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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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Related Experiment Video

Updated: May 11, 2026

Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool
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Copy Number Alteration Profiling from Plasma cfDNA WES in Advanced NSCLC.

Ho Jang1, Mi-Kyung Jeong2

  • 1Korea Medicine Data Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea.

International Journal of Molecular Sciences
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Detecting copy number alterations (CNAs) in non-small cell lung cancer (NSCLC) using cell-free DNA (cfDNA) whole-exome sequencing (WES) is now more robust. Advanced bias correction methods improve accuracy and reproducibility for minimally invasive cancer genome profiling.

Keywords:
GC biascopy number alteration detectionnon-small cell lung cancerplasma cell-free DNAwhole exome sequencing

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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Circulating cell-free DNA (cfDNA) sequencing is a minimally invasive method for tumor genome profiling.
  • Detecting copy number alterations (CNAs) from cfDNA whole-exome sequencing (WES) faces challenges from noise and guanine-cytosine (GC)-related bias.

Purpose of the Study:

  • To develop and evaluate an advanced pipeline for robust CNA detection in advanced non-small cell lung cancer (NSCLC) using cfDNA WES.
  • To address the technical challenges associated with CNA detection in cfDNA WES data.

Main Methods:

  • Characterized read count patterns in cfDNA WES data.
  • Developed and applied an advanced pipeline incorporating locally estimated scatterplot smoothing (LOESS)-based GC bias correction.
  • Evaluated CNA detection accuracy, reproducibility, and concordance with The Cancer Genome Atlas (TCGA) data.

Main Results:

  • Read count signals strongly correlated with GC content.
  • LOESS-based GC bias correction effectively reduced false positives and improved CNA detection.
  • cfDNA CNA profiles were reproducible within patients and showed strong concordance with TCGA tissue-level patterns for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Conclusions:

  • The developed cfDNA WES pipeline enables robust CNA detection in NSCLC.
  • GC bias correction is crucial for accurate CNA profiling from cfDNA WES.
  • cfDNA WES is a practical and minimally invasive alternative for genomic characterization of NSCLC.