Proteomic profile determination of autosomal aneuploidies by mass spectrometry on amniotic fluids

Alain Mange¹^,2^,3 , Caroline Desmetz¹^,2^,3 , Virginie Bellet³ , Nicolas Molinari⁴ , Thierry Maudelonde¹^,2^,3 and Jerome Solassol¹^,2^,3

¹University of Montpellier I, Montpellier, France

²CHU Montpellier, Hôpital Arnaud de Villeneuve, Department of Cellular Biology, Montpellier, France

³CRLC Val d'Aurelle, Department of Clinical Oncoproteomic, Montpellier, France

⁴IURC, Department of Biostatistic, Epidemiology and Clinical Research, Montpellier, France

author email corresponding author email

Proteome Science 2008, 6:1doi:10.1186/1477-5956-6-1


Published:	11 January 2008

Abstract

Background

Prenatal diagnosis of chromosomal abnormalities by cytogenetic analysis is time-consuming, expensive, and requires highly qualified technicians. Rapid diagnosis of aneuploidies followed by reassurance of women with normal results can be performed by molecular analysis of uncultured foetal cells. In the present study, we developed a proteomic fingerprinting approach coupled with a statistical classification method to improve diagnosis of aneuploidies, including trisomies 13, 18, and 21, in amniotic fluid samples.

Results

The proteomic spectra obtained from 52 pregnant women were compiled, normalized, and mass peaks with mass-to-charge ratios between 2.5 and 50 kDa identified. Peak information was combined together and analysed using univariate statistics. Among the 208 expressed protein peaks, 40 differed significantly between aneuploid and non aneuploid samples, with AUC diagnostic values ranging from 0.71 to 0.91. Hierarchical clustering, principal component analysis and support vector machine (SVM) analysis were performed. Two class predictor models were defined from the training set, which resulted in a prediction accuracy of 92.3% and 96.43%, respectively. Using an external and independent validation set, diagnostic accuracies were maintained at 87.5% and 91.67%, respectively.

Conclusion

This pilot study demonstrates the potential interest of protein expression signature in the identification of new potential biological markers that might be helpful for the rapid clinical management of high-risk pregnancies.