Abstract Despite the widespread use of mammography as the standard of care for breast cancer screening, its accuracy remains limited for select patient populations, such as women with high breast density. Liquid biopsy-based tests offer an accessible complement to conventional screening methods. Here, we conducted a case-control study to develop a plasma-based protein classifier to distinguish between early-stage breast cancer patients and healthy individuals. A total of 335 women, comprising 116 patients with newly diagnosed, treatment naïve breast cancer (Stage 0-2) and 219 healthy controls, had plasma samples collected and processed in a blinded manner using a sample preparation method coupled with semi-quantitative, label-free mass spectrometry (MS)-based analysis. The median number of proteins detected per patient across breast cancer and healthy individuals was 6,991 and 6,818, respectively. A machine learning-based classifier was trained and validated on patient proteome profiles using a leave-one-out cross-validation (LOOCV) approach to identify breast cancer patients. The classifier achieved an AUC of 0.96 (95% CI: 0.93-0.97), with a sensitivity of 86.2% (95% CI: 78.8-91.3%) and a specificity of 90.4% (95% CI: 85.8-93.6%). In breast cancer patients, the classifier retained 85% sensitivity regardless of breast density (low density: 87.2%, high density: 90.2%) at 90% specificity. Our workflow demonstrates the potential of plasma proteomics as a potent diagnostic tool in early-stage breast cancer screening.
Travadi et al. (2025) studied this question.