Quantitative fitness modelling in cancer using single cell timeseries population dynamics
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Salehi, Sohrab
issued: 2022-02-28T08:00:00Z
issued: 2021
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Tumour fitness landscapes underpin selection in cancer, impacting evolution and response to treatment. Quantitative fitness modelling of cancer cells has numerous and diverse implications: attributing clonal dynamics to drift or selection, identifying the determinants of clonal expansion, and forecasting tumour growth trajectories. Why and how drug resistance evolves is among the key unresolved areas of investigation that require advanced understanding of fitness in cancer. Longitudinal xenoengraftment interrogated via next generation single cell sequencing (SCS) has enabled more accurate, quantitative measurements of tumours as they evolve. This process generates timestamped samples comprising thousands of cells each measured at thousands of copy number aberrations (CNA). Major analytical challenges introduced by this new datatype include (i) how to identify biologically meaningful groups of cells (i.e., clones) across multiple timepoints, and (ii) how to quantitatively reason about the underlying evolutionary forces acting on the clones via their observed dynamics. To address the first problem, we describe and provide supplementary tools for sitka, a scalable Bayesian phylogenetic inference method in Chapter 2. It resolves the clonal structure of a heterogeneous tumour cell population sampled over multiple timepoints by reconstructing the evolutionary relationship between single cells from their inferred CNA profiles. We then develop Lumberjack, a tree-cutting algorithm, and use it to assign cells to clones. We address the second problem in Chapter 3 by developing fitClone, a Bayesian probabilistic framework that ascribes quantitative selection coefficients to individual cancer clones and forecasts competitive clonal dynamics over time. In Chapter 4, we exemplify the computational models introduced above on real-world data collected from cancer cells over a multi-year period to verify two key hypotheses, that (i) clonal dynamics in a pre-treatment triple negative …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77284
note: Quantitative fitness modelling incancer using single cell timeseriespopulation dynamicsbySohrab SalehiB.Sc., Amirkabir University of Technology, 2011M.Sc., The University of British Columbia, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Bioinformatics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)February 2021© Sohrab Salehi 2021The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the thesis entitled:Quantitative fitness modelling in cancer using single cell timeseries population dynam-icssubmitted by Sohrab Salehi in partial fulfillment of the requirements for the degree of Doctorof Philosophy in Bioinformatics.Examining Committee:Sohrab Shah, Department of Pathology and Laboratory Medicine, UBCCo-supervisorAlexandre Bouchard-Coˆte´, Department of Statistics, UBCCo-supervisorSamuel Aparicio, Department of Molecular Oncology, UBCSupervisory Committee MemberRyan Brinkman, Department of Medical Genetics, UBCUniversity ExaminerGeoffrey Schiebinger, Department of Mathematics, UBCUniversity ExaminerAdditional Supervisory Committee Members:Sarah Otto, Department of Zoology, UBCSupervisory Committee MemberiiAbstractTumour fitness landscapes underpin selection in cancer, impacting evolution and response to treat-ment. Quantitative fitness modelling of cancer cells has numerous and diverse implications: at-tributing clonal dynamics to drift or selection, identifying the determinants of clonal expansion,and forecasting tumour growth trajectories. Why and how drug resistance evolves is among thekey unresolved areas of investigation that require advanced understanding of fitness in cancer.Longitudinal xenoengraftment interrogated via next generation single cell sequencing (SCS) hasenabled more accurate, quantitative measurements of tumours as they evolve. This process gen-erates timestamped …
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0395858
language: eng
degree discipline: Bioinformatics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77284