Rapid detection of fetal aneuploidy using proteomics approaches on amniotic fluid supernatant

Prenat Diagn. 2005 Jul;25(7):559-66. doi: 10.1002/pd.1186.

Abstract

Objective: Conventional chromosomal studies or fluorescent in situ hybridization takes days to diagnose fetal aneuploidies during amniocentesis. Here, we evaluated the value of mass spectrometry-based clinical proteomics analysis on amniotic fluid supernatant (AFS) as a rapid detection of fetal aneuploidies.

Methods: Proteomics profiles generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) after fractionating samples with functionalized magnetic beads were used for differentiating 60 normal karyotypic from 20 aneuploid AFS. After the discriminating models were generated using genetic algorithm, we evaluated the clinical efficacy of the models in detecting aneuploidies in two batches (each n=30) of AFS prior to the release of chromosomal diagnoses.

Results: Within hours, the two-step proteomics analysis of AFS with the C18 model, followed by the weak cation exchange model, was able to detect aneuploid AFS at 3.3% disease prevalence rate with 100% sensitivity, 72 to 96% specificity, 11 to 50% positive predictive value, and 100% negative predictive value.

Conclusion: Clinical proteomics analysis of AFS using magnetic beads-based sample preparation and MALDI-TOF-MS can be used as a rapid detection for fetal aneuploidies. With perfect sensitivity and negative predictive value of the two-step proteomics method, it may be used for rapid detection of aneuploid AFS immediately after amniocentesis. Further large-scale examinations are apparently needed to verify the clinical value of this rapid detection.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amniotic Fluid / chemistry*
  • Aneuploidy*
  • Female
  • Humans
  • Male
  • Mass Spectrometry
  • Predictive Value of Tests
  • Pregnancy
  • Pregnancy Trimester, Second
  • Prenatal Diagnosis*
  • Proteomics*
  • Sensitivity and Specificity