Cell-free DNA Fragmentomics Assay to Discriminate the Malignancy of Breast Nodules and Evaluate Treatment Response.

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作者:Liu 刘嘉琦 Jiaqi, Li 李亚伦 Yalun, Tang 唐皖湘夫 Wanxiangfu, Qian 钱天一 Tianyi, Dai 代丽君 Lijun, Jia 贾梓淇 Ziqi, Cao 曹恒 Heng, Li 李成浩 Chenghao, Liu åˆ˜ç œç› Yuchen, Huang 黄岩松 Yansong, Wu 吴疆 Jiang, Ma 马东旭 Dongxu, Qiao 乔广东 Guangdong, Bao åŒ åŽ Hua, Chang 常双 Shuang, Zhu 朱冬琴 Dongqin, Yang 杨珊珊 Shanshan, Wu 吴徐晓辰 Xuxiaochen, Wu 吴雪 Xue, Xu å¾æ’æ¯ Hengyi, Chen 陈洪岩 Hongyan, Shao 邵阳 Yang, Wang 王翔 Xiang, Liu 刘芝华 Zhihua, Su 苏建忠 Jianzhong
The fragmentomics-based cell-free DNA (cfDNA) assays have recently illustrated prominent abilities to identify various cancers from non-conditional healthy controls, while their accuracy for identifying early-stage cancers from benign lesions with inconclusive imaging results remains uncertain. Especially for breast cancer, current imaging-based screening methods suffer from high false positive rates for women with breast nodules, leading to unnecessary biopsies, which add to discomfort and healthcare burden. Here, we enrolled 613 female participants in this multi-center study and demonstrated that cfDNA fragmentomics (cfFrag) is a robust non-invasive biomarker for breast cancer using whole-genome sequencing. Among the multimodal cfFrag profiles, the fragment size ratio (FSR), fragment size distribution (FSD), and copy number variation (CNV) show more distinguishing ability than Griffin, motif breakpoint (MBP), and neomer. The cfFrag model using the optimal three fragmentomics features discriminated early-stage breast cancer from benign nodules, even at a low sequencing depth (3×). Notably, it demonstrated a specificity of 94.1% in asymptomatic healthy women at a 90% sensitivity for breast cancer. Moreover, we comprehensively showcased the clinical utility of the cfFrag model in predicting patient responses to neoadjuvant chemotherapy (NAC) and its enhanced performance when combined with multimodal features, including radiological results [area under the curve (AUC) = 0.93-0.94] and cfDNA methylation features (AUC = 0.96).

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