Genome-wide nucleosome footprints of plasma cfDNA predict preterm birth: A case-control study

Area of Science:

• Genomics
• Biomarker Discovery
• Computational Biology

Background:

• Preterm birth (PTB) affects 11% of global births, causing significant maternal and infant morbidity and mortality.
• Early identification of at-risk pregnancies is crucial for timely intervention and improved outcomes.
• Cell-free DNA (cfDNA) in plasma is a promising biomarker for monitoring pregnancy health and detecting complications like PTB.

Purpose of the Study:

• To develop and validate a novel classifier for predicting spontaneous preterm birth using cfDNA promoter profiling.
• To address the need for accurate, large-scale, and validated methods for PTB prediction.

Main Methods:

• A large-scale, multi-center case-control study of 2,590 pregnancies was conducted.
• Whole-genome sequencing of cfDNA was performed, focusing on promoter regions.
• Machine learning models, including support vector machines, were used to develop the PTerm classifier.

Main Results:

• The PTerm classifier, based on support vector machine modeling, achieved a high area under the curve (AUC) of 0.878 in cross-validation.
• PTerm demonstrated strong predictive performance in three independent validation cohorts, with an overall AUC of 0.849.
• The classifier effectively utilizes cfDNA promoter profiling for spontaneous preterm birth prediction.

Conclusions:

• PTerm exhibits high accuracy in predicting preterm birth.
• The PTerm classifier is easily adaptable for current non-invasive prenatal testing procedures without additional cost.
• This approach facilitates widespread preclinical adoption for early PTB risk assessment.