Breast cancer can recur several decades after initial treatment, a phenomenon known as late recurrence. Recurrences are usually considered “late” if they occur more than 5 years after initial diagnosis and treatment. However, late recurrences have been documented as many as 39 years after diagnosis. Most cases of late recurrence occur in patients with hormone receptor-positive breast cancer, which accounts for about 75% of new breast cancers. In fact, more than half of the recurrences in hormone receptor-positive breast cancer are late recurrences. 

There are several emerging therapies which show promise in preventing late recurrences, but they would only be appropriate for patients at high risk for late recurrence. While several molecular tests can be used to predict a patient’s likelihood of recurrence, none of them were developed specifically to predict late recurrence risk. Thomas Ahern PhD, MPH, received a new R01 grant from the National Cancer Institute to develop the first molecular test specifically geared toward identifying late recurrence risk in breast cancer patients diagnosed with hormone receptor-positive tumors. 

The project will assemble a multi-disciplinary and international team, including UVM Cancer Center members Julie Dragon, PhD, and Paula Deming, PhD, and investigators at Aarhus University (Denmark), Emory University, University of North Carolina, University of Colorado, and BioRealm LLC. The project team will create a study population comprising late recurrence cases and recurrence-free controls among pre- and post-menopausal women diagnosed with hormone receptor-positive breast cancer. The team will collect archived primary breast tumors to carry out comprehensive gene expression profiling. Gene expression data will be augmented with high-quality clinical data on tumor, treatment, and patient characteristics. The team will then apply cutting-edge machine learning approaches to identify a concise array of genes that robustly predict late recurrence. 

Ahern and colleagues have already seen the potential of their approach. In a small pilot study, they identified several genes predictive of late recurrence that do not appear in current risk models, reinforcing the need for gene expression models specifically tailored for late recurrence prediction.

The implications of this research are far-reaching. Results from this study will allow identification of patients at high risk for late recurrence, giving them access to potentially life-saving preventative therapies.