Machine learning approaches for the analysis of circulating tumour DNA

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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.