Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations

Abstract

Motivation Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. Results In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. Availability and implementation https://github.com/CMUSchwartzLab/TUSV-ext. Supplementary information Supplementary data are available at Bioinformatics online.

Determining Optimal Placement of Copy Number Aberration Impacted Single Nucleotide Variants in a Tumor Progression History

“…However, neither of these methods infers a tumor progression tree; rather, they cluster variants and correct for CNAs without imposing a joint tree structure on all variants. On the other hand, the group of methods that integrate SNVs and CNAs in inferring a progression tree typically focus on a small number of variants or clones [7,8] or assume restrictive set of possible relationships between SNVs and CNAs [5].…”

“…However, most of these methods such as PhyloSub [11], LiCHeE [25], CITUP[17], AncesTree [6], PASTRI [29], CALDER [21], MIPUP [10] and Pairtree [34] are primarily designed for somatic single nucleotide variants (SNVs) located in genomic loci not impacted by copy number aberrations (CNAs). Although there have been some developments in the design of computational methods that account for SNVs from copy number altered mutational loci (such as SPRUCE [7]), these methods either operate on a small set of variants and/or (sub)clones [7,8] or make restrictive assumptions about the possible relationships between SNVs and copy number gains and losses [5]. More recently, PACTION [28], a method for integration of trees independently derived by the use of CNAs (CNA-tree) and SNVs (SNV-tree) was proposed.…”

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

“…The bulk of the method is dedicated to solving for a constrained optimization problem, detailed in the subsequent sections. The method is adapted from the earlier TUSV-ext method for building SNV/CNA/SV phylogenies from bulk DNA-seq data (11), itself extended from the earlier CNA/SV TUSV method (9). We describe the full program below but with an emphasis on areas in which Sc-TUSV-ext differs from bulk TUSV-ext.…”

“…Phylogenetic and ancestry constraints: As the copy number profiles of the aggregated clones are associated with a underlying phylogenetic tree, we impose phylogenetic constraints on the rows of the C est matrix as described in Fu et al (2022) (11), and Eaton et al (2018) (9). We define an n × n edge matrix E and n × n ancestry matrix A, where n = 2n ′ + 1, to define a binary tree T .…”

Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

“…Although phylogeny inference methods from bulk sequencing and cell clustering from single-cell RNA sequencing are expanding to incorporate both SNV and CNA features, such as TUSV-ext [10] and CA-SIC [11], current methods for tumor phylogeny and/or clone inference from single-cell sequencing naturally tend to focus on the features (SNV or CNA events) for which the data is ideally suited [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. One exception in the medium to high coverage scDNA-seq regime is SCARLET [27], which refines a given copy number tree using SNV read counts under a CNA loss supported evolutionary model.…”