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，每年导致超过16000人死亡(英国癌症研究中心，2016年的统计数据)。在所有癌症形式中，它可能也是最好的理解，因为它是如何形成的，以及哪些基因在其形成中发挥了作用。然而，尽管大量的工作集中在了解CRC上，我们仍然不知道导致它的所有基因突变。此外，尽管已知有60多种可增加结直肠癌风险的遗传基因变异，但产生遗传性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群体水平风险的遗传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，来影响特定细胞中制造的蛋白质的数量。然而，这一机制的实际证据在很大程度上是缺乏的，主要是因为我们不清楚基因组中变异的部分是如何发挥作用的，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。这项拟议的研究项目旨在了解与转录因子结合的DNA序列的突变或变异如何改变基因的活性，以及这种变化的活性如何导致肿瘤的形成。这是利用新的高通量方法进行的基础研究和对实验结果的计算数据分析。该项目首先将在实验室产生大量数据，然后使用定制的计算机程序来利用和理解这些数据。这项工作在很大程度上是生物科学家和计算科学家的合作，他们将密切合作，了解细胞如何判断写入DNA的基因何时何地应该活跃的基本机制。研究人员将试图理解“第二遗传密码”。第一个描述DNA序列如何转化为蛋白质序列的遗传密码在50多年前就被破译了，2001年公布了人类基因组的第一个草稿，它描述了在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示细胞生长的，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网络在读取第二个遗传密码时如何相互交谈和调节。解决这些问题对科学界来说是短期的好处，因为他们可以更深入地了解癌症是如何发展的，新的方法和计算工具有助于了解癌症的发展。从长远来看，我们的结果有望导致许多有助于预测、预防和治疗疾病的应用。例如，在该项目中开发的结果和工具可以用来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了解疾病基础的更广泛努力的一部分。最近发现，位于基因之间的基因变异非常常见，以至于大多数人都有其中的许多变异，这些变异在增加大多数常见疾病的风险方面起着重要作用。因此，我们期望在该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，该项目不仅将对结直肠癌的研究产生影响，而且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.