Tran, Hoang Son;
(2023)
Integrating multi-omics data by mapping subclonal events on tumour evolutionary trees.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Inferring the tumour’s evolutionary history is crucial for unravelling the intricate landscape of intratumour heterogeneity underlying cancer progression. Several bioinformatics tools have been designed for deciphering the subclonal population of the heterogeneous tumour mass. However, most of them rely on single-omics analysis and methods for integrating multi-omics data in the context of tumour evolutionary trees are still lacking. In this thesis, the development of MAPping SubClonal Events (MAPSCE), a new tool for mapping of subclonal events on tumour evolutionary trees, is described. This method allows for integration of multi-omics data in multisample cancer evolutionary studies. In essence, MAPSCE implements a branch test where quadratic programming is applied to every branch of a patient tumour tree to find the best mapping branch (including the root). Each solution translates into a Bayesian Information Criterion value, and Bayes factors for model selection. MAPSCE has been released as an R package. Multiple datasets with different types of copy number events and varying degrees of noise up to ±30% were simulated to assess the reliability of the tool. For losses of haploid genes, MAPSCE was benchmarked against a tool of similar functionality, LOHHLA, showing both an increase in specificity and sensitivity. This comparison was not possible for other types of copy number events as MAPSCE is the only tool to date with the ability to map these. Lastly, MAPSCE’s potential applications were demonstrated in several analyses of multi-region, multi-omics datasets. Subclonal biallelic inactivation of tumour suppressor genes on subclonal level was identified in lung cancer patients. Subclonal changes of gene expression were further compared against subclonal copy number events to infer cases of copy number dependent or independent allele specific expression. This work provides an innovative way to integrate multi-omics data in multisample cancer studies, refining the study of evolutionary processes underlying intratumour heterogeneity.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Integrating multi-omics data by mapping subclonal events on tumour evolutionary trees |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. 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 > 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 Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10180204 |
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