Scholarlink

<p>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 (<i>n</i> = 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

查看全文
  • Yuxuan Zhu,
  • Siwei Zheng,
  • Yinkuan Shao,
  • Jun Zhou,
  • Xidong Gu,
  • Lesang Shen,
  • Xue Li,
  • Wenjia Liu,
  • Wei Xue,
  • Haiqi Lu,
  • Jun Zhou,
  • Jinghua Ding,
  • Haiming Deng,
  • Jiayin Chen,
  • Zhuohang Yu,
  • Yao Yao,
  • Wenjie Xia,
  • Wuzhen Chen,
  • Shanshan Sun,
  • Zheng Wang,
  • Tianyi Qian,
  • Xiuyan Yu,
  • Jian Liu,
  • Yiding Chen,
  • Ziao Lin,
  • Jian Huang,
  • Chao Ni

摘要

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.