Van Thi Tuong Vi, Tran Trung Hieu, Nguyen Thi Hue Hanh, Nguyen Van Thien Chi, Vo Dac Ho, Nguyen Giang Thi Huong, Nguyen Trong Hieu, To Kim Sang, Nguyen Anh Luan, Tran Cao Hong An, Jasmine Thanh Xuan, Vo Thi Loan, Nai Thi Huong Thoang, Tran Thuy Trang, Truong My Hoang, Tran Ngan Chau, Le Thi Loc, Nguyen Thi Hong Nhung, Tu Ngoc Hieu, Tran Thanh Son, Le Bao Toan, Tang Van Phong, Nguyen Pham Thanh Nhan, Nguyen Khac Tien, Ho Van Chien, Nguyen Xuan Vinh, Doan Nhu Nhat Tan, Tran Thi Trang, Tran Thi Minh Thu, Tran Vu Uyen, Le Minh Phong, Vu Thi Luyen, Tieu Ba Linh, Nguyen Huu Tam Phuc, Nguyen Luu Hong Dang, Phan Ngoc Minh, Van Phan Thi, Do Thi Thanh Thuy, Dao Thi Huyen, Tang Hung Sang, Nguyen Duy Sinh, Giang Hoa, Phan Minh Duy, Nguyen Hoai-Nghia, Vo Duc Hieu, Tran Le Son
Medical Genetics Institute, Ho Chi Minh, Vietnam.
Ho Chi Minh City Oncology Hospital, Ho Chi Minh, Vietnam.
BMC Biol. 2025 Aug 20;23(1):259. doi: 10.1186/s12915-025-02371-z.
Breast cancer (BC) remains the second leading cause of cancer-related mortality among women worldwide. Liquid biopsy based on circulating tumor DNA (ctDNA) offers a promising noninvasive approach for early detection; however, differentiating malignant tumors from benign abnormalities remains a significant challenge.
Here, we developed a multimodal approach to analyze cfDNA methylation and fragmentomic patterns in 273 BC patients, 108 individuals with benign breast conditions, and 134 healthy controls. Genome-wide analyses revealed distinct cfDNA copy number alterations and cytosine-enriched cleavage sites in BC patients. Targeted sequencing further revealed unique methylation patterns, including hypermethylation in GPR126, KLF3, and TLR10 and hypomethylation in TOP1 and MAFB. Our machine-learning model achieved an AUC of 0.90, with 93.6% specificity and 62.1-66.3% sensitivity for stage I-II cancers. In symptomatic populations, sensitivities were 50.0%, 68.2%, and 64.7% for BI-RADS categories 3, 4, and 5, respectively, with 96.1% specificity.
These findings underscore the potential of cfDNA biomarkers to enhance BC detection and reduce the rate of unnecessary biopsies.
乳腺癌(BC)仍是全球女性癌症相关死亡的第二大主要原因。基于循环肿瘤DNA(ctDNA)的液体活检为早期检测提供了一种有前景的非侵入性方法;然而,区分恶性肿瘤与良性异常仍然是一项重大挑战。
在此,我们开发了一种多模态方法,用于分析273例乳腺癌患者、108例患有良性乳腺疾病的个体和134名健康对照者的游离DNA(cfDNA)甲基化和片段组学模式。全基因组分析揭示了乳腺癌患者中不同的cfDNA拷贝数改变和富含胞嘧啶的切割位点。靶向测序进一步揭示了独特的甲基化模式,包括GPR126、KLF3和TLR10中的高甲基化以及TOP1和MAFB中的低甲基化。我们的机器学习模型的曲线下面积(AUC)为0.90,对I-II期癌症的特异性为93.6%,敏感性为62.1%-66.3%。在有症状人群中,BI-RADS分类3、4和5的敏感性分别为50.0%、68.2%和64.7%,特异性为96.1%。
这些发现强调了cfDNA生物标志物在增强乳腺癌检测和降低不必要活检率方面的潜力。