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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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LiquidCNA: Tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations.

Eszter Lakatos1, Helen Hockings2,3, Maximilian Mossner1

  • 1Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.

Iscience
|August 17, 2021
PubMed
Summary

LiquidCNA enables tracking of tumor subclonal evolution using cell-free DNA (cfDNA) from liquid biopsies. This method monitors therapy resistance by analyzing somatic copy number alterations (SCNA) in cfDNA.

Keywords:
CancerCancer systems biologyGenomics

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Liquid biopsies using cell-free DNA (cfDNA) offer minimally invasive monitoring of tumor dynamics during cancer therapy.
  • Understanding subclonal evolution is crucial for predicting and overcoming therapy resistance.

Purpose of the Study:

  • To introduce liquidCNA, a novel computational method for tracking subclonal evolution from longitudinal cfDNA samples.
  • To enable simultaneous genotyping and quantification of dominant subclones using somatic copy number alterations (SCNA).

Main Methods:

  • LiquidCNA analyzes cfDNA from low-pass whole-genome sequencing.
  • It utilizes SCNA to genotype and quantify subclones without B-allele frequency, matched-normal samples, or prior clone information.
  • The method was validated using synthetic datasets and in vitro cancer cell line mixtures.

Main Results:

  • LiquidCNA accurately tracks subclonal evolution in cfDNA.
  • Application in metastatic lung cancer patients revealed the emergence of novel tumor subpopulations during therapy.
  • The method is computationally inexpensive and easy to implement.

Conclusions:

  • LiquidCNA provides a robust and cost-effective approach for monitoring tumor evolution and therapy resistance.
  • Continuous monitoring of subclonal dynamics can inform treatment strategies and improve patient outcomes.