Williams, Marc J;
(2019)
Methods and practice of detecting selection in human cancers.
Doctoral thesis (Ph.D), UCL (University College London).
Preview |
Text
Main.pdf - Accepted Version Download (26MB) | Preview |
Abstract
Cancer development and progression is an evolutionary process, understanding these evolutionary dynamics is important for treatment and diagnosis as how a cancer evolves determines its future prognosis. This thesis focuses on elucidating selective evolutionary pressures in cancers and somatic tissues using population genetics models and cancer genomics data. First a model for the expected diversity in the absence of selection was developed. This neutral model of evolution predicts that under neutrality the frequency of subclonal mutations is expected to follow a power law distribution. Surprisingly more than 30% of cancer across multiple cohorts fitted this model. The next part of the thesis develops models to explore the effects of selection given these should be observable as deviations from the neutral prediction. For this I developed two approaches. The first approach investigated selection at the level of individual samples and showed that a characteristic pattern of clusters of mutations is observed in deep sequencing experiments. Using a mathematical model, information encoded within these clusters can be used to measure the relative fitness of subclones and the time they emerge during tumour evolution. With this I observed strikingly high fitness advantages for subclones of above 20%. The second approach enables measuring recurrent patterns of selection in cohorts of sequenced cancers using dN/dS, the ratio of non-synonymous to synonymous mutations, a method originally developed for molecular species evolution. This approach demonstrates how selection coefficients can be extracted by combining measurements of dN/dS with the size of mutational lineages. With this approach selection coefficients were again observed to be strikingly high. Finally I looked at population dynamics in normal colonic tissue given that many mutations accumulate in physiologically normal tissue. I found that the current view of stem cell dynamics was unable to explain sequencing data from individual colonic crypts. Some new models were proposed that introduce a longer time scale evolution that suppresses the accumulation of mutations which appear consistent with the data.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Methods and practice of detecting selection in human cancers |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/ 4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068934 |
Archive Staff Only
 |
View Item |
