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Statistical Underpinning of Cancer Mutational Signature Analyses

Project Description

The existence of context-specific DNA mutational signatures as a response to carcinogens has been studied for at least three decades [e.g. 1,2]. Genome-wide mutational signatures have been at the forefront of cancer research for the past seven years, driven largely by one prominent approach [3,4]. The idea that one can take the mutational profile of a sample and infer the processes that have acted on the tumour over time is an extremely attractive one.

Recent studies have used these methods to identify biological processes contributing to tumourigenesis [5], to confirm links with risk factors [6], to identify patterns associated with rare germline mutations [7], to identify patients with high immune-cell infiltration and good prognosis[8], and to classify patients so as to suggest potential treatment paths [9] to mention but a few.

We can consider the problem generally as one of decomposing a matrix of mutations M into W x S where S is a matrix of mutational signatures (each column of which we interpret as representing the effect of a real physical process e.g. Smoking, UV light exposure) and W a matrix of the weights of each signature in each sample. Errors/uncertainty can arise from the observations in M, the choice of underlying model, non-identifiability, or through mis-specifying/estimating one or other of W or S.

Typically, we require any important statistic presented in clinical research to be accompanied by a measure of confidence. This has not been the case for mutational signature analyses where useful indicators of uncertainty have been lacking. Consequently, there is a risk that inaccurate and misleading results may find their way into the literature. This project will seek to remedy this situation, developing methods for quantifying and displaying uncertainty in signature analyses, while exploring alternative approaches to inferring mutational signatures.

Funding Notes

This project is supported by a three-year studentship (fees + stipend) from the Melville Trust.

Applicants should have a good first degree, or Master’s degree, in mathematics, statistics or similar. Experience of coding in R would be advantageous, as would a demonstrable interest in medical statistics or molecular biology.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website


References (Pubmed IDs):
[1] Proc Natl Acad Sci U S A. 1991 Nov 15;88(22):10124-8. (PMID 1946433),
[2] Oncogene. 2002 Oct 21;21(48):7435-51 (PMID 12379884),
[3] Cell. 2012 May 25;149(5):979-93 (PMID 22608084),
[4] Nature. 2013 Aug 22;500(7463):415-21 (PMID 23945592),
[5] J Neurosurg. 2019 Apr 5:1-12 (PMID 30952131),
[6] J Dent Res. 2019 Jun;98(6):652-658 (PMID 30917298),
[7] Hum Mutat. 2019 Jan;40(1):36-41 (PMID 30362666),
[8] Nat Commun. 2016 Sep 26;7:12910 (PMID 27666519),
[9] Nat Genet. 2016 Oct;48(10):1131-41 (PMID 27595477)

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

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