Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment
Menée à partir d'échantillons sériques prélevés sur 503 patientes atteintes d'un cancer du sein et 289 patientes avec lésions mammaires bénignes et menée à l'aide de 79 échantillons tumoraux, cette étude met en évidence l'intérêt d'un modèle d'apprentissage automatique basé sur les caractéristiques du fragmentome de l'ADN libre circulant pour détecter un cancer du sein, déterminer son sous-type moléculaire et évaluer le statut des ganglions lymphatiques
Breast cancer remains a leading global health concern in women, while screening is still limited by imaging accessibility and reduced sensitivity in dense breasts. Here we conduct a multicenter case-control study including 503 breast cancer patients and 289 benign controls to develop TuFEst, a machine learning model based on genome-wide cell-free DNA fragmentomic features. TuFEst achieves high sensitivity (95%) and specificity (78.3%) for early cancer detection and reliably identifies malignancies missed by conventional imaging. Extension of this framework enables non-invasive molecular subtyping (TuFEst-MS) and lymph node status prediction (TuFEst-LN), with strong performance in independent validation cohorts and imaging-pathology discordant cases. Transcriptomic profiling of paired bulk tumor samples (n = 79) demonstrates that elevated TuFEst-derived cancer scores reflect tumor aggressiveness and immune-related biological programs. Together, these findings support cfDNA fragmentomics as an integrated liquid biopsy strategy for breast cancer management, enabling concurrent detection, molecular subtyping, and lymph node evaluation with potential clinical utility.
Nature Communications , article en libre accès, 2026