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Since breast cancer is a heterogeneous disease composed of various molecular subtypes, the need for biomarkers that permits prognostication across subtypes is advantageous.
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Background Breast cancer is second leading cause of death among women in the United States in 2016 and It is estimated to be responsible for over 40,000 deaths in 2017 (ACS). The use of biomarkers plays a key role in the management of patients with breast cancer, especially in the decision process to select the appropriate systemic therapy to be administered. Furthermore, the discovery of new tissue-based and gene biomarkers has led to the development of a “molecular signature” for predicting patient outcome and treatment modalities. There are three subtypes of breast cancer that are determined by performing specific tests on a sample of the tumor. The first subtype is a tumor that is positive/negative for a hormone receptor, either estrogen (ER) and/or progesterone (PR); tumors without these receptors are classified “hormone receptor-negative”. The second subtype is characterized by the overexpression the human epidermal growth factor receptor 2 (HER2) protein on the tumor. HER2 proteins are receptors on normal breast cells and help control the growth, but when overexpressed make the tumor grow faster and are designated HER2-positive tumors. The last subtype is designated triple-negative, since it does not express ER, PR, and/or HER2. Technology Description Researchers at UC San Diego have identified a Gα-interacting vesicle-associated protein (GIV or Girdin) as a bona fide metastasis-related protein that modulates multiple signaling pathways triggered by diverse classes of receptors. Full-length GIV has been found to be significantly overexpressed in tumor cells with high metastatic potential and undetectable in tumors cells with poor metastatic potential. Furthermore, a number of exploratory studies have demonstrated the potential for GIV to serve as a biomarker for tumor aggressiveness for various subtypes of breast cancer. To that end, a biomarker study was conducted on a cohort of 187 patients with breast cancer to evaluate the prognostic role of total GIV (tGIV) and tyrosine phosphorylated GIV (pYGIV) in various subtypes. The study’s findings are the following: The combined presence of tGIV and cytoplasmic pYGIV was associated with worse outcome among patients that were HER2-positive The presence of membrane pYGIV alone was associated with worse outcomes with patients with TNBC tGIV is an effective prognosticator in HER2-positive turmors, but not in TNBCs Cytoplasmic pYGIV is a key determinate of poor outcome in HER2-positive tumors Applications An algorithm was developed that is potentially useful for stratification and risk of recurrence among patients with different molecular subtypes of breast cancer. Advantages Since breast cancer is a heterogeneous disease composed of various molecular subtypes, the need for biomarkers that permits prognostication across subtypes is advantageous. State Of Development Further studies on larger cohorts are warranted to determine whether such an algorithm is effective and robust in prognostication outcome and predicting chemoresponsiveness in a clinical setting. Intellectual Property Info A provisional patent has been submitted and the technology is available for licensing.