RNA sequencing analysis of Cancer

摘要 癌症是一种复杂的疾病，代表了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病因改变，以帮助指导和定制治疗。基因组 技术已被用于项目，如癌症基因组图谱（TCGA），以表征一个大的 癌症的数量然而，这些早期的基因组学项目大多是在有限的 由于技术限制，随访和许多临床相关数据集无法进行分析 使用福尔马林固定和少量的起始组织。癌症基因组学中心的新举措 能帮助我们解决更多临床上有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，以进一步表征癌症，以帮助确定新的诊断标记，新的 药物治疗和临床协会。我们将从三个目标着手。对于目标1，我们将使用RNA 序列信息，以鉴定体细胞突变，改善作图组装和B和T的定量 细胞，识别结构变异，并进行高水平的质量控制，包括基因型检查， 同一样品的序列数据。对于目标2，我们将计算基因和亚型水平，用于 鉴定肿瘤亚型、替代同种型使用和应用先前定义的基因标签， 肿瘤亚型对于目标3，我们将使用监督分析来寻找与以下因素显著相关的基因： 分子特征和模型基因表达数据，以寻找与临床结果或药物 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相结合， 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了解到的信息来识别共享的基因组改变， 可能加速治疗发展的途径活动。

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.