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First-trimester non-invasive prediction of preterm birth using cell-free DNA fragmentomics
My-Diem Nguyen Pham1,2, Minh-Tam Thi Phan1,2, Nhat-Thang Tran3
1Medical Genetics Institute (Pham, Phan, Do, Van, Tang, Nguyen, Phan, Giang, Nguyen, Trac), Ho Chi Minh City, Vietnam.
Background:
Spontaneous preterm birth (PTB) remains a leading cause of neonatal morbidity and mortality, yet accurate prediction in asymptomatic women during early gestation remains a critical unmet clinical need. Cell-free DNA (cfDNA) obtained through the same maternal blood collection used for routine first-trimester non-invasive prenatal testing (NIPT) represents an underutilized substrate for early PTB risk identification.
Objective:
To develop and validate a cell-free DNA (cfDNA) fragmentomic classifier for the early prediction of spontaneous preterm birth (PTB) using routine first-trimester non-invasive prenatal testing (NIPT) data.
Study Design:
A case-control study was conducted within a multicenter Vietnamese cohort comprising 286 pregnancies, including 82 spontaneous PTB cases and 204 term controls. Maternal plasma cfDNA collected during routine first-trimester NIPT (median gestational age, 12 weeks) was sequenced to a depth of approximately 20 million reads per sample. Five fragmentomic feature categories including copy number alterations, end-motif composition, nucleosome distance, fragment length, and joint fragment-length × end-motif were evaluated for PTB prediction. Machine learning classifiers were developed in a training cohort (n=228, 65 PTB vs 163 TB) and tested in an internal held-out validation cohort (n=58, 17 PTB vs 41 TB).
Results:
Among the 5 fragmentomic feature classes evaluated, 4-mer end-motif (EM) profiles exhibited the most pronounced differences between PTB and term control samples. Consistent with these findings, the EM-based classifier demonstrated the highest discriminative performance in the internal validation cohort, achieving an AUC of 0.970 (95% CI, 0.912-1.000). At a specificity >90%, the model achieved a sensitivity of 94% (95% CI, 78%-100%).
Conclusion:
These findings demonstrate that cfDNA EM signatures derived from routine first-trimester NIPT can accurately identify pregnancies at risk of spontaneous preterm birth, using only a routine maternal blood sample, supporting the potential to extend the clinical utility of existing prenatal screening infrastructure pending prospective validation at production sequencing depths.