Decoding the Mechanoregulation of Breast Tumor Organoid Invasion, One Cell at a Time
Abstract
Despite recent advances, breast cancer remains the most common cancer among women and the second leading cause of female cancer deaths. Most breast cancer patients die from metastasis when cancer cells migrate through the body and seed new tumors. Cancer metastasis consists of multiple and poorly understood steps. One of the first steps in metastasis is cell migration and invasion: cancer cells that are detached from the primary tumor invade and migrate through the surrounding tissue. Recent experiments have shown that migrating breast cancer cells can switch between different modes of migration, especially when they encounter different extracellular environments. This so-called plasticity in migration mode is currently poorly understood. Importantly, it has impeded the development of cancer therapies that target metastasis: a drug that would affect one migration mode general does not work on other modes. This also explains why previous clinical trials, which targeted matrix metalloproteinase-dependent migration, failed to stop cancer metastasis. In this proposal, we propose to study, using both quantitative experiments and computational modeling, the causes and consequences of cancer cell migration mode transitions, addressing the overarching challenge why some breast cancers become metastatic. Our research will lead to new metrics to improve breast cancer risk assessment and will help establish a comprehensive strategy to effectively treat the metastatic disease. We hypothesize that bidirectional and mechanical interactions between the tumor and its extracellular environment regulate the migration mode switching of breast cancer cells, which ultimately determines the metastatic potential of breast tumors. To test this hypothesis, we will carry out experiments using tumor organoids, which have recently emerged as useful preclinical tools. We will use organoids from a panel of 18 breast cancer cell lines representing different breast cancer subtypes and will embed them in networks of collagen fibers. We will systematically alter the rigidity, pore size, and alignment of the collagen fiber networks, to simulate the diverse extracellular environment in patients. For each set of conditions, we will measure micromechanics, a novel metric to characterize tissue stiffness at cell-relevant scale, of the extracellular matrix. We will employ machine learning techniques to determine the migration mode and mode switching of tumor cells, so that we can quantify how the properties of the extracellular environment are coupled to metastasis. In addition, we will conduct experiments in which we systematically vary the geometry of the organoids, ranging from spherical to pyramidal. These experiments are motivated by both the diverse shapes of breast tumors observed in clinics and by our preliminary results, which show that tissue remodeling by breast tumor organoids depends on the organoid geometry. Using deep learning-based cell tracking and classification software we developed, along with measurements of the tissue stiffness at cell-relevant scale, we will determine how tumor geometry affects migration, modes of migration, and invasiveness. These quantitative experiments will give us a clear and essential picture of how the extracellular matrix dictates cell migration modes and how cells can remodel their environment. The experimental results will be incorporated into an integrated mathematical model that can simulate a fully three-dimensional cell. Such a model is currently not available and will be a novel tool that can be used to test potential migration and metastasis mechanisms. In addition, it can be used to generate predictions, including optimized therapeutic strategies based on individual patient’s cancer cell motility and extracellular matrix properties. Finally, cancer researchers will be able to extend this model to address other steps in the metastatic process of breast cancer. As a Breakthrough
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010444
Entities
People
- Bo Sun
Organizations
- Oregon State University
- United States Army