ArticleBioinformatics (Oxford, England)2021
Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- 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
- Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models.Genome research · 2023Article
- Recent Advances in High-sensitivityActa histochemica et cytochemica · 2023Review
- Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.Bioinformatics (Oxford, England) · 2022Article
- CircularBioengineered · 2022Article
- Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Article
- Assessing the contribution of tumor mutational phenotypes to cancer progression risk.PLoS computational biology · 2021Article
- Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.Bioinformatics (Oxford, England) · 2020Article
- Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases.Frontiers in physiology · 2020Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 3 countries.
Funding
Abstract
motivationComputational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate.
resultsIn this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.