Abstract 4073: Deep plasma proteomics for early-stage breast cancer detection

Abstract Annual breast cancer screening is a critical piece of women’s health. While mammography remains the gold standard, increased breast density leads to decreased sensitivity, 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 0-2). 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 proteins can be identified which are 8-9 orders of magnitude less abundant than the most common plasma proteins. This study evaluates the presence of breast cancer using label-free shotgun mass spectrometry-based proteomics on less than 1 ml of plasma from 1,259 women classified as either healthy or newly diagnosed treatment naïve breast cancer patients with a focus on early-stage disease. We trained using a cohort of 845 women, comprising of 466 healthy and 379 with breast cancer and validated on 397 women (195 healthy and 202 breast cancer) using a protein-based machine learning classifier to distinguish these groups. All plasma samples were processed in a blinded manner 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 7,064 and 7,054, respectively. The validation performance achieved 92.3% specificity for healthy controls and an overall sensitivity of 92.6% with an AUC of 0.975 (95% CI: 0.961-0.987) for breast cancer patients. Sensitivity broken down by clinical stage (0-IV), molecular subtypes (HR+/HER2+, HR-/HER2+, HR+/HER2-, and HR-/HER2-) and pathological subtypes (LCIS, DCIS, ILC, and IDC) were ≥85% and indistinguishable across all stages and subtypes. Gene set enrichment analyses (GSEA) identified pathways including epithelial-to-mesenchymal transition (EMT) and PI3K-AKT signaling as enriched in the breast cancer samples, highlighting that our test can identify cancer-related proteins in early-stage patients. Overall, 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 and 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. Citation Format: Alec Horrmann, Yash Travadi, Jacob Carey, Kevin Mallery, Ella Boytim, Grant Schaap, Carissa Rungkittikhun, Kaylee Kamalanathan, Nathaniel R. Bristow, Catalina Galeano-Garces, Adam Groth, Alexa R. Hesch, Pooja Advani, Justin Hwang, Badrinath R. Konety, Justin M. Drake. Deep plasma proteomics for early-stage breast cancer detection abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4073.