Risk-on-a-Chip for Tailored Primary Prevention of Breast Cancers
Abstract
A major issue to effectively prevent breast cancer is that multiple risk factors work independently and/or in combination for disease onset; this synergism varies depending on an individual?s ancestry and lifestyle that create a very specific landscape for gene expression distinct for each organ under study. This situation poses a unique challenge to prevention, not only because of combinatorial effects of risk factors, but also because breast characteristics modulate the biological impact of risk factors. There is an urgent need for models of risk that enable scientists and clinicians to identify the mechanisms associated with the modulation of risk factors in the breast. Such models should use human cells and risk conditions that pertain to humans. We propose that a solution is to create risks-on-a-chip (ROC) that can be tailored to the risk(s) of interest and to individual backgrounds illustrated by specific gene expression patterns. Our project will address two challenges of interest to the Breast Cancer Research Program: (1) prevent breast cancer (primary prevention) and (2) identify determinants of breast cancer initiation, risk or susceptibility. To demonstrate the feasibility of the concept, we will focus on oxidative stress (OS) as a risk factor. The widespread condition of OS is considered carcinogenic. It results from small reactive molecules created in response to abnormal estrogen metabolism, certain diets or overweight situation, and alters the genome of the breast, thus helping cancer development. We will mimic OS by enabling controlled local production of reactive molecules in a three-dimensional cell culture system that mimics the breast ducts where cancer starts. We will also include breast stiffness as a modulator of risk. It is linked to mammographic density that has been associated with up to 30% of breast cancer cases. Stiffness of the mammary stroma as a risk factor per se is arguable, but it seems capable of modulating the epithelial response to OS given that this type of stress also modifies the stromal microenvironment with changes that can influence epithelial cells. We propose to mimic this situation in the laboratory by culturing fibroblasts and epithelial cells in a manner that reproduces their organization in the breast, using a matrix in which the density of collagen I fibers can be tuned to obtain different levels of stiffness. Matrix stiffness sensors and controlled production in the matrix of molecules leading to OS will be engineered so that risk levels are known at all times and the level of risk factor and risk modulator can be tailored to the response in the cells that participate in cancer development. The proposed model is unique because it is malleable and can be tailored to different groups of women at risk for breast cancer. So far only high risk associated with genetic mutations can be easily recognized, yet 95% of cancer cases are not linked to genetic mutations initially. Here we are focusing on OS, a feature important for all cancer onsets, but particularly so for triple-negative breast cancer. If the model includes breast density as a risk modulator, then it will benefit women with increased breast matrix stiffness; if it includes cells involved in inflammatory reaction linked to being overweight, then it will benefit women in that situation, etc. There are several applications from the ROC investigation for the clinics. It can help identify the determinants of cancer onset associated with combinations of risks, which should help identify better markers of risk to screen a population. This aspect of the work is a few years away from benefitting the population since markers will have to be validated in clinical trials once identified. The ROC can also be used immediately, once tested, to screen natural and manufactured compounds for their protective effect on cancer onset depending on the risk level in the breast. In the current project, we propos
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 07, 2017
- Source ID
- W81XWH1710250
Entities
People
- Sophie Lelievre
Organizations
- United States Army
- University of Virginia