ArticleNature communications2026
Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07304934 (A Prospective Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for Axillary Lymph Node Status Assessment in Breast Cancer Patients Undergoing Primary Surgery or Neoadjuvant Therapy), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for Axillary Lymph Node Status Assessment in Breast Cancer Patients Undergoing Primary Surgery or Neoadjuvant Therapy
Who cites it
2 citing papers in PubMed.
- Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting.Molecular biomedicine · 2026Review
- Plasma proteome-metabolome signatures enable non-invasive early detection and lymph node risk stratification in breast cancer.Molecular cancer · 2026Article
Corrections and comments
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Authors and funding
27 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.