Unsupervised Placental Gene Expression Profiling Identifies Clinically Relevant Subclasses of Human Preeclampsia

Abstract

Preeclampsia (PE) is a complex, hypertensive disorder of pregnancy, demonstrating considerable variability in maternal symptoms and fetal outcomes. Unfortunately, prior research has not accounted for this variability, resulting in a lack of robust biomarkers and effective treatments for PE. Here, we created a large (N=330) clinically relevant human placental microarray data set, consisting of 7 previously published studies and 157 highly annotated new samples from a single BioBank. Applying unsupervised clustering to this combined data set identified 3 clinically significant probable etiologies of PE: "maternal", with healthy placentas and term deliveries; "canonical", exhibiting expected clinical, ontological, and histopathologic features of PE; and "immunologic" with severe fetal growth restriction and evidence of maternal antifetal rejection. Moreover, these groups could be distinguished using a small quantitative polymerase chain reaction panel and demonstrated varying influence of maternal factors on PE development. An additional subclass of PE placentas was also revealed to form because of chromosomal abnormalities in these samples, supported by array-based comparative genomic hybridization analysis. Overall, our findings represent a new paradigm in our understanding of the origins and maternal-placental contributions to the pathology of PE. The study of PE represents a unique opportunity to access human tissue associated with a complex hypertensive disorder, and our novel approach could be applied to other hypertensive and heterogeneous human diseases.

Keywords: bioinformatics; class discovery; hypertension; microarray; placenta; preeclampsia.