Abstract PS2-08-03: A multi-site evaluation of the performance of the MasSpec Pen for surgical margin analysis during breast cancer surgeries

Abstract Introduction Breast cancer is the second leading cause of cancer deaths among women worldwide. Breast-conserving surgery (BCS), the most common treatment option. Achieving negative margins is critical for prolonged disease-free survival, making accurate intraoperative margin assessment essential. Molecular technologies offer the opportunity to incorporate cancer-specific markers into clinical decision-making to improve cancer detection. Our team has developed the MasSpec Pen (MSPen), a handheld device connected to a mass spectrometer for rapid, gentle molecular analysis of human tissues. This technology, combined with statistical learning, enables tissue disease state classification. Here, we evaluate the performance of the MSPen for intraoperative breast cancer detection. Methods Banked normal breast, ductal carcinoma in situ (DCIS) and invasive ductal and lobular carcinomas, IDC and ILC respectively, were analyzed with the MSPen coupled to Mass Spectrometer. Following analysis, the tissue region analyzed was demarcated, and the samples snap-frozen, sectioned, H&E stained and evaluated by a pathologist. Classification models were built with logistic regression and the least absolute shrinkage and selector operator (LASSO) method to distinguish normal breast from breast cancer tissue. The MSPen system was adapted as a mobile cart for implementation and testing within the surgical workflows of two different hospitals, intraoperatively and in pathology. The classifiers were used to predict on data obtained from the intraoperative analyses and validated against final histopathological reports. Preliminary data To date, we have analyzed 478 banked human breast tissue samples using the MSPen, including normal breast (n = 205), healthy lymph nodes (n = 26), IDC (n = 101), metastatic IDC in lymph nodes (n = 44), DCIS (n = 61), and ILC (n = 41). The rich mass spectral data obtained revealed a high relative abundance of metabolites and lipid species previously identified as potentially diagnostic of breast cancer. Using this data, we built three statistical classifiers to differentiate: 1) normal breast from IDC, 2) normal breast from ILC, and 3) normal breast from cancer (IDC, ILC, and DCIS). The classifier distinguishing IDC from normal breast achieved an accuracy of 95.6% on a test set and 95.5% on a validation set. The ILC classifier reached 82.1% accuracy on a test set. The model distinguishing normal from cancer (IDC, ILC, and DCIS) achieved 87.5% accuracy on a test set and 84% on a validation set. Classifier refinements are ongoing to enhance performance. In a pilot clinical study, we demonstrated feasibility for ex vivo and in vivo tissue analysis using the MSPen in 29 breast surgeries. At Ben Taub site, we deployed a compact, mobile, battery-operated MSPen cart, which is easily transported to any operating room without requiring line power.We have successfully performed intraoperative in vivo and ex vivo analyses of breast tissues during BCS and mastectomies. The mobile cart enables real-time analysis of resected specimens alongside pathologists conducting margin assessments, enhancing integration between the MSPen system and clinical surgical workflows. Novel aspect The MSPen is being experimentally used for real-time surgical margin evaluation intraoperatively and in the pathology room at two hospitals. Citation Format: K. Liebenberg, R. Costa, S. Bench, E. Ma, M. Hutchinson, F. Jackobs, J. Mardick, E. Silberfein, C. Hsu, A. Thompson, S. Carter, E. Bonefas, N. Chandandeep, J. Wang, J. Suliburk, L. Eberlin.. A multi-site evaluation of the performance of the MasSpec Pen for surgical margin analysis during breast cancer surgeries [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 PS2-08-03.