1. Understanding molecular mechanisms underlying breast cancer risk due to breast density. 

Patients with “dense” breast tissue have a four- to six-fold increased risk of developing breast carcinomas.  In fact, 1/3 of all breast cancer cases are attributed to breast density, making it one of the greatest risk factors for carcinoma.  Increased breast density is associated with a significant increase in the deposition of connective tissue, or ECM components, most notably the protein, collagen.  We have been developing model systems to understand why increased breast density results in an increased risk for developing breast carcinoma.  We find in a simple in vitro model that increasing the density of collagen in the matrix is sufficient to disrupt breast epithelial differentiation, suggesting that matrix density is itself an important regulator of cellular behavior.  Additionally, we are employing a mouse strain engineered to have more collagen in its connective tissue.  We find evidence for a collagen “signature” that is present in even before a tumor is palpable, predicting where a tumor will soon arise.  We are investigating whether this signature can be developed as a tool to aid in diagnosing human breast carcinoma at an earlier stage.

2. Molecular signaling events related to cell interactions with the ECM. 

Cells interact with the ECM through a variety of cell surface receptors, the best understood of which are members of the integrin family. Much remains to be determined regarding the specific molecular players and signaling pathways downstream of integrins, and how these pathways are involved in the progression of various diseases. Therefore, part of the focus of the lab is to investigate signaling events through the integrin family of receptors. A second aspect of this work is to investigate how small GTPases of the Ras superfamily, some of which are known or suspected oncogenes, affect the response of cells to the ECM. Specifically, we have focused on R-Ras and Rho, which we find alter the way breast epithelial cells respond to the ECM, promoting cellular migration and invasion. We are particularly interested in studying signaling events using state of the art imaging approaches at LOCI to understand how small GTPases function in a spatial and temporal manner during cell migration.

3. Intravital Imaging to recognize cell-cell and cell-matrix interactions in a native environment

Our ability to understand cancer has been significantly enhanced by the techniques of multiphoton fluorescence excitation microscopy (MPM) and second harmonic generation imaging (SHG).  Applying these techniques using the rodent mammary imaging window allows us to study tumor formation, progression, and metastasis within single animals. Through the use of animal models that express extrinsic flourophores and the characterization of endogenous fluorescence we are able to identify specific cell types and examine the interplay between the immune system and cancer. The use of ported imaging windows allows access to the tumor microenvironment in vivo to characterize the collagen structure in normal glands and around tumors.  These procedures helps us better understand the physical relationship between cells and the collagen fibers found during breast cancer progression. 

4. Metabolomics in a dense environment

Changes in cellular metabolism are a hallmark of cancer development and progression. Cancer cells alter their utilization of different fuels throughout the process of tumorigenesis and metastasis in order to meet the energetic and anabolic demands of the growing tumor. These changes in cellular metabolism may be a result of the extracellular microenvironment in which the cancer cells are growing.  We utilize a variety of techniques including gene microarray analysis, proteomics, metabolomics and intra and extracellular flux analysis to study the role of collagen ECM in determining cancer cell metabolism.  We also utilize the small animal imaging center on campus to evaluate changes in tumor metabolism via FDG-PET in our various mouse models. By better understanding the changes in cellular metabolism in response to different tumor microenvironments, we hope to identify new diagnostic and therapeutic metabolic targets for identifying and treating cancer in patients.