A cancer phenotype is the specific form of cancer affecting a patient. Phenotypic prediction of cancer patients is a critical task in the context of stratified medicine. This is because the type of treatment, or combination of treatments, that are administered to cancer patients depend on the phenotypes, such as cancer histological subtypes or metastatic status.
Accurate prediction means that we can minimise the risk of patients going through unnecessary treatment that can be extremely painful. In this regard, the patients’ genomic data or information have been increasingly seen as a valuable predictor. Specifically, genome-wide copy number alteration (CNA) profiles of the patients, generated using the next-generation sequencing (NGS) technology, contain valuable information which we can utilize to make phenotypic prediction.
The CNA profile of a patient comes in the form of gains (‘jumps’) and losses (‘drops’) from the normal two copies along the genomes. The gains and losses are in segments, i.e. the estimates tend to be the same along the neighbouring genomic regions, and the segments can be short or long. From statistical point-of-view, the pattern of gains and losses along the genome can be considered as a time series. CNA profile from a patient can then be summarized using a mathematical transformation called a wavelet ('little wave') to produce wavelet coefficients.
These coefficients represent the frequency of gains and losses at multiple scales, which can be considered to correspond to genome, chromosome, chromosomal arm, region, short region, and random error levels.
This project will take advantage of the wavelet transformation, and develop new statistical methodology for improved prediction of the phenotypes. The main characteristic of the wavelet transformation is that we can perform thresholding; “switching off” some wavelet coefficients that are expected to correspond to either random error or variability that is not directly related to the phenotype of interest. After the thresholding, we are then left with CNA information that is more directly associated with the phenotypes.
This project aims to investigate and develop optimal wavelet transformation and thresholding methods for improved prediction of phenotypes. Although this project does not have dedicated funding, all successful applicants without funding will be considered for a fully-funded scholarship, in an open competition across the entire School of Maths.
Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1), and/or a Master's degree in a relevant engineering or science degree such as (but not limited to) mathematics and statistics.
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
How to apply
Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is the 'Phenotypic prediction based on multiscale copy number alteration profiles’ as well as Dr Stuart Barber as your proposed supervisor.
We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.