Integrative approach for predicting cancer driver genes

 描述（由申请人提供）：致癌作用，正常细胞向恶性癌症的进展，来源于通过获得“驱动基因”中的（体细胞）突变驱动的癌症的标志性能力，具有细胞增殖和潜在转移的选择性优势。现代癌症基因组学研究的一个主要动机是通过发现新的驱动基因来破译癌症的遗传结构。最广泛使用的预测和优先考虑驱动基因的方法是基于突变频率的统计数据。已经提出了几种方法来鉴定具有过量体细胞突变的基因[9-11]，称为显著突变的基因。我建议解决这种方法的两个主要局限性。首先，考虑到目前可用的测序数据量，这些方法的统计效力不足[15]。我将通过利用目前可用的癌症基因组学中的各种信息来提高统计能力，以开发机器学习方法。其次，这些方法的真正有效性几乎没有客观的明确性[11，14]，因为除了少数众所周知的驱动因素外，没有商定的驱动基因黄金标准。我将开发一个框架来比较驱动基因预测方法的有效性，在没有金标准的情况下。有效和高效地识别癌症驱动基因对于癌症基因组学的科学资助政策非常重要。

 DESCRIPTION (provided by applicant): Carcinogenesis, progression of normal cells to malignant cancer, derives from hallmark capabilities of cancer driven by acquiring (somatic) mutations in "driver genes" with a selective advantage for cellular proliferation and potentially metastasis. A major motivation for modern cancer genomics studies is to decipher the genetic architecture of cancer by discovering new driver genes. The most widely-used approaches to predict and prioritize driver genes are based on statistics of mutation frequencies. Several methods have been proposed to identify genes with an excessive number of somatic mutations [9-11], known as significantly mutated genes. I propose to address two major limitations of this approach. First, these methods are insufficiently statistically powered given the amount of sequencing data currently available [15]. I will improve statistical power by leveraging diverse information in cancer genomics currently available into a developed machine learning method. Second, there is little objective clarity about the true effectiveness of these methods [11, 14], since there is no agreed-upon gold standard of driver genes, with the exception of a few well-known drivers. I will develop a framework to compare the effectiveness of driver gene prediction methods, in the absence of a gold standard. Both effectively and efficiently identifying cancer driver genes is a matter of great importance to science funding policy towards cancer genomics.