Predicting clone genotypes from tumor bulk sequencing of multiple samples

Miura, Sayaka; Gómez, Karen; Murillo, Óscar; Huuki, Louise A; Vu, Tracy; Buturla, Tiffany; Kumar, Sudhir

doi:10.1101/341180

Cited by 2 publications

(11 citation statements)

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“…We begin with results for G7 and G12 datasets that were modeled after the predicted evolutionary histories of two patients (EV005 and RK26, respectively) ( Fig. 1a-1d ) [35, 44]. Each tumor sample may contain one or a few evolutionarily closely-related clones, assuming a localized genetic heterogeneity [4, 6], i.e., migration of cancer cells to another section of a tumor was assumed to be rare.…”

Section: Resultsmentioning

confidence: 99%

“…Also, most methods are known not to be robust to the presence of incorrect SNV assignments, so one should proceed with extreme caution when analyzing datasets with high rates of sequence error. For example, LICHeE may fail to produce any inferences on such datasets or the accuracy may become much lower than other methods (e.g., Treeomics) [35]. LICHeE failed to produce any results for our example empirical dataset [30].…”

Section: Discussionmentioning

confidence: 99%

“…( c and d ) A phylogeny and clone frequencies of twelve clones and eleven tumor samples (T1-T11) derived from RK26 tree (G12 datasets) [44]. ( e and f ) One of thirty phylogenies and its tumor composition from P10 datasets [35]. ( g and h ) One example of MA datasets (out of the 60) with primary tumor (PSec1 and PSec2) and metastatic tumors (M1-M5) [13].…”

Section: Introductionmentioning

confidence: 99%

“…LICHeE generates SNV clusters defined by the pattern of presence and absence of SNVs among tumor samples while considering SNV frequencies [34]. CloneFinder reconstructs ancestral clones in predicting clone genotypes [35]. Treeomics examines the presence and absence of SNVs among tumor samples and resolves evolutionarily incompatible patterns when decomposing SNV profiles into clone genotypes [36].…”

Section: Introductionmentioning

confidence: 99%

“…Simulated data included small and large numbers of persistent ancestral clones and metastatic tumors that arise from polyclonal seeding events. Our assessments are based on simulation studies because correct phylogenies are known, and computer simulation has emerged as a standard approach for evaluating the performance of statistical methods in cancer genomics [34, 35, 37, 42]. In this study, we identify and highlight the limitations of methods that can most accurately infer clone phylogenies.…”

Section: Introductionmentioning

confidence: 99%

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

Miura

Deng

et al. 2019

Preprint

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2 3 4 5 Power and pitfalls of computational methods for inferring clone phylogenies and mutation 6 orders from bulk sequencing data 7 8 9 Abstract 29Background. Tumors harbor extensive genetic heterogeneity in the form of distinct clone 30 genotypes that arise over time and across different tissues and regions of a cancer patient. Many 31 computational methods produce clone phylogenies from population bulk sequencing data 32 collected from multiple tumor samples. These clone phylogenies are used to infer mutation order 33 and clone origin times during tumor progression, rendering the selection of the appropriate clonal 34 deconvolution method quite critical. Surprisingly, absolute and relative accuracies of these 35 methods in correctly inferring clone phylogenies have not been consistently assessed. 36Methods. We evaluated the performance of seven computational methods in producing clone 37 phylogenies for simulated datasets in which clones were sampled from multiple sectors of a 38 primary tumor (multi-region) or primary and metastatic tumors in a patient (multi-site). We 39 assessed the accuracy of tested methods metrics in determining the order of mutations and the 40 branching pattern within the reconstructed clone phylogenies. 41Results. The accuracy of the reconstructed mutation order varied extensively among methods 42 (9% -44% error). Methods also varied significantly in reconstructing the topologies of clone 43 phylogenies, as 24% -58% of the inferred clone groupings were incorrect. All the tested methods 44 showed limited ability to identify ancestral clone sequences present in tumor samples correctly. 45The occurrence of multiple seeding events among tumor sites during metastatic tumor evolution 46 hindered deconvolution of clones for all tested methods. 47 Conclusions.Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall 48 accuracy, but none of the methods performed well for all simulated datasets and conditions. 49 50 Background 52Somatic mutations play a crucial role in cancer progression [1][2][3]. Early models proposed that 53 clones with driver mutations sweep through the population, which is called a linear progression of 54 clone evolution [4]. Now, it is clear that tumors are not monoclonal, and that the clonal evolution 55generally follows a branching model (i.e., incomplete clonal sweep) even within a tumor [4][5][6][7][8][9][10]. 56Similarly, metastatic tumors also follow a branching pattern [11, 12]. Clones found in primary and 57 metastatic tumors show inter-and intra-tumor evolutionary relationships, which can be 58represented by a single-patient clone phylogeny [13-16] (e.g., Fig. 1g and 1h). The reconstruction 59 and analysis of clone phylogenies have become standard practices in cancer genomics [16][17][18][19][20][21][22][23][24][25][26]. 60Clone phylogenies are most often inferred using bulk sequencing data [16,[27][28][29][30]. Bulk 61 sequencing of tumor samples is cost effective and can accurately identify single nucleotide 62 variants (SNVs) [31, 32]. The result...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Miura

Deng

et al. 2019

Preprint

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Testing for phylogenetic signal in single-cell RNA-seq data

Moravec

Lanfear

Spector

et al. 2021

Preprint

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Phylogenetic methods are emerging as an useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing (WGS) or single-cell DNA sequencing (scDNA-seq) and are based on calling copy number alterations and single nucleotide variants (SNVs). Here we explore the potential of single-cell RNA sequencing (scRNA-seq) to reconstruct cancer evolutionary dynamics. scRNA-seq is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this paper, we demonstrate for the first time that scRNA-seq data contains sufficient evolutionary signal and can be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from our scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Without an explicit error model, standardized expression values appears to be more powerful and informative than the SNV values at a lower computational cost, due to being a by-product of standard expression analysis. Our results suggest that scRNA-seq can be a competitive alternative or useful addition to conventional scDNA-seq phylogenetic reconstruction. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

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Predicting clone genotypes from tumor bulk sequencing of multiple samples

Cited by 2 publications

References 39 publications

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

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

Testing for phylogenetic signal in single-cell RNA-seq data

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