Abstract PD5-03: Deep Proteomics and AI Classifier for Early Breast Cancer Detection

Abstract Introduction: Women with dense breast tissue face a two-fold higher risk of breast cancer, yet mammography sensitivity is as low as 30% in this group, underscoring the need for more sensitive and accessible screening methods. Liquid-based biopsies for early breast cancer detection are emerging but currently remain out of reach for clinical use. Recent data on nucleotide assessment from plasma in breast cancer have been mixed with 87% sensitivity for late-stage disease (stage 3-4) but only 20% sensitivity for early-stage disease (stage 1-2). Proteomics is an exciting area for early cancer screening where improvements in sample preparation and equipment have enabled major advances in early cancer detection. This is particularly true for a deep proteome assay as the detection of proteins 8-9 orders of magnitude lower in abundance than common plasma proteins can be identified. Objective: This study evaluates the presence of breast cancer using label-free shotgun mass spectrometry-based proteomics on less than 1 ml of plasma in >1,100 women classified as either healthy or as newly diagnosed breast cancer patients with a focus on early-stage disease (stages 0-2). Methods: 363 banked blood samples from multiple sources were used to establish a training set consisting of 156 stage 0-4 treatment-naive breast cancer patients, 46 benign breast conditions, and 161 healthy controls. Samples were age and demographic matched between healthy, benign, and breast cancer cohorts. Samples were processed in a blinded manner using an automated and novel deep proteomic platform developed by Astrin Biosciences. This platform combines label-free quantitative mass spectrometry and a proprietary breast cancer-specific spectral library to extract features used to train a classifier to identify breast cancer patients. Blinded validation of this AI classifier was evaluated on the remaining 788 patient samples (healthy, n=374; benign, n=81; breast cancer, n=333) to confirm performance characteristics. Results: Preliminary analyses from our training set of deep proteomic profiling identified over 8,600 proteins in total and up to 7,500 proteins per patient (median 6,926 proteins) with a dynamic range spanning over 8 orders of magnitude. Differentially expressed proteins in early-stage breast cancer patients from healthy patient controls were also observed. Our AI classifier, using a leave-one-out cross-validation (LOOCV) approach on the interim cohort, separated healthy controls from breast cancer patients with an ROC of >95% and a specificity and sensitivity greater than 85% across all stages. Conclusion: We have developed a highly sensitive blood-based assay that utilizes deep proteomic profiling to identify distinctive cancer specific signatures in women who are undergoing screening for breast cancer. This work enables us to develop a protein-based classifier from plasma for early detection of breast cancer. Impact on screening strategies: This assay advances clinical diagnostics by identifying proteomic markers for early detection of breast cancer. This innovative approach enhances screening strategies for women with dense breasts who are at average or high-risk based on family history, specific genetic mutations such as BRCA1/2, race, or other factors. Acknowledgements: We thank all of the members at Astrin Biosciences who have contributed endless hours dedicated to ending cancer. We are also grateful to all the donors who generously contributed blood samples to this study. Citation Format: A. Horrmann, Y. Travadi, G. Schaap, K. Mallery, K. J. Kamalanathan, N. Bristow, C. Galeano-Garces, S. Y. Bae, A. Hesch, C. Rungkittikhun, B. R. Konety, J. M. Drake. Deep Proteomics and AI Classifier for Early Breast Cancer Detection [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD5-03.