A Clinical Assay for Predicting the Prognosis of Ductal carcinoma in situ (DCIS)

Track Code: 
This invention is a molecular screen to predict which patients’ non-invasive breast tumors will eventually progress to invasive breast cancers.
Ductal carcinoma in situ (DCIS) is the most common form of pre-invasive cancer diagnosed in women, with 50,000 cases diagnosed per year in the U.S. Progression from DCIS to invasive ductal carcinoma (IDC), significantly decreases long-term survival. 5-20% of patients receiving standard treatment of surgery and radiation therapy experience disease relapse, and ½ to 2/3 of cases are accompanied by invasive disease. Risk factors do not accurately predict disease progression. Small DCIS lesions or low grade DCIS, which are considered low risk, may still become invasive. Relative to invasive breast cancer, the use of biomarkers in DCIS as risk factors for progression are recent and have not been clearly studied. This new invention seeks to accurately identify cases of DCIS that may progress to invasive breast cancer.
The invention is a clinical assay for predicting cancer, which may be used by pathologists who specialize in diagnosing cancer. Information gathered by the pathologist can be used to make clinical decisions by an oncology team.
How it works: 
Cells and/or tissue from non-invasive cancers (DCIS) are evaluated for expression of at least 5 biomarkers compared with disease-free samples. A unique algorithm is used to determine the prognosis of the cancer (whether or not DCIS will progress to invasion).
There is no gold standard test to identify which patients with DCIS will progress and develop invasive breast cancers. By developing a new screen that may predict the patients who will progress, lives may be saved through more effective preventive treatments of DCIS. Since many patients with DCIS may be subjected to painful and costly diagnostic procedures and preventive treatments, many women who are identified as not at risk may also be spared the harsh surgical treatments.
Why it is better: 
Our assay has a better predictive value and may be more accurate than a comparable test with limited predictive value.
Licensing Associate: 
Aswini Betha, PhD · abetha@ku.edu · 913-588-5713
Wei Bin Fang
Nikki Cheng