Christina Curtis, PhD, MSc
Assistant Professor, Medicine and Genetics
Endowed, Faculty Scholar, Stanford University
Co-director, Molecular Tumor Board, Stanford Cancer Institute
Director, Breast Cancer Translational Research, Stanford Cancer Institute
Stanford University, School of Medicine
Understanding the drivers of breast cancer recurrence and metastasis in order to develop more effective treatments for patients with metastatic estrogen receptor-positive breast cancer.
Estrogen receptor (ER)-positive breast cancer is the most common type of breast cancer and can be treated with ER-directed therapies. Unfortunately, while most women with ER-positive breast cancer respond well, a substantial number experience recurrence at distant sites such as the lungs, brain, liver, or bones. This process, called metastasis, is the major cause of breast cancer mortality and can occur more than five years after the initial diagnosis. When metastasis occurs, current therapies can palliate symptoms and prolong survival, but metastatic breast cancer is not curable. Therefore, preventing and treating metastasis in ER-positive breast cancer remains a major unmet medical need. Dr. Curtis and her colleagues are investigating the underlying molecular drivers of recurrence and metastasis. Recently, they identified several subgroups of patients with ER-positive breast cancer who have a high-risk of relapse up to 20 years post diagnosis. These high-risk patients collectively account for one quarter of all ER-positive breast cancers and the majority of recurrences. Dr. Curtis’ team is seeking unique tumor targets within this group of patients that may be therapeutically actionable. Her results will help inform strategies for personalized breast cancer treatment and risk prediction with the goal of improving outcomes for women diagnosed with breast cancer witha high-risk of recurrence.
Dr. Curtis and her colleagues have developed and characterized patient-derived model systems which represent four of the high-risk ER-positive/HER2-negative breast cancer subgroups. These models provide unique tools that capture the heterogeneity of patient tumors and the underlying molecular drivers. Her team also used a newly developed machine learning approach and found that there is a prevalence of these four subgroups in patients with breats cancer metastasis compared to those with early stage disease breast cancer.
The team will continue to determine whether there is any association with the high-risk groups they identified and treatment response. Building on their results, they will seek to develop novel ways to prevent recurrence in high-risk patient populations helping to deliver the right drug to the right patient at the right time.
“If not for BCRF, I would not be translating findings made at the bench and computer to the clinic to enable more precise therapeutic approaches for breast cancer patients.” - Dr. Curtis
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 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 novel targeted therapeutics and improved prognostic signatures.
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