Quantitative extracellular matrix proteomics to study mammary and liver tissue microenvironments

Normal epithelium exists within a dynamic extracellular matrix (ECM) that is tuned to regulate tissue specific epithelial cell function. As such, ECM contributes to tissue homeostasis, differentiation, and disease, including cancer. Though it is now recognized that the functional unit of normal and transformed epithelium is the epithelial cell and its adjacent ECM, we lack a basic understanding of tissue-specific ECM composition and abundance, as well as how physiologic changes in ECM impact cancer risk and outcomes. While traditional proteomic techniques have advanced to robustly identify ECM proteins within tissues, methods to determine absolute abundance have lagged. Here, with a focus on tissues relevant to breast cancer, we utilize mass spectrometry methods optimized for absolute quantitative ECM analysis. Employing an extensive protein extraction and digestion method, combined with stable isotope labeled Quantitative conCATamer (QconCAT) peptides that serve as internal standards for absolute quantification of protein, we quantify 98 ECM, ECM-associated, and cellular proteins in a single analytical run. In rodent models, we applied this approach to the primary site of breast cancer, the normal mammary gland, as well as a common and particularly deadly site of breast cancer metastasis, the liver. We find that mammary gland and liver have distinct ECM abundance and relative composition. Further, we show mammary gland ECM abundance and relative compositions differ across the reproductive cycle, with the most dramatic changes occurring during the pro-tumorigenic window of weaning-induced involution. Combined, this work suggests ECM candidates for investigation of breast cancer progression and metastasis, particularly in postpartum breast cancers that are characterized by high metastatic rates. Finally, we suggest that with use of absolute quantitative ECM proteomics to characterize tissues of interest, it will be possible to reconstruct more relevant in vitro models to investigate tumor-ECM dynamics at higher resolution. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.