Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNA), has been impeded by lack of time series single cell sampling of polyclonal populations and temporal statistical models [1][2][3][4][5][6][7] . Here, we generated 42,000 genomes from multi-year time series single cell whole genome sequencing (scWGS) of breast epithelium †
A new generation of scalable single cell whole genome sequencing (scWGS) methods [Zahn et al., 2017, Laks et al., 2019, allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cells populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing mutational processes. The ability to sequence tens of thousands of single genomes at high resolution per experiment [Laks et al., 2019] is challenging the assumptions and scalability of existing phylogenetic tree building methods and calls for tailored phylogenetic models and scalable inference algorithms. We propose a phylogenetic model and associated Bayesian inference procedure which exploits the specifics of scWGS data. A first highlight of our approach is a novel phylogenetic encoding of copy-number data providing an attractive statistical-computational trade-off by simplifying the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. A second highlight is an innovative phylogenetic tree exploration move which makes the cost of MCMC iterations bounded by O(|C| + |L|), where |C| is the number of cells and |L| is the number of loci. In contrast, existing off-the-shelf likelihood-based methods incur iteration cost of O(|C| |L|). Moreover, the novel move considers an exponential number of neighbouring trees whereas offthe-shelf moves consider a polynomial size set of neighbours. The third highlight is a novel * Equal contribution 1 mutation calling method that incorporates the copy-number data and the underlying phylogenetic tree to overcome the missing data issue. This framework allows us to realistically consider routine Bayesian phylogenetic inference at the scale of scWGS data.
Bouchard-Côté (2023) Cancer phylogenetic tree inference at scale from 1000s of single cell genomes , Peer Community Journal, 3: e63.
1Tumour fitness landscapes underpin selection in cancer, impacting etiology, evolution and 28 response to treatment. Progress in defining fitness landscapes has been impeded by a lack 29 of timeseries perturbation experiments over realistic intervals at single cell resolution. We 30 studied the nature of clonal dynamics induced by genetic and pharmacologic perturbation 31 with a quantitative fitness model developed to ascribe quantitative selective coefficients to 32 individual cancer clones, enable prediction of clone-specific growth potential, and forecast 33 competitive clonal dynamics over time. We applied the model to serial single cell genome 34 (>60,000 cells) and transcriptome (>58,000 cells) experiments ranging from 10 months to 2.5 35 years in duration. We found that genetic perturbation of TP53 in epithelial cell lines induces 36 multiple forms of copy number alteration that confer increased fitness to clonal populations 37 with measurable consequences on gene expression. In patient derived xenografts, predicted 38 selective coefficients accurately forecasted clonal competition dynamics, that were validated 39 with timeseries sampling of experimentally engineered mixtures of low and high fitness 40 clones. In cisplatin-treated patient derived xenografts, the fitness landscape was inverted in a 41 time-dependent manner, whereby a drug resistant clone emerged from a phylogenetic lineage 42 of low fitness clones, and high fitness clones were eradicated. Moreover, clonal selection 43 mediated reversible drug response early in the selection process, whereas late dynamics in 44 genomically fixed clones were associated with transcriptional plasticity on a fixed clonal 45 genotype. Together, our findings outline causal mechanisms with implication for interpreting 46 how mutations and multi-faceted drug resistance mechanisms shape the etiology and cellular 47 49 Cellular fitness underpins the tissue population dynamics of cancer progression and treatment 50 response. Yet, quantifying fitness in heterogeneous cell populations, and identifying causal 51 mechanisms shaping fitness landscapes remain as open problems, impeding progress in developing 52 effective and durable therapeutic strategies. In particular, quantitative fitness modeling of cancer 53 cells has numerous and diverse implications; attributing clonal dynamics to drift or selection, 54 identifying the determinants of clonal expansion, enabling causal inference, and forecasting growth 55 trajectories. Drug resistance and etiology are among the key unresolved areas of investigation that 56 require advanced understanding of fitness in cancer. For example, drug resistance mechanisms are 57 commonly attributed to phenotypic plasticity encoded via epigenetic changes 1, 2 , or evolutionary 58 selection of pre-existing genomic clones 3 . However, the relative contribution of these processes 59 when studied in tandem is poorly understood and requires integrated genome-transcriptome 60 investigation. Moreover, how changes in genomic architecture brought ...
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