Machine learning approaches for the analysis of circulating tumour DNA

肿瘤细胞产生于细胞中的遗传变异（通常是突变），这导致细胞分裂不受限制。识别这些遗传变异，并随着时间和治疗的推移对其进行监测，将使我们能够在早期阶段检测癌症，针对特定肿瘤类型进行治疗，并确定治疗反应或缓解。然而，通常很难获得肿瘤组织，通常需要进行活检，这可能是侵入性的，并涉及了解潜在肿瘤的确切位置。这可能使其不可能随着时间的推移获得多个肿瘤样本。所有细胞都会释放无细胞DNA（cfDNA）到血流中，然而肿瘤细胞通常会释放更大量的DNA，称为循环肿瘤DNA（ctDNA）。这种ctDNA含有与肿瘤细胞相同的遗传变异，因此与其他cfDNA略有不同。鉴定ctDNA将提供一个容易获得的样本，使我们能够在任何时间点检测肿瘤的存在，并确定肿瘤的精确遗传组成。下一代测序可用于捕获所有cfDNA。确定其中有多少（如果有的话）是ctDNA是复杂的，因为ctDNA可以占所有cfDNA的约1%至90%，并且可能只有非常少量的遗传变异仅发生在ctDNA中。对一系列癌症类型的ctDNA研究的数量和规模都在增加，该项目将使用机器学习方法来改善ctDNA的检测并确定其临床实用性。首先，机器学习将用于学习一组特征，这些特征将ctDNA中发现的遗传变异与所有cfDNA中发现的遗传变异区分开来。具有匹配的肿瘤和cfDNA数据的数据集将提供理想的训练集，因为肿瘤数据将包含仅在ctDNA中可见的遗传变异集。其次，一旦肿瘤中存在的遗传变异被识别出来，学生将评估特定突变的重要性，或所有遗传变异的重要性，例如突变的总数，突变的类型，或cfDNA中ctDNA的比例。为了做到这一点，我们将测试这些特征中的任何一个是否可以用来预测临床因素，如癌症类型、对治疗的反应或预后。有越来越多的研究在一系列癌症类型中观察ctDNA，我们将使用统计荟萃分析来结合多项研究的联合收割机数据。学生将开发机器学习方法，并将其应用于尖端的基因测序数据。该项目有可能改善癌症检测和诊断，并提供治疗见解。学生将受益于一个多学科团队，该团队位于利物浦大学转化医学研究所的统计遗传学和药物遗传学小组，并与正在开发测序技术的工业合作者密切合作。

Tumour cells arise from genetic variants (usually mutations) in cells, which lead to unrestrained cell division. Identifying these genetic variants, and monitoring them over time and treatment, would allow us to detect cancer at an early stage, tailor treatment to specific tumour type, and determine treatment response or remission. However, it is often difficult to obtain tumour tissue, usually a biopsy is required which may be invasive and involves knowing the exact location of the potential tumour. This can make it impossible to obtain multiple tumour samples over time. All cells release cell free DNA (cfDNA) into the blood stream, however tumour cells often release a much larger amount, known as circulating tumour DNA (ctDNA). This ctDNA contains the same genetic variants as the tumour cells, so will be slightly different from other cfDNA. Identifying ctDNA would provide an easily accessible sample that would allow us to detect tumour presence at any time point and determine the precise genetic composition of the tumour. Next generation sequencing can be used to capture all cfDNA. Determining how much of it (if any) is ctDNA is complex since ctDNA can comprise between around 1% and 90% of all cfDNA, and there may be only a very small number of genetic variants which occur only in ctDNA. Studies of ctDNA across a range of cancer types are increasing in number and size, and the project will use machine learning approaches to improve detection of ctDNA and to determine its clinical utility. Firstly machine learning will be used to learn a set of features which distinguish genetic variants found in ctDNA from those found in all cfDNA. Datasets which have matched tumour and cfDNA data will provide an ideal training set, since the tumour data will contain the set of genetic variants which will be seen only in ctDNA. Secondly, once the genetic variants present in the tumour have been identified, the student will then assess the importance of specific mutations, or of the set of all genetic variants, for example the total number of mutations, type of mutations, or fraction of cfDNA that is ctDNA. To do this, we will test whether any of these features can be used to predict clinical factors such as cancer type, response to treatment, or prognosis. There are an increasing number of studies looking at ctDNA across a range of cancer types, and we will use statistical meta-analysis to combine data from multiple studies. The student will develop machine learning methods and apply them to cutting edge genetic sequencing data. This project has the potential to improve cancer detection and diagnosis, and to provide insights into treatment. The student will benefit from a multidisciplinary team, based in the Statistical Genetics and Pharmacogenetics group within the Institute of Translational Medicine at the University of Liverpool, and also work closely with industrial collaborators who are developing sequencing technology.