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Background <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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Integrative benchmarking and automation of clonal reconstruction of somatic mutations in single-sample tumor genome analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Marina Masliakova,
  • Steve Lefever,
  • Jo Vandesompele

摘要

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.