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Related Experiment Video

Updated: Feb 22, 2026

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
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ClonEvol: clonal ordering and visualization in cancer sequencing.

H X Dang1,2, B S White1,2, S M Foltz1

  • 1McDonnell Genome Institute.

Annals of Oncology : Official Journal of the European Society for Medical Oncology
|September 27, 2017
PubMed
Summary
This summary is machine-generated.

ClonEvol software accurately reconstructs tumor clonal evolution by overcoming statistical variability in cellular prevalence estimates. This tool aids in understanding tumor progression and guiding personalized cancer therapies through robust visualization and interpretation.

Keywords:
cancer evolutionclonal evolutionclonal evolution visualizationclonality analysisnext-generation sequencing

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Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Reconstructing tumor clonal evolution is crucial for personalized cancer therapies.
  • Current methods struggle with statistical variability and heterogeneity, hindering accurate clonal ordering.
  • A lack of comprehensive visualization tools complicates the interpretation of clonal relationships.

Purpose of the Study:

  • To develop ClonEvol, a unified software tool for clonal ordering, visualization, and interpretation.
  • To address challenges in accurate clonal evolution reconstruction caused by data variability and heterogeneity.
  • To provide a tool that facilitates the understanding of complex clonal relationships in tumors.

Main Methods:

  • ClonEvol employs bootstrap resampling to estimate clone cellular fractions and probabilistically models clonal ordering.
  • The method accounts for statistical variability inherent in bulk tumor sequencing data.
  • Automated generation of multiple visualizations aids in reconstructing and interpreting clonal evolution.

Main Results:

  • ClonEvol demonstrated superior performance compared to state-of-the-art tools in clonal evolution inference, showing enhanced error tolerance and accuracy.
  • The software successfully rediscovered relapsed subclones in acute myeloid leukemia patients.
  • Noninvasive monitoring with ClonEvol recapitulated emerging subclones during metastatic progression in breast cancer patients.

Conclusions:

  • ClonEvol offers broad applicability for longitudinal monitoring of tumor clonal populations.
  • The tool can be utilized with both tumor biopsies and noninvasive samples.
  • ClonEvol can guide precision medicine strategies by providing insights into tumor evolution.