Integrative benchmarking and automation of clonal reconstruction of somatic mutations in single-sample tumor genome analysis.
- 1pxlence bv, Building RTP (entrance 97), Corneel Heymanslaan 10, Ghent, 9000, Belgium.
- 2pxlence bv, Building RTP (entrance 97), Corneel Heymanslaan 10, Ghent, 9000, Belgium. [email protected].
- 3Department of Biomolecular Medicine, Ghent University, Building the Core (entrance 37), Corneel Heymanslaan 10, 9000, Ghent, Belgium. [email protected].
Abstract
Background
Accurate identification of truncal mutations, those present in all tumor cells, is essential for understanding tumor evolution and guiding downstream analyses in cancer genomics, including detection of minimal residual disease. Existing clonal reconstruction tools vary in accuracy, specificity, and computational efficiency, and, to our knowledge, no standardized workflow exists for systematic truncal mutation extraction.Results
We benchmarked five clonal reconstruction tools on simulated tumor genomes from the DREAM challenge using true positive rate, false positive rate, and runtime. Based on the results, we developed TruncalFlow, a containerized pipeline for automated clonal reconstruction and truncal mutation extraction. Applied to real tumor genomes from 30 cancer types available in TCGA, it enabled analyses of mutation types, recurrence among tumor types, and cancer driver annotation, identifying biologically relevant truncal mutations enriched for driver events. TruncalFlow provides a scalable, accurate framework for systematic truncal mutation analysis in cancer genomes.Conclusions
TruncalFlow enables robust and automated identification of truncal mutations from tumor sequencing data, supporting accurate clonality assessment across cancer types. By facilitating scalable extraction of both driver and non-driver truncal variants, it provides a practical framework linking tumor evolution analysis to translational and liquid biopsy applications.
Related
- WITHDRAWN: Heterozygous germline MSH3 mutations, and probably MLH3 mutations, act as classical tumour suppressors, leading to excess somatic deletion mutations, signature ID4 and increased colorectal cancer risk
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- Genomic foundation model-derived disruption profiling links somatic mutations to cancer biology and clinical outcomes
- Genome-Wide Characterisation of Somatic Mutations Across COSMIC Cancer Cell Lines Enables Gene-Centric Experimental Model Selection.
- Case Report: Coexpression of somatic EGFR, BRCA1, and BRCA2 mutations in metastatic non-small cell lung cancer
BibTeX
@article{masliakova2026integrative,
title = {{Integrative benchmarking and automation of clonal reconstruction of somatic mutations in single-sample tumor genome analysis.}},
author = {Marina Masliakova and Steve Lefever and Jo Vandesompele},
journal = {BMC bioinformatics},
year = {2026},
month = aug,
doi = {10.1186/s12859-026-06589-8},
url = {https://europepmc.org/article/MED/42972248},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrative benchmarking and automation of clonal reconstruction of somatic mutations in single-sample tumor genome analysis.
AU - Marina Masliakova
AU - Steve Lefever
AU - Jo Vandesompele
PY - 2026
DA - 2026/08/05
JO - BMC bioinformatics
DO - 10.1186/s12859-026-06589-8
UR - https://europepmc.org/article/MED/42972248
AB - Background Accurate identification of truncal mutations, those present in all tumor cells, is essential for understanding tumor evolution and guiding downstream analyses in cancer genomics, including detection of minimal residual disease. Existing clonal reconstruction tools vary in accuracy, specificity, and computational efficiency, and, to our knowledge, no standardized workflow exists for systematic truncal mutation extraction.Results We benchmarked five clonal reconstruction tools on simulated tumor genomes from the DREAM challenge using true positive rate, false positive rate, and runtime. Based on the results, we developed TruncalFlow, a containerized pipeline for automated clonal reconstruction and truncal mutation extraction. Applied to real tumor genomes from 30 cancer types available in TCGA, it enabled analyses of mutation types, recurrence among tumor types, and cancer driver annotation, identifying biologically relevant truncal mutations enriched for driver events. TruncalFlow provides a scalable, accurate framework for systematic truncal mutation analysis in cancer genomes.Conclusions TruncalFlow enables robust and automated identification of truncal mutations from tumor sequencing data, supporting accurate clonality assessment across cancer types. By facilitating scalable extraction of both driver and non-driver truncal variants, it provides a practical framework linking tumor evolution analysis to translational and liquid biopsy applications.
SN - 1471-2105
ER -