Methods of treatment based on molecular characterization of breast cancer

Stanford researchers have formulated a statistical model to determine the risk of breast cancer recurrence with unprecedented accuracy in women 5 – 20 years after initial diagnosis. By categorizing distinct genome-driven subsets of breast cancer, inventors were able to determine the rate, route, and location of tumor metastasis with utmost precision. Currently, doctors rely on the size/grade of the tumor and the degree of lymph node spread but the exact course of the tumor is unknown especially after the patient was considered cured (after 5 yrs post diagnosis). This leaves doctors with no full-proof way to plan careful clinical follow-ups--ultimately missing critical cancer recurrences.

After following over 3000 women diagnosed with breast cancer (between 1977 and 2005), researchers discovered that certain molecular components of tumor tissue could be categorized into specific, distinct tumors which helped to provide a chronology of tumor metastasis. More specifically, 25% of women categorized as estrogen receptor positive and HER2 negative have a 42-55% chance of late relapse within 20 years. Further, researchers found that women with the aggressive and difficult to treat triple-negative breast cancers were able to be divided into defining subgroups.

This discovery allows practitioners to drastically improve upon clinical management and outcome by honing-in on targeted therapies and eventual drug delivery systems for millions of women with precision and confidence – solving a huge, unmet clinical need worldwide (please refer to list of applications and advantages).

Stage of Development:
Prediction models have been verified and continued research will confirm the clinical, therapeutic, and diagnostic use, and possible drug therapies with both in vivo and in vitro data