When Cells Grow Old: Modelling Cell Aging and Breast Cancer Risk

As we get older, there is increasing concern over our health, with the concern for many being cancer. For women, the specific concern is about breast cancer, with an estimated 1 in 8 women in the United States diagnosed with breast cancer in their lifetime (National Breast Cancer Foundation). As a consequence, about 80 percent of women ages 50-74 in the United States are screened for breast cancer (National Cancer Institute), making up the majority of the middle-aged female population in this country. However, there is a problem with how these screenings are conducted, with risk models either over or under-estimating cancer risk. Essentially, risk assessment methods rely heavily on population-wide data, which can be very generalized and insufficient in assessing a person’s  individual breast-cancer risk. Although there are other satisfactory methods that can test individuals’ DNA for mutations that may cause breast cancer, they are mostly for individuals with a hereditary risk, which only makes up a small subset of cases (about 5-10%). Thus, there is a need for an accurate screening method that can account for environmental exposures and other factors that may cause breast cancer in women that do not have a hereditary risk. 
To address this, researchers have proposed examining cellular aging as a way to assess individual risk for breast cancer. When we get sick or age, our cells are permanently affected, becoming less able to perform their normal functions, and becoming more stiff (much like many other things, such as our joints, when we age). These changes in functions can be quantified, and can be tracked to create a dataset to capture the features of individual cells and assess that individual’s risk. 
In their study, the researchers profiled human mammary epithelial cells (HMECs), which are human breast skin tissue cells, from a group of women with varying ages and backgrounds, looking for properties of how the cells function internally and if their cells showed signs of aging. They found that cells from younger women that carry a cancer-related mutation (called BRCA1/2) in their DNA exhibited faster cell aging. Another factor that caused more cellular aging was the overproduction of a protein called keratin 14. This information helped to describe the association between cancer risk and cellular age. The MechanoAge classifier, a machine learning platform designed to predict breast cancer risk based on cell functional properties, was found to be very accurate in predicting cellular age and cellular changes associated with cancer risk. The data they collected on cellular aging suggests that a cell’s mechanical properties (its shape and elasticity) may be a good tool for cancer risk assessment. This may be used for cellular mechanical profiling in the future, helping the cancer screening process by detecting more subtle cellular aging factors that would otherwise go unnoticed.  
Ultimately, this new method of cancer risk assessment can be better for patients, greatly enhancing early detection accuracy, especially for those who may not have a family history of breast cancer. By getting a more accurate risk assessment, patients can employ better risk reduction strategies with their care providers, allowing them to stay healthier in the long run. However, this new method of tracking cellular aging with respect to cancer risk raises questions about how patients prevent themselves from being considered high-risk for breast cancer in the first place. In a society that is so focused on youth in terms of health and beauty, this new information on how our cells age may force us to not just think about preserving our exterior youth, but our internal cellular youth as well.