Although recent advances in cancer research offer new ways to treat cancer, early detection still represents the best opportunity for curing cancer. Earlier stage treatment not only greatly improves patient survival but also costs considerably less. Therefore, a non-invasive, low cost and reliable cancer diagnostic assay could greatly benefit cancer patients and the public. In this regard, circulating cell-free DNA (cfDNA) holds tremendous potential to develop such a diagnostic assay. However, current methods are either not sensitive or not general enough, and cannot distinguish different cancer types. New technologies are needed to effectively extract the tissue and cancer-specific epigenetic information from cfDNA for early cancer detection.
Aims and Objectives
We are establishing a comprehensive cfDNA genetic and epigenetic sequencing platform. As a DPhil student, you will develop a computational workflow to extract and integrate multidimensional data, including epigenetic (methylation and hydroxymethylation) and genetic (base mutations, copy-number variations, and microbiome) information. We are in the process of establishing a database of tissue-specific epigenetic maps that will allow us to deconvolute the relative contributions of tissue and cancer DNA to the observed signal in individual cell-free DNA sequencing samples. As part of this project, you will be involved in building the first map of comprehensive cell-free epigenetic and genetic features for various solid tumours to help achieve the full potential of liquid biopsy. Finally, machine learning techniques will be applied to build classifiers that distinguish different cancer types from healthy controls and precancerous conditions.
This project would suit a candidate with a numerical mindset, ideally with good computational skills. However, the most important feature is curiosity and a willingness to learn. Through this project, you will become familiar with using the full spectrum of bioinformatics tools for next-generation sequencing. You will also learn to use a distributed high-performance computing environment and to develop new statistical approaches to analyse and visualise data.
Project reference number: 961
|Benjamin Schuster-Böckler||Oxford Ludwig Institute||Oxford University, Old Road Campus Research Building||GBRfirstname.lastname@example.org|
|Chunxiao Song||Oxford Ludwig Institute||Oxford University, NDM Research Building||GBRemail@example.com|
There are no publications listed for this DPhil project.