First-trimester non-invasive prediction of preterm birth using cell-free DNA fragmentomics.
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Pham My-Diem Nguyen, Phan Minh-Tam Thi, Tran Nhat-Thang, Vo Ta-Son, Le Hong-Thinh, Nguyen Thu-Ha Thi, Nguyen Quoc-Huy Vu, Ha Minh-Thi Thi, Le Tam Minh, Hoang Diem-Tuyet Thi, Huynh Khanh-Trang Nguyen, Nguyen Nhan Viet, Nguyen Chuong Canh, Bui Thuong Chi, Nguyen Xuan Thanh, Le Sa Viet, Tran Vinh Dinh, Nguyen My-Nhi Ba, Van Nguyen Thong, Nguyen Tuyet-Anh Thi, Hoang Ba Phuoc, Van Nguyen Trong, Nguyen Thuy-Ai Thuy, Nguyen Toa Tri, Duong Thang Duc, Pham Cuong Huy, Luong Kim-Oanh Thi, Dao Cuong Ngoc, Van Hoang Khanh, Huynh Thu-Thanh Thi, Nguyen Khuong Manh, Tran Son-Tra Thi, Tran Hoanh Trung, Nguyen Son Canh, Tran Thuy Dinh, Nguyen Phương Thi Lan, Pham Thanh Viet, Pham Kong Chi, Thai Minh Doan, Do Thanh-Thuy Thi, Dao Hoa Thi, Van Yen-Linh Thi, Tang Sang Hung, Nguyen Hoai-Nghia, Phan Minh-Duy, Giang Hoa, Nguyen Cuong Thach, Trac Quang Thinh
Аннотация
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.