Development and Validation of a Machine-Learning Deep Plasma Proteome Classifier for Early-Stage Breast Cancer Detection.

Introduction: Proteome-guided liquid biopsy tests hold immense promise for the future of early cancer detection. Here, we analyzed the plasma proteome of 1,259 biobanked samples consisting of healthy women and women with breast cancer. The Astrin Biosciences' breast cancer early detection test is a laboratory developed test (LDT) that uses a protein-based machine learning classifier to identify breast cancer with high accuracy. Methods: The classifier was trained on 845 women (466 healthy and 379 with newly diagnosed, treatment naïve breast cancer) and validated on 397 women (195 healthy and 202 breast cancer) from the same collection cohort (held-out validation). All plasma samples were processed in an automated, blinded manner coupled with semi-quantitative, label-free mass spectrometry (MS)-based analysis. Results: The held-out validation performance achieved 92.3% specificity (180/195; 95% Wilson CI: 87.7-95.3%), 92.6% sensitivity (187/202; 95% Wilson CI: 88.1-95.4%) and an AUC of 0.975 (95% Bootstrap CI: 0.961-0.987). Observed sensitivity remained high across all breast cancer stages and pathological and molecular subtypes, albeit with small sample sizes for some subtypes. Gene set enrichment analyses (GSEA) identified epithelial-to-mesenchymal transition (EMT) and PI3K-AKT signaling as enriched in the breast cancer samples, highlighting that our test may possibly identify cancer-related proteins in early-stage patients. A simulated population demonstrates the utility of our test as a supplement to mammography, detecting nearly all (93%) breast cancers missed by mammography and reducing the number of false positives relative to MRI and Contrast-Enhanced Mammography (CEM) alone by >10-fold. Discussion: Overall, our proteomic data demonstrates high sensitivity and specificity in women with breast cancer, especially at early stages, and is a favorable supplemental test post mammogram.