Expression Signature as a Biomarker for Prenatal Diagnosis of Trisomy 21

PLOS ONE, Dec 2019

A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker’s high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal and reactive cellular mechanisms, transcriptomic changes may thus have future potential in the diagnosis of a wide array of heterogeneous diseases that result from genetic disturbances.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074184&type=printable

Expression Signature as a Biomarker for Prenatal Diagnosis of Trisomy 21

Citation: Volk M, Maver A, Lovrei L, Juvan P, Peterlin B ( Expression Signature as a Biomarker for Prenatal Diagnosis of Trisomy 21 Marija Volk 0 Ale Maver 0 Luca Lovrei 0 Peter Juvan 0 Borut Peterlin 0 Yann Herault, IGBMC/ICS, France 0 1 Clinical Institute of Medical Genetics, Department of Obstetrics and Gynecology, University Medical Center , Ljubljana, Ljubljana , Slovenia , 2 Center for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana , Ljubljana , Slovenia A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leaveone-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker's high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal and reactive cellular mechanisms, transcriptomic changes may thus have future potential in the diagnosis of a wide array of heterogeneous diseases that result from genetic disturbances. - Funding: This work has been supported by Slovenian Research Agency grant number P3-03269 (http://www.arrs.gov.si). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Progressive development and improvement of methods for investigation of global transcriptional alterations in human disease is now enabling reproducible and consistent selection of genes that best differentiate samples originating from transcriptome is a highly complex and dynamic system, difficult to model with classical approaches, it does nevertheless present a landscape where manifold pathogenic and reactive transcriptional regulation results from genetic as well as environmental influences, transcriptomics present a valuable opportunity for the development of a heterogeneous diagnostic tool for diseases ranging from those of clear genetic aetiology to those of complex unexplained aetiology. Identification of high-risk pregnancy with its heterogeneous aetiology and complex pathogenesis remains complicated since there is no single investigation deemed to be best in all circumstances. As there already are successful implementations of expression biomarkers into clinical practice [2] we opted to investigate and demonstrate the feasibility of expression biomarkers to predict high-risk pregnancy status on Ts21 as a model of high-risk pregnancy. The objective for selecting Ts21 as a model system is its definite genomephenome correlation. Several global gene expression studies of Ts21 demonstrated extensive changes in expression of chromosome 21 (HSA21) and non- chromosome 21 (nonHSA21) genes [3-10]. Global transcriptome profiling studies on prenatal samples of amniotic fluid and chorionic villi with trisomy of chromosome 21 [3,5] revealed significant dysregulation of HSA21 and nonHSA21 genes and concluded that resulting alterations reflect a combination of gene dosage effect and genome-wide transcriptional dysregulation hypotheses. Reports on transcriptomic analyses of other fetal tissues [4,6,10], including cerebellum, heart or fibroblasts demonstrated that sets of overexpressed and under-expressed genes differ across differe (...truncated)


This is a preview of a remote PDF: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074184&type=printable

Marija Volk, Aleš Maver, Luca Lovrečić, Peter Juvan, Borut Peterlin. Expression Signature as a Biomarker for Prenatal Diagnosis of Trisomy 21, PLOS ONE, 2013, 9, DOI: 10.1371/journal.pone.0074184