Patients with early-stage breast cancer and those who are diagnosed with locally advanced breast cancer are often treated with a combination of pre-operative therapy, surgical removal of the tumor, and post-operative therapy. However, many patients are over-treated because the field has lacked effective biomarkers that can detect minimal residual disease, which refers to the very small number of cancer cells that might remain in the body during or after phases of cancer treatment. Detecting residual disease requires highly sensitive laboratory methods, and Division of Surgical Oncology Associate Professor Dr. Muhammed Murtaza recently developed a new test called Structural Variant Enrichment and Normalization, or SVEN, to improve its detection. Now, with a new three-year, $1 million Cooperative Award (UH3) from the National Cancer Institute, Murtaza and his team will be validating SVEN in a large sample of patients with breast cancer.
“Our goal is to be able to tailor treatment plans to meet the needs of the individual patient at each step of their treatment,” explained Murtaza. “For example, if we can confidently rule out any indication of residual cancer after surgical removal of a tumor, then that patient could be spared post-operative chemotherapy and/or radiation without compromising their long-term outcomes. Similarly, if there is no evidence of residual tumor after pre-surgical therapy, it may become safe to omit surgery or limit its scope. To provide each patient with just the right extent of treatment while preserving and improving outcomes, clinicians require tests that can accurately detect residual disease.”
SVEN uses a novel approach to detect circulating tumor DNA (ctDNA), which is genetic material from a tumor that is present in the bloodstream and which serves as a biomarker for residual disease. When a patient is treated for cancer, the amount of ctDNA can drop to low enough levels that current tests fail to identify it. Thus, when Murtaza’s team developed SVEN, they knew it had to be sensitive enough to detect even very low levels of cancer DNA. It also had to be able to distinguish between DNA from cancer cells and DNA from normal cells. To address this, SVEN first identifies DNA alterations that are unique to each patient’s cancer. Once this test is designed for each patient, blood samples can then be collected and tested to find any evidence of these unique alterations over the course of the patient’s treatment.
“To mimic what we might find in blood samples from cancer patients, we tested accuracy of SVEN using DNA from cell lines and prepared mixtures with trace amounts of cancer DNA. In these experiments, our results suggest that SVEN can accurately predict if a sample is free of cancer DNA,” said Murtaza. In addition, the team found that SVEN was able to detect cancer DNA even when it was present in trace amounts against a large background of DNA from normal cells.
Working with collaborators at UW Health and the Mayo Clinic in Arizona, the research team will now be expanding testing of SVEN to confirm these preliminary findings in a much larger samples of patients. They plan to collect blood samples from over 200 patients with early and locally advanced breast cancer before they begin any treatment and as they progress through their treatment in order to test SVEN’s ability to detect changing ctDNA levels at various stages of treatment.
“Once we’ve validated the identified clinically relevant thresholds for changes in ctDNA levels and established the ctDNA threshold that is relevant for prognosis after surgery, we can then design a clinical trial to evaluate if treatment plans that are optimized based on changes in ctDNA levels can maintain or even improve patient outcomes,” said Murtaza.