Detection of DNA copy number alterations by matrix-assisted laser desorption/ionization time-of-flight mass

Shengnan Jin1,2, Dan Huang1,2, Weijiang Jin1,2

  • 1School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, P.R. China.

Abstract

Insights

This study introduces a novel method for detecting copy number alterations (CNAs) in heterogeneous tumors using SNP analysis and MALDI-TOF MS. This approach accurately identifies genomic changes in early-stage lung carcinoma, distinguishing it from normal tissues.

Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Copy number alterations (CNAs) are common in malignant tissues.
  • Detecting a wide range of CNA targets in heterogeneous tumors remains challenging.
  • Existing methods face limitations in sensitivity and specificity for complex samples.

Purpose of the Study:

  • To develop and validate a novel approach for detecting CNAs in heterogeneous tumor samples.
  • To quantitatively analyze allelic imbalance using single nucleotide polymorphisms (SNPs) and MALDI-TOF MS.
  • To distinguish between loss of heterozygosity (LOH) and genomic amplification using real-competitive PCR (rcPCR).

Main Methods:

  • Quantitative analysis of allelic imbalance by SNP allelotyping using MALDI-TOF MS.
  • Quantification of copy number changes via rcPCR to differentiate LOH and amplification.
  • Validation of CNA regions identified by next-generation sequencing (NGS) in early-stage lung carcinoma.

Main Results:

  • CNAs were successfully detected in heterogeneous DNA samples with as little as 10% tumor DNA.
  • Quantitative rcPCR effectively distinguished between LOH and chromosome amplification.
  • Validation using 41 SNPs confirmed copy number changes and differentiated early-stage lung carcinoma tissues from normal tissues.

Conclusions:

  • MALDI-TOF MS based CNA detection is effective for validating NGS-identified genomic regions.
  • This method enables accurate CNA detection in mixed neoplastic and normal cell populations.
  • The developed approach offers a robust tool for cancer genomics research and diagnostics.

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