Assistant Professor, Medicine and Genetics
Stanford University, School of Medicine
Tumors are composed of a mixture of cells that are genetically unique and have different properties. This intra-tumor complexity poses numerous clinical challenges, including drug resistance. Although drug resistance represents a major cause of breast cancer mortality, the underlying mechanisms remain poorly understood. Dr. Curtis has developed an innovative mathematical model that uses genomic profiles of individual tumor cells to simulate tumor dynamics in response to various drugs. This model can provide information on patient-specific tumor response, which cannot be measured directly in humans. By applying this approach to human tumor samples collected before and after treatment, the researchers can identify the genomic drivers, as well as molecular markers of drug resistance. This strategy is yielding new insights into mechanisms of resistance and will lead to the development of more effective patient-tailored combinations to prevent resistance and disease progression.
Dr. Curtis is an Assistant Professor of Medicine and Genetics in the School of Medicine at Stanford University where she leads the Cancer Systems Biology Group and serves as Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. She received her doctorate in Molecular and Computational Biology in 2007 and completed a postdoctoral fellowship in Computational Biology at the University of Cambridge in 2010. Dr. Curtis was the recipient of several young investigator awards, including the 2012 V Foundation for Cancer, V Scholar Award and the 2012 STOP Cancer Research Career Development Award.
Dr. Curtis’s laboratory pursues innovative experimental approaches and data-driven modeling to address outstanding questions in cancer systems biology. In particular, her research seeks to delineate mechanisms of tumor progression and therapeutic resistance. For example, she and her team have developed an experimental and computational framework to interrogate tumor evolutionary dynamics and the timeline of neoplastic progression. They are also developing approaches to model therapeutic resistance. By coupling this approach with high-resolution genomic profiling of patient samples, this research will enable a paradigm shift in patient stratification and will ultimately inform optimal treatment strategies.
Another aspect of her research has focused on the integration of diverse genomic data types to elucidate inter-individual variation and mechanisms of tumorigenesis. For example, she lead a seminal study that redefined the molecular map of breast cancer through a detailed characterization of the genomic and transcriptomic landscape of 2,000 breast cancers. Using integrative genomics and statistical approaches, this work identified novel subtypes of breast cancer with distinct clinical outcomes and subtype-specific driver genes. Ongoing efforts in this area will guide the development of novel targeted therapeutics and improved prognostic signatures.