Molecular and Imaging Biomarkers for Precision Therapy of Breast Cancer
Abstract
Mammography is an x-ray examination for detection of breast cancer. Although it is generally quite accurate, some breast cancers are missed by mammography. This prompted the development of tomosynthesis (also called DBT or digital breast tomosynthesis), a 3D breast imaging method. A clinical trial called TMIST is being conducted in the US and Canada to evaluate if DBT is more accurate than the current digital mammography (DM) method in detecting breast cancers, especially the ones that grow quickly and would become deadly if not found early. On the other hand, some cancers are slow-growing and may not require intensive treatment. It is important to tell the difference between these two categories of cancers to ensure the right therapy and to avoid unnecessary anxiety to women. In this project, we want to find out how features of the cancer that are seen on the mammograms could be used to tell the difference between the slow-growing cancers and the ones that are aggressive. We will study changes in genes and proteins. We will compare biological findings to the DBT and DM images to see if certain patterns are related. The patterns in mammograms that we found that are related to the biology of the cancer can then be used by doctors to quickly and accurately determine what type of treatment is the most efficient, tell if patients require aggressive treatments or offer conservative therapy. Since we began screening women with DBT and DM for breast cancer at Sunnybrook Health Sciences Centre over 2 years ago, before the main TMIST began, we are in a position to conduct this exploratory, early-phase study that will analyze about 100 cancers and the surrounding breast tissue. It is a collaboration between experts in molecular pathology, oncology, and medical imaging science. Its findings will be used to evaluate some of the methods for and guide the much larger US/Canada TMIST trial involving 165,000 women where 3,500 cancers are expected. Our findings that aim to use patterns from mammographic images to distinguish between the “good” and “bad” cancers could eventually be used by doctors to treat cancers in the most efficient ways. This could lead to reduced deaths from breast cancer while at the same time avoiding both the side effects of treating advanced cancers or overtreating slow-growing cancers.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810474
Entities
People
- Martin Yaffe
Organizations
- Sunnybrook Research Institute
- United States Army