Non-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

Background

Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors.

Methods

We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors.

Results

By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.

Conclusion

Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

BACKGROUND

IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.

METHODS

We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.

RESULTS

We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.

CONCLUSIONS

our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Hypertensive disorders of pregnancy share common cfDNA methylation profiles

Hypertensive disorders of pregnancy (HDP) contribute substantially to perinatal morbidity and mortality. Epigenetic changes point towards cardio-metabolic dysregulation for these vascular disorders. In early pregnancy, epigenetic changes using cell free DNA (cfDNA) are largely unexplored. We aimed to investigate these in HDP between 11 and 14 weeks of gestation by analysis of cfDNA methylation profiles in patients with hypertensive disorders. We identified patients without chronic hypertension but with subsequent development of preeclampsia (PE) (n = 11), with chronic hypertension (HT) but without PE development (n = 14), and lacking both PE and HT (n = 422). We matched patients according to PE risk factors into three groups (n = 5 each group): (1) PE: no HT but PE development, (2) HT: chronic hypertension but no PE and (3) Control: no PE or HT. We successfully optimized our cfDNA isolation process prior to whole genome bisulfite sequencing. Analysis of cfDNA methylation changes indicate a common predisposition in PE and HT groups, chiefly of maternal origin. Assessment of significant differentially methylated regions and annotated genes point towards a common cardiovascular predisposition in preeclampsia and hypertension groups in the first trimester. We postulate the pivotal role of the maternal cardiovascular system in HDP, which is already evident in the first trimester.

Integrative analyses of maternal plasma cell-free DNA nucleosome footprint differences reveal chromosomal aneuploidy fetuses gene expression profile

Background

Chromosomal aneuploidy is the most common birth defect. However, the developmental mechanism and gene expression profile of fetuses with chromosomal aneuploidy are relatively unknown, and the maternal immune changes induced by fetal aneuploidy remain unclear. The inability to obtain the placenta multiple times in real-time is a bottleneck in research on aneuploid pregnancies. Plasma cell-free DNA (cfDNA) carries the gene expression profile information of its source cells and may be used to evaluate the development of fetuses with aneuploidy and the immune changes induced in the mother owing to fetal aneuploidy.

Methods

Here, we carried out whole-genome sequencing of the plasma cfDNA of 101 pregnant women carrying a fetus with trisomy (trisomy 21, n = 42; trisomy 18, n = 28; trisomy 13, n = 31) based on non-invasive prenatal testing (NIPT) screening and 140 normal pregnant women to identify differential genes according to the cfDNA nucleosome profile in the region around the transcription start sites (TSSs).

Results

The plasma cfDNA promoter profiles were found to differ between aneuploid and euploid pregnancies. A total of 158 genes with significant differences were identified, of which 43 genes were upregulated and 98 genes were downregulated. Functional enrichment and signaling pathway analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases found that these signal pathways were mainly related to the coordination of developmental signals during embryonic development, the control of cell growth and development, regulation of neuronal survival, and immune regulation, such as the MAPK, Hippo, TGF-β, and Rap1 signaling pathways, which play important roles in the development of embryonic tissues and organs. Furthermore, based on the results of differential gene analysis, a total of 14 immune-related genes with significant differences from the ImmPort database were collected and analyzed. These significantly different immune genes were mainly associated with the maintenance of embryonic homeostasis and normal development.

Conclusions

These results suggest that the distribution characteristics of cfDNA nucleosomes in maternal plasma can be used to reflect the status of fetal development and changes of the immune responses in trisomic pregnancies. Overall, our findings may provide research ideas for non-invasive detection of the physiological and pathological states of other diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03735-7.

Performance of whole-genome promoter nucleosome profiling of maternal plasma cell-free DNA for prenatal noninvasive prediction of fetal macrosomia: a retrospective nested case-control study in mainland China

Background

Fetal macrosomia is common occurrence in pregnancy, which is associated with several adverse prognosis both of maternal and neonatal. While, the accuracy of prediction of fetal macrosomia is poor. The aim of this study was to develop a reliable noninvasive prediction classifier of fetal macrosomia.

Methods

A total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12^+ 0–27^+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis.

Results

According to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (C_MA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927–0.8586).

Conclusions

Our study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-05027-w.

Genetic Background of Fetal Growth Restriction

Fetal growth restriction (FGR) is one of the most formidable challenges in present-day antenatal care. Pathological fetal growth is a well-known factor of not only in utero demise in the third trimester, but also postnatal morbidity and unfavorable developmental outcomes, including long-term sequalae such as metabolic diseases, diabetic mellitus or hypertension. In this review, the authors present the current state of knowledge about the genetic disturbances responsible for FGR diagnosis, divided into fetal, placental and maternal causes (including preeclampsia), as well as their impact on prenatal diagnostics, with particular attention on chromosomal microarray (CMA) and noninvasive prenatal testing technique (NIPT).

A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.