I-Corps: Epigenetic Profiling of DNA Methylation: Detection & Diagnostics

University Of Delaware, Newark DE

Investigators

Abstract

Researchers are using a diagnostic technology to pursue linkages between shifts in gene expression patterns mediated by epigenetic DNA modifications and the early onset of a disease etiology. The eventual result is that molecular, biochemical and physiological activities will diverge from a normative state and overtly express the disease symptom progression. If shifts in gene expression could be detected early, a diagnosis could be made before symptoms were significant. Better yet, detecting pre-symptomatic changes in regulatory controls over gene expression events (like epigenetic DNA methylation) would make it possible to diagnosis the pre-onset of an impending disease or disease risk. Early intervention prior to the onset of any disease could potentially reduce the progression of the disease and perhaps reduce the severity of the symptoms. The team proposes to apply an epigenetic profiling technology to the characterization of human lung cancer tumors. Once a tumor is large enough to spot on an x-ray it usually has already metastasized to nearby lymph nodes. Consequently, there is a large unmet need for tests that can diagnose certain types of cancer at an early stage, when the cancer is more likely to respond to treatment. The team is pursuing technical proof of concept work for the epigenetic profiling platform. The team's quantitative algorithms are combined with statistical pattern recognition technology that can enable rapid and cost-effective screening of billions of base pairs of sequence data in a biopsy or tissue sample, producing a sophisticated and statistically rigorous report to an attending physician, clinic or submitting laboratory. This information can be critical for the development of effective personalized medicine strategies for a broad range of disease detection and treatment needs. With genome sequencing rapidly becoming cost-effective, there is an increasing requirement for profiling DNA methylation sites within a patient's genome to provide an individual fingerprint profile of abnormal gene activities. This project may solve this need for rapid, high-throughput, low-cost, computational modeling and statistical pattern recognition to identify how a patient's DNA methylation profile varies from a normative population state.