A Plasma-based Deep Proteomic Platform for early-stage Breast Cancer Detection

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 those with early-stage breast cancer 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 semiquantitative, label-free mass spectrometry-based analysis. The median number of proteins detected per patient across breast cancer and healthy individuals was 6991 and 6818, respectively. A machine learning-based classifier was trained and validated on patient proteome profiles using a leave-one-out cross-validation approach to identify patients with breast cancer. The classifier achieved an area under the curve 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 patients with breast cancer, 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.