ArticleJournal of computational biology : a journal of computational molecular cell biology2018
Phylogenetic Copy-Number Factorization of Multiple Tumor Samples.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Trial
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.Nature methods · 2024Article
- CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.Genome biology · 2023Article
- Structurally Complex Osteosarcoma Genomes Exhibit Limited Heterogeneity within Individual Tumors and across Evolutionary Time.Cancer research communications · 2023Article
- Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.Bioinformatics (Oxford, England) · 2022Article
- CONET: copy number event tree model of evolutionary tumor history for single-cell data.Genome biology · 2022Article
- Parsimonious Clone Tree Integration in cancer.Algorithms for molecular biology : AMB · 2022Article
- Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL.Nature biotechnology · 2021Article
- Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data.Nature communications · 2020Article
- Copy number evolution with weighted aberrations in cancer.Bioinformatics (Oxford, England) · 2020Article
- Identification of conserved evolutionary trajectories in tumors.Bioinformatics (Oxford, England) · 2020Article
- SCARLET: Single-cell tumor phylogeny inference with copy-number constrained mutation losses.Cell systems · 2020Article
- Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Cancer biology as revealed by the research autopsy.Nature reviews. Cancer · 2019Review
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Authors and funding
4 authors.
Funding
Abstract
Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.