Comparative Genomic Hybridization of Neuroblastoma

神经母细胞瘤的比较基因组杂交

• 批准号: 7292868
• 负责人: Javed KHAN
• 金额: --
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7292868
• 关键词: Comparative; Genomic; Hybridization; Neuroblastoma

Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression. We are performing Array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma. We are developing a novel probabilistic algorithm, called topological statistics, to increase the sensitivity of cDNA A-CGH for detecting single-copy alterations. Our method not only overcomes the shortcoming of relative low sensitivity of cDNA A-CGH but also enables a direct visualization of the statistical confidence for the observed alterations. Furthermore using probabilistic approaches and machine learning algorithms (ANNs and Support Vector Machines) to estimate the frequency of genomic imbalances in tumors of different stage and MYCN amplification status, we are attempting to identify unique patterns of genomic imbalance in all three categories. Using these approaches we identifying models of the evolution and progression of neuroblastoma.

复发性非随机基因组改变是癌症的标志，这些失衡的表征对于我们对肿瘤发生和癌症进展的理解至关重要。我们正在对包含42,000个神经母细胞瘤元素的cDNA微阵列上进行阵列比较基因组杂交（A-CGH）。我们正在开发一种称为拓扑统计的新型概率算法，以提高cDNA A-CGH检测单拷贝改变的敏感性。我们的方法不仅克服了cDNA A-CGH相对低灵敏度的缺点，而且还可以直接可视化观察到的变化的统计置信度。此外，使用概率方法和机器学习算法（ANN和支持向量机）来估计不同阶段和MYCN扩增状态肿瘤中基因组失衡的频率，我们试图在所有三个类别中识别基因组失衡的独特模式。使用这些方法，我们确定了神经母细胞瘤的进化和进展模型。