Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

Abstract

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.

Methods for copy number aberration detection from single-cell DNA-sequencing data

“…Dorri et al [84] developed a method for inferring phylogenetic trees from scDNAseq copy number profiles by assuming the perfect phylogeny not on the copy numbers but rather on the breakpoints. Furthermore, SCICoNE [15] estimates a tree in addition to the inferred CNAs.…”

CONET: Copy number event tree model of evolutionary tumor history for single-cell data

“…To our knowledge, CONET is the first Bayesian probabilistic approach for copy number evolution inference and copy number calling, that fully exploits the scDNAseq readouts, in the form of both per-breakpoint and the per-bin data. CONET differs from other recent evolutionary models of breakpoints or copy number events: the model of [48], MEDALT [49] and SCICoNE [50]. For the trees inferred by [48] or MEDALT, the nodes do not correspond to copy number events.…”

“…CONET differs from other recent evolutionary models of breakpoints or copy number events: the model of [48], MEDALT [49] and SCICoNE [50]. For the trees inferred by [48] or MEDALT, the nodes do not correspond to copy number events. Each node of [48] tree corresponds to acquisition of only a single breakpoint.…”

“…Recent approaches analyzing copy number changes in single cells from an evolutionary perspective aim at either improving breakpoint and copy number calling from scDNA-seq based on breakpoint trees [48], or modeling cell lineages based on the similarities of copy number profiles between single cells and identifying events carrying fitness advantage [49], or inference of copy number evolution and copy number calling from the binned read counts [50]. None of these approaches, however, infers copy number event trees or identifies copy number states from both per-bin and per-breakpoint data.…”

PhylEx: Accurate reconstruction of clonal structure via integrated analysis of bulk DNA-seq and single cell RNA-seq data

“…Future extensions of PhylEx to characterize clones by both somatic mutations and copy number profiles have the potential for detecting subclonal copy number information. Inferring subclonal copy numbers is inherently challenging to achieve using only the bulk sequencing data and is only currently feasible using specialized sequencing techniques such as DLP WGS on single cells [32,14,33]. PhylEx represents substantial progress in reconstructing the full evolutionary trajectory of cancer.…”