Accuracy of somatic variant detection in multiregional tumor sequencing data

Detering, Harald; Tomás, Laura; Prieto, Tamara; Posada, David

doi:10.1101/655605

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Within-organism cancer evolution is increasingly being studied using population genetics approaches, including phylogenetics (Navin et al 2011; Yuan et al 2015; Alves et al 2017; Schwartz et al 2017; Caravagna et al 2018; Singer et al 2018; Alves et al 2019; Caravagna et al 2019; Detering et al 2019; Malikic et al 2019; Werner et al 2019; Kuipers et al 2020), to understand molecular dynamics of cancer cell populatons. These approaches have shown promise to be developed into therapeutic applications in the personalized medicine framework (Gerlinger et al 2012; Abbosh et al 2017; Rao et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

Moravec

Lanfear

Spector

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Within-organism cancer evolution is increasingly being studied using population genetics approaches, including phylogenetics (Alves et al, 2019 ; Alves et al, 2017 ; Caravagna et al, 2020 ; Caravagna et al, 2018 ; Detering et al, 2019 ; Kuipers et al, 2020 ; Malikic et al, 2019 ; Navin et al, 2011 ; Singer et al, 2018 ; Schwartz and Schäffer, 2017 ; Werner et al, 2020 ; Yuan et al, 2015 ), to understand evolutionary dynamics of cancer cell populations. These approaches have shown promise to be developed into therapeutic applications in the personalized medicine framework (Abbosh et al, 2017 ; Gerlinger et al, 2012 ; Rao et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Moravec

Lanfear

Spector

et al. 2023

Journal of Computational Biology

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Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (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 article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. 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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“…2.1. For these multi-regional mutation calling methods, comprehensive performance evaluations were conducted in recent reports [15].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples

Moriyama

Imoto

Miyano

et al. 2019

Mathematical and Computational Oncology

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We propose a Bayesian method termed MultiMuC for accurate detection of somatic mutations (mutation call) from multi-regional tumor sequence data sets. To improve detection performance, our method is based on the assumption of mutation sharing: if we can predict at least one tumor region has the mutation, then we can be more confident to detect a mutation in more tumor regions by lowering the original threshold of detection. We find two drawbacks in existing methods for leveraging the assumption of mutation sharing. First, existing methods do not consider the probability of the "No-TP (True Positive)" case: we could expect mutation candidates in multiple regions, but actually, no true mutations exist. Second, existing methods cannot leverage scores from other state-of-the-art mutation calling methods for a single-regional tumor. We overcome the first drawback through evaluation of the probability of the No-TP case. Next, we solve the second drawback by the idea of Bayes-factor-based model construction that enables flexible integration of probability-based mutation call scores as building blocks of a Bayesian statistical model. We empirically evaluate that our method steadily improves results from mutation calling methods for a singleregional tumor, e.g., Strelka2 and NeuSomatic, and outperforms existing methods for multi-regional tumors through a real-data-based simulation study. Our implementation of MultiMuC is available at https://github. com/takumorizo/MultiMuC.

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Accuracy of somatic variant detection in multiregional tumor sequencing data

Cited by 4 publications

References 37 publications

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

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

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples

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