Molecular mechanisms conferring risk for colorectal cancer

结直肠癌（CRC，或肠癌）是英国第四大常见癌症，每年有超过16000人死亡（英国癌症研究中心，2016年统计数据）。就癌症如何形成以及哪些基因在其形成过程中起作用而言，它可能是所有癌症形式中理解得最好的。然而，尽管大量的工作集中在了解CRC，我们仍然不知道所有的基因突变导致它。此外，尽管已知超过60种增加CRC风险的遗传基因变异，但产生遗传疾病风险的机制尚不清楚。这在很大程度上是因为携带最大比例人群风险的基因变异存在于基因组中不编码蛋白质的区域。据推测，这些变异通过影响被称为转录因子的蛋白质的DNA结合，影响了特定细胞中蛋白质的数量。然而，这种机制的实际证据在很大程度上是缺乏的，主要是因为我们并不确切地了解变异所在的基因组部分如何起作用，以及DNA序列的变化如何影响转录因子的结合和基因的活性。拟议的研究项目旨在了解结合转录因子的DNA序列的突变或变异如何改变基因的活性，以及这种改变的活性如何导致肿瘤的形成。这是利用新颖的高通量方法和实验结果的计算数据分析的基础研究。该项目将首先在实验室中生成大量数据，然后使用定制的计算机程序利用和理解这些数据。这项工作在很大程度上是生物学和计算科学家之间的合作，他们将密切合作，以了解细胞如何判断何时何地写入DNA的基因应该活跃的基本机制。研究人员将试图理解“第二种遗传密码”。第一个描述DNA序列如何转化为蛋白质序列的遗传密码早在50多年前就已被破译，而人类基因组的第一份草图于2001年发表，它描述了所有人类细胞中发现的化学字母A、C、G和T的序列。确定人类基因组序列在生物学和医学的几个领域产生了非常大的影响。然而，仅仅知道字母的顺序还不足以理解它们是如何指导细胞生长的，以及这个过程在癌症中是如何出错的。我们的项目旨在了解蛋白质和基因的网络如何在读取第二个遗传密码时相互交谈和调节。解决这些问题对科学界来说是一个短期的好处，因为它能提供更深入的理解，新的方法和计算工具来帮助理解癌症是如何发展的。从长远来看，我们的研究结果有望带来许多应用，帮助预测、预防和治疗疾病。例如，该项目开发的结果和工具可用于改进对谁有患癌症风险的预测。在更广泛的背景下，拟议的项目是使用先进的基因组和计算工具来了解疾病基础的更广泛努力的一部分。位于基因之间的基因变异是如此普遍，以至于大多数人都有许多基因变异，最近发现它们对增加患大多数常见疾病的风险很重要。因此，我们期望项目内开发的方法和工具将广泛适用于研究引起其他常见疾病的机制。因此，该项目不仅将影响结直肠癌的研究，还将对其他常见疾病的研究、预防和治疗产生更广泛的影响。

Colorectal cancer (CRC, or Bowel cancer) is the fourth most common cancer in UK, accounting for over 16 000 deaths annually (Cancer Research UK, 2016 statistics). It is also perhaps best understood of all cancer forms, in terms of how it forms, and which genes have a role in its formation. However, despite the large amount of work that has concentrated on understanding CRC, we still do not know all the genetic mutations that cause it. Furthermore, although more than 60 inherited genetic variants that increase risk for CRC are known, the mechanisms that generate the inherited disease risk are not well understood. This is in large part because the genetic variants that carry the largest fraction of population-level risk reside in the region of the genome that does not code for proteins. The variants are presumed to affect the amount ofproteins made in particular cells, by affecting DNA binding of proteins called transcription factors. However, actual evidence for this mechanism is largely lacking, mostly because we do not understand exactly how the part of the genome where the variants are functions, and how changes in DNA sequence affect binding of the transcription factors and activity of genes. The proposed research project aims to understand how mutations or variations in DNA sequences that bind the transcription factors changes the activity of genes, and how the changed activity leads to tumour formation. This is basic research utilizing novel high throughput methods and computational data analysis of the experimental results. The project will first generate vast amounts of data in a laboratory, and then utilize and understand it using tailor-made computer programs.The work is very much a collaboration between biological and computational scientists, who will work closely together to understand basic mechanisms of how cells can tell when and where the genes written in their DNA should be active. The researchers will seek to understand 'the second genetic code'. The first genetic code that describes how DNA sequence is converted to protein sequence was decoded already more than 50 years ago, and the first draft of human genome, which describes the sequence of the chemical letters A, C, G and T found in all human cells was published in 2001. Determining the human genome sequence has had a very large impact in several fields of biology and medicine. However, knowing just the order of the letters is not enough to understand how they instruct cells to grow, and how this process goes wrong in cancer. Our project aims to understand how the network of proteins and genes talk and regulate each other when reading the second genetic code. To solve these issues is a short-term benefit to scientific community in terms of deeper understanding, novel methods and computational tools aiding to understand how cancer develops. In the longer term, our results are expected to lead to many applications that help to predict, prevent and treat disease. For example, the results and tools developed within the project can be used to improve predicting of who is at risk to develop cancer.In a wider context, the proposed project in part of a broader effort to use advanced genomic and computational tools to understand the basis of disease. Genetic variants that are located between genes, and are so common that most people have many of them have recently been found to be important in increasing risk to most common diseases. We therefore expect that the methods and tools developed within the project will be widely applicable to the study of the mechanisms that cause other common diseases. Hence, this project will not only impact research on colorectal cancer but will have broader implications for research, prevention and treatment of other common diseases.