Improving computational tumor ploidy estimation in complex cancer genomes through flow-cytometry-guided calibration
11 affiliations
- 1Research Department of Pathology, Cancer Institute, University College London, London, UK
- 2University College London Medical School, London, UK
- 3European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- 4Department of Pathology, Royal National Orthopaedic Hospital, Stanmore, UK
- 5IRIBHM Jacques E. Dumont, Université Libre de Bruxelles, Brussels, Belgium
- 6University of California, San Diego, San Diego, CA, USA
- 7Research Department of Oncology, University College London, London, UK
- 8Flow Cytometry TTP, Cancer Institute, University College London, London, UK
- 9Wellcome Sanger Institute, Cambridge, UK
- 10Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- 11Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Abstract
DNA ploidy is an important predictor of tumor behavior and prognosis, and its accurate estimation is essential for robust genomic analysis in translational cancer research and diagnostics. However, the most common in silico methods for ploidy estimation using next-generation-sequencing-based copy-number aberration (CNA)-calling algorithms are often inaccurate due to inherent ambiguity in fitting ploidy solutions. This study evaluates the accuracy of state-of-the-art CNA callers using whole-genome sequencing by comparing their ploidy estimates with gold-standard ploidy measurements derived by flow cytometry (FC). We demonstrate that CNA callers are up to 38% inaccurate in cancers with complex genomes, which impacts the accurate estimation of copy number of cancer genes and could have clinical implications and impacts on inferences of tumor evolution. Critically, flow-cytometry-based calibration of CNA callers yields highly accurate ploidy estimates (ρPearson = 0.92, p < 0.001), providing a robust solution to the substantial inaccuracies that compromise clinical decision-making and evolutionary inference in complex cancers.
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BibTeX
@article{butters2026improving,
title = {{Improving computational tumor ploidy estimation in complex cancer genomes through flow-cytometry-guided calibration}},
author = {Thomas Butters and Dahmane Oukrif and Punn Tannirandorn and Ianthe AEM van Belzen and Jianan Chen and Christopher Davies and Runtian Lin and Solange De Noon and Dalil Taher and Chloe Cutler and Mark Markov and Anna Tollit and Fatine Oumlil and Christopher D. Steele and Matthew Fittall and Yanping Guo and Isidro Cortés-Ciriano and Peter Van Loo and Adrienne M. Flanagan and Nischalan Pillay and Maxime Tarabichi},
journal = {Cell Reports Methods},
year = {2026},
month = aug,
doi = {10.1016/j.crmeth.2026.101570},
url = {https://doi.org/10.1016/j.crmeth.2026.101570},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Improving computational tumor ploidy estimation in complex cancer genomes through flow-cytometry-guided calibration
AU - Thomas Butters
AU - Dahmane Oukrif
AU - Punn Tannirandorn
AU - Ianthe AEM van Belzen
AU - Jianan Chen
AU - Christopher Davies
AU - Runtian Lin
AU - Solange De Noon
AU - Dalil Taher
AU - Chloe Cutler
AU - Mark Markov
AU - Anna Tollit
AU - Fatine Oumlil
AU - Christopher D. Steele
AU - Matthew Fittall
AU - Yanping Guo
AU - Isidro Cortés-Ciriano
AU - Peter Van Loo
AU - Adrienne M. Flanagan
AU - Nischalan Pillay
AU - Maxime Tarabichi
PY - 2026
DA - 2026/08/31
JO - Cell Reports Methods
DO - 10.1016/j.crmeth.2026.101570
UR - https://doi.org/10.1016/j.crmeth.2026.101570
AB - DNA ploidy is an important predictor of tumor behavior and prognosis, and its accurate estimation is essential for robust genomic analysis in translational cancer research and diagnostics. However, the most common in silico methods for ploidy estimation using next-generation-sequencing-based copy-number aberration (CNA)-calling algorithms are often inaccurate due to inherent ambiguity in fitting ploidy solutions. This study evaluates the accuracy of state-of-the-art CNA callers using whole-genome sequencing by comparing their ploidy estimates with gold-standard ploidy measurements derived by flow cytometry (FC). We demonstrate that CNA callers are up to 38% inaccurate in cancers with complex genomes, which impacts the accurate estimation of copy number of cancer genes and could have clinical implications and impacts on inferences of tumor evolution. Critically, flow-cytometry-based calibration of CNA callers yields highly accurate ploidy estimates (ρPearson = 0.92, p < 0.001), providing a robust solution to the substantial inaccuracies that compromise clinical decision-making and evolutionary inference in complex cancers.
SN - 2667-2375
ER -