Predicting the prognosis of triple-negative breast cancer (TNBC) could be faster and more accurate using a new xenograft tool developed by scientists at Huntsman Cancer Institute at the University of Utah. TNBC is a particularly aggressive disease.
The team found that, “Only the most aggressive breast cancers can grow in this setting [the xenograft]. This is a type of “functional precision medicine” test because it measures a property of the tumor using a functional assay to test its behavior,” senior author Alana L. Welm, PhD, told Inside Precision Medicine.
Their research was published in JCO Precision Oncology. The team’s work is part of the TOWARDS Study, which has led to the development of a new mechanism to forecast the aggressiveness of TNBC.
Currently, TNBC lacks reliable recurrence prediction methods after treatments, such as chemotherapy and surgery. Unlike other breast cancers, TNBC tumor cells lack estrogen receptors, progesterone receptors, and high levels of HER2/neu protein. These biomarkers are targets for mainstay drugs in breast cancer.
These researchers used a patient-derived xenograft (PDX) model, where biopsies of tumors from patients were implanted into mice to grow human tumors. Welm points out this method allows for an earlier and more accurate assessment of the cancer’s aggressiveness.
Their tool was more accurate than existing methods in predicting whether TNBC will recur. Welm said, “By implanting a biopsy of the tumor into a PDX, we can discover how aggressive the cancer is. We hope to extend our new findings to improve the current standard tests used to predict whether the patients’ cancer will recur.”
“This study addresses a very pressing problem in the clinic,” says Christos Vaklavas, MD, co-first author of the study, and head of the breast cancer clinical program at Huntsman Cancer Institute. “PDX models help us not only predict with greater accuracy who will relapse and who will not, but also to treat recurrences with greater precision.”
In the second phase, now underway as a clinical trial, the researchers are testing specific drugs on PDX models. If these therapies prove effective, results will be shared with physicians, providing them with valuable insights on how to guide treatment decisions.
If a tumor grows in the PDX model, it often indicates a highly aggressive cancer, which is significantly harder to treat.
“We found that only the most aggressive breast cancers can grow in this setting [the donografs]. This is a type of “functional precision medicine” test because it measures a property of the tumor using a functional assay to test its behavior. If the tumor grows, we found that we can not only predict that the tumor has a greater chance of recurring as a distant metastasis, currently an incurable form of the disease, but we also can use the xenograft models to test personalized therapies for those patients,” Welm said.
The researchers also have two trials in which they are growing patient tumors in functional assays to test recurrence risk for early-stage disease, and to personalize therapy. They are also collaborating with computational scientists to train machine learning models on the data obtained in their studies, so that in the future they can make these predictions computationally, without having to grow each individual’s tumor in the lab.
