RNA sequencing analysis of Cancer

摘要 癌症是一种复杂的疾病，代表着数百种不同的疾病类型。重要的是要确定 这些疾病类型及其潜在的致因变化有助于指导和量身定做治疗。基因组学 技术已被用于癌症基因组图谱(TCGA)等项目中，以表征大型 癌症的数量。然而，这些早期的基因组学项目大多是在没有选择的队列中进行的， 由于技术上的限制，随访和许多临床相关的数据集无法进行分析 使用福尔马林固定的少量组织。癌症基因组学中心的新举措 将帮助我们解决更多具有临床意义的问题。我们建议利用我们在基因表达方面的专业知识 和RNA测序分析，以进一步确定癌症的特征，以帮助确定新的诊断标记物 药物疗法和临床协会。我们将通过三个目标来实现这一点。对于目标1，我们将使用RNA 识别体细胞突变的序列信息，改进B和T的图谱组装和量化 细胞，识别结构变异，并执行高水平的质量控制，包括跨 同一样本的序列数据。对于目标2，我们将计算基因和异构体水平，用于 确定肿瘤亚型、可供选择的异构体用途以及先前定义的基因标记和应用 肿瘤亚型。对于目标3，我们将使用监督分析来寻找与 寻找与临床结果或药物相关的分子特征和模型基因表达数据 治疗反应。我们希望我们的数据与其他基因组数据分析中心的数据集成在一起， 将揭示癌症发展、进展和癌症治疗的新见解。我们还将 利用我们从泛癌症分析中学到的信息来识别共享的基因组改变或 可能加速治疗发展的途径活性。

Abstract Cancer is a complex disease and represents hundreds of different disease types. It is important to identify these disease types and their underlying causative alterations to help guide and tailor treatments. Genomic technologies have been used in projects such as The Cancer Genome Atlas (TCGA) to characterize a large number of cancers. However, these early genomics projects were mostly on unselected cohorts with limited follow-up and many clinically relevant datasets were not feasible for analysis due to limitations in technologies using formalin-fixed and small starting quantities of tissue. New initiatives of the Center for Cancer Genomics will help us address more clinically-meaningful questions. We propose to use our expertise in gene expression and RNA-sequencing analysis to further characterize cancer to help identify novel markers for diagnosis, novel drug therapies and clinical associations. We will approach this in three aims. For Aim 1, we will use RNA sequence information to identify somatic mutations, improve mapping assembly and quantification of B and T cells, identify structural variations, and perform high level quality control including genotype checks across sequence data for the same sample. For Aim 2, we will calculate gene and isoform levels that will be used to identify tumor subtypes, alternative isoform usage, and application of previously defined gene signatures and tumor subtypes. For Aim 3, we will use supervised analyses to find genes significantly associated with molecular features and model gene expression data to look for association with clinical outcome or drug treatment response. We expect our data, integrated with the data from other Genome Data Analysis Centers, will uncover novel insights into cancer development, progression and treatment of cancer. We will also leverage the information we have learned from pan-cancer analyses to identify shared genomic alterations or pathway activity that may accelerate therapy development.