ROCKVILLE, MD (September 29, 2008)–20/20 GeneSystems, Inc. (“20/20”) announced today that the U.S. National Cancer Institute (NCI) has awarded the company a grant of $650,000 under its Small Business Innovative Research (SBIR) program to utilize its patented tumor profiling technology to create new tests for selecting treatments for breast cancer.
Despite advances in the early detection of breast cancer, there were an estimated 213,000 new cases and 40,840 deaths in the U.S. alone in 2005. This makes breast cancer the most prevalent malignancy in women constituting 30% of all cancers reported in the U.S. in females. Traditional treatments involve surgery, radiation, hormonal and/or cytotoxic chemotherapy. Breast cancer is among the first cancers to be effectively treated with more recently developed “targeted” therapies that attack molecular and genetic abnormalities in the tumor. Since each individual’s breast tumor will differ at the molecular level, technology that will fingerprint tumor cells will help determine which drug will likely be effective for each patient. This paradigm represents advancement over the “one size fits all” approach of cancer treatment for decades and is expected to lead to better outcomes and cost savings.
20/20 has developed a technology that maries traditional microscope based visual analysis with newer multiplex biomarker techniques. The technology, called the Layered ImmunoPlex Assay (LIPA), simultaneously analyzes the visible (histological) characteristics of the tumor together with the underlying protein or biomarker fingerprint. Biomarkers from the tissue section are accurately ‘mapped’ to a layered array which is then read using sophisticated microscopes and digital imaging techniques resembling computed tomography (CT) scans. This provides the medical team with critical information needed for optimal treatment selection. According to one of the outside expert reviewers of the company’s Phase II SBIR grant application:
Targeted therapies are growing in importance and each therapy requires a test that will help to predict the response of the individual with the targeted disease. In most instances, the current generation of tests has proven to be imperfect, having only moderate success at correctly predicting response. More accurate