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 made significant strides in these efforts through several lines of research. In particular, her team is working to characterize the intrinsic molecular features that cause some breast tumor cells to be resistant to chemotherapeutic agents and/or targeted therapies. She is also hoping to better understand how populations of cancer cells evolve under the selective pressure imposed by therapy, leading to the outgrowth of resistant cells, followed by clinical resistance and disease recurrence. Towards these goals, Dr. Curtis is combining advanced genomic and computational techniques to analyze existing breast cancer datasets as well as longitudinal breast tumor samples taken before, during, and after the course of therapy. 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. They have already demonstrated the power of this novel framework to measure patient-specific properties of individual tumors such as the extent of heterogeneity, the mutation rate and mutational timeline, revealing quantitative insights into mechanisms of tumor progression. In the last year, Dr. Curtis has achieved several milestones in this work including the identification of candidate biomarkers and a potentially widespread mechanism of resistance to a commonly used type of chemotherapy, and with her colleagues is in the process of validating these finding in independent patient cohorts. In the coming year, they plan to further extend these approaches to develop and test predictive signatures to enable improved patient-stratification, thereby sparing patients ineffective therapy, while informing the development of patient-tailored treatment strategies.
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.