Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data

Abstract

tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.www.nature.com/scientificreports www.nature.com/scientificreports/ ignoring SNV frequencies by using a heuristic algorithm based on co-comparability graphs 38 . It addresses the minimum conflict-free row split problem, where row is tumor genotypes, and observed tumor genotypes are split into clone genotypes. Ultimately, all of these methods deconvolute individual clones from population bulk sequencing of multiple tumor samples acquired over time and/or different locations in a patient.Surprisingly, absolute and relative accuracies of clone phylogenies produced by these computational methods have not been assessed using the same collection of datasets, i.e., their performances are yet to be benchmarked. Such benchmarking is critical because of the biological relevance of the downstream inferences derived by using the results produced by these methods. For example, the accuracies of the order of driver mutations and the interrelationship of clones depend on the performance of current methods in accurately deconvoluting individual clone genotypes and reconstructing evolutionary events 13,34,36 . Accurate clone phylogenies are also critical for inferring migration paths. No previous study has evaluated the relative accuracy of clone phylogenies, because their focus has been on introducing and assessing the strengths of the new clone prediction method proposed 13,34-39 . Besides, the robustness of these computational methods to the complexity of clonal structures and evolutionary histories from different tumor sites is mostly unknown.Therefore, we evaluated the accuracy of clone phylogenies produced by seven methods t...

Profiles of expressed mutations in single cells reveal subclonal expansion patterns and therapeutic impact of intratumor heterogeneity

“…This is also the case when n = 1000 and m = 300 when false negative rate is 0.05, however PhISCS fails to report solutions for some instances when false negative rate is 0.2. When m = 1000 the performance of SCITE (with a 24-hour time limit) under both measures falls substantially 4 Examples include the number of ancestor-descendant and different-lineage relationships shared across the trees compared, as well as more sophisticated measures such as the Multi-Labeled Tree Edit Distance and others [24,39,40,41,42]. 5 which is important especially since many methods build trees based on mutations shared by at least two cells or pre-cluster cells based on their mutational profiles 6 Another parameter used in our simulations is the missing value rate γ, which determines the fraction of entries in the genotype matrix which are unknown.…”

“… 3 Examples include the number of ancestor-descendant and different-lineage relationships shared across the trees compared, as well as more sophisticated measures such as the Multi-Labeled Tree Edit Distance and others [23, 37, 38, 39, 40]. …”

Single-cell tumor phylogeny inference with copy-number constrained mutation losses

“…The evolution of a tumor is typically described by a phylogenetic tree, or phylogeny, whose leaves represent the cells observed at the present time and whose internal nodes represent ancestral cells. Tumor phylogenies are challenging to reconstruct using DNA sequencing data from bulk tumor samples, since this data contains mixtures of mutations from thousandsmillions of heterogeneous cells in the sample [3][4][5][6][7][8][9][10][11][12][13][14][15] . Recently, single-cell DNA sequencing (scDNA-seq) of tumors has become more common, and new technologies such as those from 10X Genomics 16…”

Phylomedicine of mutational processes in somatic cancer cell populations

“…By the way, in the above, we assumed that the clone phylogeny is known. In empirical data analysis, one needs to generate it using available computational tools for bulk and single-cell sequencing methods; see reviews in the accuracy of methods (Miura et al, 2018a; Miura et al, 2018b; Miura et al, 2020). The errors in the collection of variants for each branch will lead to false-negative detection of signatures due to diluted signals caused by incorrect variants and correct variants that are not assigned to a branch.…”

“…Tumor cells accumulate somatic mutations during cancer progression, in which cells exhibit dynamic demography, including emergence, expansion, and extinction (Bailey et al, 2018; Martincorena & Campbell, 2015). Through the analysis of genomic variation, researchers now routinely reconstruct mutational histories and phylogenies of clones (Brown et al, 2017; El-Kebir et al, 2018; Miura et al, 2020; Turajlic et al, 2018; Zhao et al, 2016). In a clone phylogeny, variants can be localized to individual branches and relative frequencies of different variant types compared across lineages to detect shifts in cellular mutational processes over time.…”