A liquid biopsy-based assay for classifying breast cancer from benign nodule.
Authors: Song Zhenchuan , An Mengyang , Liu Yang , Wang Xinle , Li Ningyou , Chen Xiaoxi , Wu Xuxiaochen , Bao Hua , Yang Ruowei , Xu Xiuxiu , Zhu Dongqin 
Published: June 2024

e12572 Background: Breast cancer (BRCA) ranks as the most frequently diagnosed non-skin cancer and stands as the second leading cause of cancer-related mortality in women in the United States. Globally, 2.3 million women were diagnosed with breast cancer in 2020, resulting in 685,000 deaths. Therefore, accurate classification of breast cancer could contribute to immediate treatment and improve outcomes for individuals diagnosed with breast cancer. Our study introduces an ensemble model utilizing the distinctive characteristics of cell-free DNA (cfDNA) fragmentation from whole genome sequencing (WGS) for the classification of BRCA and benign nodule. Methods: The study cohort consisted of 214 individuals diagnosed with breast cancer (BRCA) and 211 individuals with benign breast nodule. Employing whole-genome sequencing (WGS), we subjected plasma samples from all participants to comprehensive fragmentomic profiling. Three fragmentomic features: copy number variation (CNV), fragmentation size coverage (FSC), and fragmentation size distribution (FSD), were utilized by machine learning algorithms. An ensemble model integrated results from each algorithm then generated a cancer score ranging from 0 to 1 for each sample. The model was trained exclusively using the training cohort and the model’s performance was assessed in the independent validation cohort. Results: Our ensemble model demonstrated a sensitivity of 80.2% and a specificity of 95.3% in training cohort, with an AUC of 0.966. In the validation cohort, the model maintained its effectiveness, with a sensitivity of 73.6%, a specificity of 89.3% supported by an AUC of 0.919. Notably, the model showed various predictive ability across different stages of breast cancer. In the training cohort, it exhibited a sensitivity of 76.4% for Stage I, 81.8% for Stage II, and 88.9% for Stage III, indicating superior predictive power at advanced progression levels. Additionally, the model demonstrated a consistent sensitivity of 75% for Stage I in the validation cohort, affirming its reliable performance across independent datasets. When stratified by molecular subtypes, the model showed relatively lower scores and sensitivity BRCA with HR+/HER2+, compared to HR-/HER2+ and HR+/HER2- subgroups, as well as Triple-Negative Breast Cancer. Finally, the model generated unbiased predictions across various histology subtypes, accurately discerning ductal carcinoma and other BRCA histology subtypes in training and validation cohorts. Conclusions: The ensemble model that leverages non-invasive liquid biopsy-based assay for the accurate classification of BRCA and benign nodule provides a critical opportunity for effective intervention for cancer patients. This approach, facilitating swift and appropriate treatments, and optimizing the allocation of healthcare resources, underscores its significance in the ongoing pursuit of enhancing breast cancer care and outcomes.