member of the AACR Board of Directors. She also serves on the editorial board of journals spanning computational biology to precision oncology.
The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer. They are particularly interested in elucidating tumor evolutionary dynamics, novel therapeutic targets, and the genotype to phenotype map in cancer. A unifying theme of the lab's research is to exploit ‘omic’ data derived from clinically annotated samples in robust computational frameworks coupled with iterative experimental validation in order to advance our understanding of cancer systems biology. In particular, they employ advanced genomic techniques, computational and mathematical modeling, and powerful model systems in order to:
- Model the evolutionary dynamics of tumor progression and therapeutic resistance and metastasis
- Elucidate disease etiology and novel molecular targets through integrative analyses of high-throughput omic data
- Develop techniques for the systems-level interpretation of genotype-phenotype associations in cancer
The Curtis lab's research is funded by the NIH/NCI, NHGRI, Department of Defense, Breast Cancer Research Foundation, American Association for Cancer Research, Susan G. Komen Foundation, Emerson Collective and V Foundation for Cancer Research.