Christina Curtis, PhD
Keck School of Medicine
Norris Comprehensive Cancer Center
University of Southern California
Los Angeles, California
2013-2014 BCRF Project:
(The ULTA Beauty Award)
Co-Investigator: Sir Bruce A. J. Ponder, PhD, FRCP, FRS, University of Cambridge, Cancer Research UK, Cambridge, United Kingdom
Dr. Curtis’s research focuses on mechanisms of resistance to treatment. She has profiled breast cancer patient samples at extremely high-resolution and is applying her analytical framework to quantify the extent of intra-tumor heterogeneity and to delineate the ordering of mutational events. BCRF funds have been used to perform high-resolution genomic profiling, including whole exome sequencing and ultra-deep bisulfite sequencing of breast cancer patient specimens. The measurement of these and other clinically relevant tumor parameters will then be tested for their association with various patient outcomes to provide insight into mechanisms of disease progression.
It is now appreciated that tumors are composed of a heterogeneous mixture of cells that are genetically distinct and possess different properties. However, the extent of this intra-tumor heterogeneity has been underestimated due to technological limitations, and has yet to be systematically assessed in breast cancer. A complicating factor is that subpopulations of cancer cells might inherently contain molecular features that render them resistant to therapy, as is believed to be the case for cancer stem cells. In response to treatment selective pressures, these resistant cells out-grow the sensitive cells, leading to clinical resistance and disease recurrence. This represents a critical area of research since resistance is the ultimate cause of treatment failure and mortality, and will only be addressed when resistant subclones are successfully targeted. Tumor progression and therapeutic resistance are inherently evolutionary processes, and new approaches to study the dynamics of tumor cell populations are needed. Another major challenge is that mechanisms of drug resistance remain poorly characterized because the laboratory models used to develop new treatments do not accurately reflect the complexity of the human system, and hence fail to predict the efficacy of therapy in human tumors. The identification of the true drivers of resistance in human tumors will enable the development of more effective patient-tailored combinations that circumvent resistance and subsequent disease progression. Dr. Curtis and colleagues have developed a pioneering approach that overcomes these limitations, by utilizing the genomic profiles encoded in individual tumor cells, computational models of tumor dynamics and statistical inference to study mechanisms of therapeutic resistance. This framework enables the simulation of tumor growth in response to various therapeutic interventions, as well as the quantification of patient-specific tumor parameters. Hence, this approach can ultimately help to predict optimal treatment strategies for individual patients.
By applying this novel approach to breast tumor samples from patients receiving targeted therapy, Dr. Curtis will address several pressing clinical questions. In particular, this approach will enable the unbiased identification of the true drivers of resistance, which can subsequently be targeted in combination therapy to circumvent resistance. Her team will also model the effects of known and novel driver mutations on tumor growth, and the impact of different therapies. Moreover, through the identification of predictive biomarkers of resistance, this approach can ultimately be used to inform patient-tailored therapeutic strategies.
Dr. Curtis is an Assistant Professor of Preventive Medicine at the Keck School of Medicine of the University of Southern California and a member of the Norris Comprehensive Cancer Center, where she leads the Cancer Systems Biology Group. She obtained her bachelor of science degree from the University of California, Los Angeles in 2001 and went on to obtain a master’s of science degree in Molecular Biology from the University of Heidelberg, Germany and another master’s of science degree in Bioinformatics from the University of Southern California. She received her doctorate in Molecular and Computational Biology in 2007 and completed a postdoctoral fellowship in Computational Biology at the University of Cambridge before assuming her current position in 2010.
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 leads 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 targeted therapeutics and improved prognostic signatures.
Dr. Curtis was the recipient of the 2009 American Association of Cancer Research Scholar in Training Award, the 2012 STOP CANCER Research Career Development Award, and the 2012 V Foundation for Cancer, V Scholar Award.