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Quantitative fitness modelling in cancer using single cell timeseries population dynamics

University of British Columbia

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 breast cancer (TNBC) tumour is quantifiably reproducible, and that (ii) the fitness landscape is reversed under early response to cisplatin treatment. Our results show that population genetic modelling of timeseries tumour measurements to predict clonal evolution is tractable. Further study with timeseries modelling will provide insight into therapeutic strategies promoting early intervention, drug combinations and evolution-aware approaches to clinical management.

Text

2022-02-28

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/77284

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/