Breast Pre-Cancer Atlas
Overview
The Breast Pre-Cancer Atlas Center is designed to construct an atlas that can be used to better understand ductal carcinoma in situ (DCIS), a preinvasive breast cancer precursor. DCIS is an extremely common clinical diagnosis that is essentially a disease of screening triggered by the detection of abnormal breast calcifications on mammography. Before the advent of mammography, DCIS was an incidental and relatively uncommon finding. Over 60,000 women in the United States will be presented with this diagnosis each year with relatively weak evidence-based guidance for disease management, which ranges from active surveillance to bilateral mastectomy. We propose to compile multi-dimensional and multi-scale information on DCIS to construct a Pre-Cancer Atlas that can be used to better understand the disease but also to better stratify risk of progression, a useful translational endpoint.
To do this, we have assembled a team of investigators with deep and complementary clinical, experimental, and quantitative expertise and experience with DCIS and breast cancer in general. Further, we conduct these studies with full consideration of tumor evolution and ecology as it pertains to precancer development and progression. Specific aspects of the proposed atlas construction include: 1) several types of DCIS cohorts that will capture spatial and longitudinal information including a prospective clinical trial cohort undergoing active surveillance; 2) analyses designed to maintain relevant spatial organization of the disease for evolutionary and atlas building considerations based on 3) radiologic-histologic-cellular-molecular registration approaches; 4) characterization at multiple scales including whole-tumor, single-duct, and single-cell levels; 5) characterization of relevant parameters including mutations, copy number changes, methylation, gene expression, and microenvironmental elements including inflammatory cell profiles; 6) incorporation of the breast cancer-intrinsic subtype paradigm into the analytic phase; and 7) layered, spatial, and longitudinal data visualization. Overall, this work will provide a comprehensive platform to guide the next generation of studies on DCIS and other precancers.
Principal Investigators
E. Shelley Hwang, MD, MPH
Dr. E. Shelley Hwang is the Mary and Deryl Hart Professor of Surgery at Duke University School of Medicine and Chief of Breast Surgery for the Duke Cancer Institute, an NIH-designated Comprehensive Cancer Center. Her research interests have spanned a broad range of projects in translational research that seek to elaborate biomarkers in the tumor, the microenvironment, and blood. Dr. Hwang is a surgeon-scientist and experienced clinical trial investigator with an over 15-year interest in both the biology and treatment of early-stage breast cancer. She also is the PI of a national cooperative group study through the ALLIANCE, known as the COMET study, which evaluates the role of active surveillance compared to usual care for DCIS.
Rob West, MD, PhD
Dr. Rob West is a Professor of Pathology at Stanford University Medical Center. He is a clinician scientist with experience in translational genomics research to identify new prognostic and therapeutic markers in cancer. His research focus is on the progression of neoplasia to invasive carcinoma. His lab has developed spatially oriented in situ methods to study archival specimens. In addition to running a research laboratory, Dr. West also serves as a surgical pathologist specializing in breast pathology at Stanford University Hospital.
Carlo Maley, MD, MSc
Dr. Carlo Maley is Associate Professor of the Biodesign Institute at the Arizona State University School of Life Sciences. He is a cancer biologist, evolutionary biologist, and computational biologist working at the intersection of those fields. His team applies evolutionary and ecological theory to three problems in cancer: (1) neoplastic progression: the evolutionary dynamics among cells of a tumor that drive progression from normal tissue to malignant cancers; (2) acquired therapeutic resistance: the evolutionary dynamics by which our therapies select for resistance and we fail to cure cancer; and (3) the evolution of cancer suppression mechanisms in large, long-lived animals like elephants and whales (a problem called Peto’s Paradox). Dr. Maley’s lab uses genomic data mining, phylogenetics, computational modeling, as well as wet lab techniques to solve these problems. In all of this work, their goals are to develop better methods to prevent cancer and improve cancer management.