Abstract 11P
Background
Breast cancer is the second leading cause of cancer deaths in women worldwide. Early detection of breast cancer has been demonstrated to improve patients' treatment outcomes and survival. Liquid biopsy based on detecting DNA shed by breast tumors into the circulation, known as circulating tumor DNA (ctDNA), has emerged as a promising non-invasive approach. However, differentiating benign breast lumps from malignant tumors remains a challenge in current clinical practice, and inaccurate detection may result in unnecessary invasive procedures.
Methods
To address this challenge, we employed a multimodal analysis approach, namely SPOT-MAS (Screen for the Presence of Tumor by DNA Methylation and Size) to profile alterations in methylation and fragment length patterns of cell free DNA (cfDNA) from 133 breast cancer patients and 59 patients with benign breast lumps comprising cysts and fibroadenomas.
Results
We identified multiple distinct end motifs, differential methylation and fragment length patterns across 22 chromosomes, which were further exploited as input features to build machine learning models to discriminate early-stage breast cancer patients from patients with benign lesions. The models achieved an area under the curve of 0.87 (95% CI: 0.79 – 0.94) and a sensitivity of 64.1% at 90% specificity in detecting patients with malignant tumors.
Conclusions
Therefore, our findings demonstrated that cancer-specific methylation and fragmentomic patterns in plasma cfDNA could serve as novel biomarkers for accurately differentiating malignant breast cancer patients from those with benign lesions.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Medical Genetics Institute and Gene Solutions Joint Stock Company, Vietnam.
Funding
Gene Solutions Joint Stock Company.
Disclosure
All authors have declared no conflicts of interest.
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