Signal processing for accurate detection of copy number variants in cancer

描述（由申请人提供）：人类基因组变异性发生在不同的尺度上，从单核苷酸多态性到含有大量基因的DNA片段。拷贝数变异（CNVs）是人类遗传异质性的重要组成部分，也与多种疾病和病症相关。虽然大的CNV可以在有噪声的基于阵列的数据中检测到，但是由于低信噪比（SNR），短的局部像差可能是检测不到的。然而，短CNV可能在人类疾病中起重要作用，因此需要高度灵敏的方法来检测它们。为了有意义地鉴定疾病CNV，有必要首先估计正常等位基因畸变的位置和水平以进行基线比较。我们已经成功地开发了一种基于信号处理的方法，用于序列去噪，然后进行模式匹配，以提高正常基因组数据中的SNR并改善正常人中的CNV检测。我们建议进一步发展这种方法的正常人，然后开发和扩展它的应用程序在癌症设置，特别是，非典型脑膜瘤和肺鳞状细胞癌，高度敏感和特异性检测癌症相关的CNV的。癌症中的CNV检测对于理解疾病的病因并最终开发治疗靶点至关重要，我们的方法学将为这一目标做出独特而重大的贡献。我们将使用国家癌症研究所的癌症基因组图谱资源来获得正常阵列数据和肺癌数据。 公共卫生相关性：拷贝数变异（CNVs）是人类遗传异质性的重要组成部分，也与多种疾病和病症相关。我们建议进一步发展一种正常人CNV检测方法，然后发展和扩展其在癌症环境中的应用，特别是在非典型脑膜瘤中的应用 和肺鳞状细胞癌，用于癌症相关CNV的高灵敏度和特异性检测。癌症中的CNV检测对于理解疾病的病因并最终开发治疗靶点至关重要，我们的方法学将为这一目标做出独特而重大的贡献。

DESCRIPTION (provided by applicant): Human genomic variability occurs at different scales, from single nucleotide polymorphisms to DNA segments containing a large number of genes. Copy number variations (CNVs) represent a significant part of human genetic heterogeneity and have also been associated with a wide range of diseases and disorders. Although large CNVs may be detectable in noisy array-based data, short, localized aberrations may be undetectable due to low signal-to-noise ratio (SNR). Short CNVs may, however, play an important role in human disease, and thus highly sensitive methodologies are needed for their detection. For meaningful identification of disease CNVs, it is necessary to first estimate the locations and levels of normal allelic aberrations for baseline comparison. We have successfully developed a signal processing-based methodology for sequence denoising followed by pattern matching, to increase SNR in normal genomic data and improve CNV detection in normals. We propose to further develop this method for normals, and then to develop and extend it for application in the cancer setting, in particular, for atypical meningioma and lung squamous cell carcinoma, for highly sensitive and specific detection of cancer related CNV's. CNV detection in cancer is critical for understanding the etiology of disease and ultimately for the development of therapeutic targets, and our methodology will contribute uniquely and substantially to this goal. We will use The Cancer Genome Atlas resource of the National Cancer Institute for normal array data and for the lung cancer data. PUBLIC HEALTH RELEVANCE: Copy number variations (CNVs) represent a significant part of human genetic heterogeneity and have also been associated with a wide range of diseases and disorders. We propose to further develop a method for CNV detection for normals, and then to develop and extend it for application in the cancer setting, in particular, for atypical meningioma and lung squamous cell carcinoma, for highly sensitive and specific detection of cancer related CNV's. CNV detection in cancer is critical for understanding the etiology of disease and ultimately for the development of therapeutic targets, and our methodology will contribute uniquely and substantially to this goal.