Dissect genetic basis of cancer using allele-specific gene expression

描述（申请人提供）：一些先驱研究表明，癌症驱动/贡献基因可能表现出基因表达的等位基因失衡。这种重要的等位基因特异性表达（ASE）信息尚未广泛应用于癌症研究，因为传统的基因表达微阵列无法获得ASE。然而，随着测序技术的快速发展，RNA-seq越来越受欢迎，并可能在不久的将来取代基因表达微阵列。使用RNA-seq转录丰度通过序列读取数来测量，ASE可以通过与杂合snp重叠的序列读取数来测量。因此，在癌症研究中使用ASE的唯一障碍是开发适当的统计方法和数据分析策略。这是本课题提出的研究重点。我们建议开发一种无监督的方法来鉴定基因表达失衡的等位基因，开发新的方法来将等位基因特异性拷贝数（ASCN）变化与ASE联系起来，并结合来自种系和肿瘤组织的基因组数据来优先考虑因果种系突变，而不需要对照样本或大样本量。我们将运用我们的方法研究248名结直肠癌患者的基因组数据。结直肠癌是成年人癌症死亡的第二大原因。在美国，每年有160,000例结直肠癌被诊断出来，57,000名患者死于这种疾病。我们的研究结果将有助于深入了解结直肠癌的分子机制，从而帮助确定治疗和药物开发靶点，最终减轻这种疾病的负担。我们的方法和数据分析策略也将有利于许多其他癌症研究，以确定相关的种系突变和肿瘤驱动/贡献基因。

DESCRIPTION (provided by applicant): Some pioneer studies have shown that cancer driver/contributor genes may show allelic imbalance of gene expression. This important allele-specific expression (ASE) information has not been widely used in cancer studies because ASE is not available from traditional gene expression microarrays. However, with the rapid advance of sequencing techniques, RNA-seq is becoming more popular and may replace gene expression microarrays in the near future. Using RNA-seq transcription abundance is measured by the number of sequence reads, and ASE can be measured by the sequence reads that overlap with the heterozygous SNPs. Therefore the only obstacle to using ASE in cancer studies is the development of appropriate statistical methods and data analysis strategies. These are the focus of the present research project proposed here. We propose to develop an unsupervised approach to identify genes with allelic imbalance of gene expression, develop new methods to associate allele specific copy number (ASCN) changes with ASE, and combine genomic data from germline and tumor tissues to prioritize causal germline mutations without requiring control samples or huge sample size. We will apply our method to study genomic data from 248 colorectal cancer patients. Colorectal cancer is the 2nd leading cause of death from cancer among adults. Every year in the United States, 160,000 cases of colorectal cancer are diagnosed and 57,000 patients die of this disease. Our results will provide insight into the molecular mechanisms of colorectal cancer, and thus help to identify therapeutic and drug development targets, ultimately reducing the burden of this disease. Our methods and data analysis strategies will also benefit many other cancer studies for the identification of relevant germline mutations and tumor driver/contributor genes.