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

研究人员正在使用一种诊断技术来探索表观遗传DNA修饰介导的基因表达模式变化与疾病病因学早期发病之间的联系。最终的结果是，分子、生化和生理活动将偏离正常状态，公开表达疾病症状的进展。如果能及早发现基因表达的变化，就可以在症状明显之前做出诊断。更好的是，检测对基因表达事件(如表观遗传DNA甲基化)的调控控制的症状前变化将使诊断即将到来的疾病或疾病风险成为可能。任何疾病发作前的早期干预可能会减缓疾病的进展，或许还会减轻症状的严重程度。该团队建议将表观遗传学图谱技术应用于人类肺癌肿瘤的特征描述。一旦肿瘤大到可以在X光上发现，它通常已经转移到附近的淋巴结。因此，有一个巨大的未得到满足的需求，即可以在癌症更有可能对治疗产生反应的早期阶段诊断某些类型的癌症的测试。该团队正在进行表观遗传图谱平台的概念技术验证工作。该团队的定量算法与统计模式识别技术相结合，可以对活检或组织样本中的数十亿碱基对数据进行快速且经济高效的筛选，向主治医生、诊所或提交实验室生成复杂且统计严谨的报告。这些信息对于为广泛的疾病检测和治疗需求制定有效的个性化药物战略至关重要。随着基因组测序迅速变得具有成本效益，对分析患者基因组中的DNA甲基化位点以提供异常基因活动的个人指纹图谱的要求越来越高。这个项目可能会解决快速、高通量、低成本、计算建模和统计模式识别的需求，以确定患者的DNA甲基化特征与标准人群状态的差异。

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.