ArticleNature communications2019
Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
- Statistical Inconsistency of Error-correction Objectives for Perfect Phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.bioRxiv : the preprint server for biology · 2026Article
- GATES: A Lightweight Tool Automating Pathogenic Variant Discovery From Raw Whole-Exome Sequencing Data.Cancer informatics · 2026Article
- Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.Statistics in biosciences · 2026Article
- scDNA: Single Cell DNA analysis software toolkit for subclonality discovery and assessment.bioRxiv : the preprint server for biology · 2025Article
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- DelSIEVE: cell phylogeny modeling of single nucleotide variants and deletions from single-cell DNA sequencing data.Genome biology · 2025Article
- Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Advances in tumor subclone formation and mechanisms of growth and invasion.Journal of translational medicine · 2025Review
- Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq.Bioinformatics advances · 2025Article
- scMitoMut for calling mitochondrial lineage-related mutations in single cells.Briefings in bioinformatics · 2024Article
- Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope.Nature communications · 2024Article
- Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma.bioRxiv : the preprint server for biology · 2024Article
- Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data.Bioinformatics (Oxford, England) · 2024Article
- Article
- Computational immunogenomic approaches to predict response to cancer immunotherapies.Nature reviews. Clinical oncology · 2024Review
- Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction.Bioinformatics advances · 2024Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees.
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