Research Finds Novel Mechanism That Predicts Severity Of Triple Negative Breast Cancer - Video

Their research, published in JCO Precision Oncology as part of the TOWARDS study, has led to the development of a new mechanism that accurately forecasts the aggressiveness of triple-negative breast cancer. This advancement could revolutionize the way doctors treat triple-negative breast cancer, allowing them to identify higher-risk patients and tailor precise treatments.

Currently, triple-negative breast cancer lacks reliable methods to predict recurrence after treatments like chemotherapy and surgery. Unlike other breast cancers, triple-negative breast cancer is challenging to treat because its tumour cells lack estrogen receptors, progesterone receptors, and high levels of HER2/neu protein, according to experts at the National Institutes of Health. This often results in a higher likelihood of relapse after treatment.

Researchers used a patient-derived xenograft (PDX) model, where biopsies of tumours from patients were implanted into mice to grow human tumours. Alana Welm, PhD, senior author of the study, senior director of basic science at Huntsman Cancer Institute, and professor of oncological sciences at the U, highlights the significance of this method noting that it allows for an early and accurate assessment of the cancer’s aggressiveness.

Cindy Matsen, MD, co-first author of the study, leader of the Breast and Gynecologic Disease Center at Huntsman Cancer Institute and associate professor in the Department of Surgery at the University of Utah, emphasizes the direct impact this research could have on patient care. She describes the study as highly relevant to addressing a major challenge in breast cancer treatment, with the potential of creating more personalized treatment plans for patients with recurrent triple-negative breast cancer.

Reference: Christos Vaklavas et al., TOWARDS Study: Patient-Derived Xenograft Engraftment Predicts Poor Survival in Patients With Newly Diagnosed Triple-Negative Breast Cancer. JCO Precis Oncol 8, e2300724(2024).DOI:10.1200/PO.23.00724