Monocyte-derived macrophage assisted breast cancer cell invasion as a personalized, predictive metric to score metastatic risk

• Type of Material: Article
• Authors: Keon-Young Park, Georgia Institute of Technology; Gande Li, Georgia Institute of Technology; Manu Platt, Emory University
• Language: English
• Date: 2015-09-09
• Publisher: Nature Publishing Group: Open Access Journals - Option C
• Publication Version: Final Published Version
• Copyright Statement: © 2015, Macmillan Publishers Limited
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/srep13855
• Title of Journal or Parent Work: Scientific Reports
• ISSN: 2045-2322
• Volume: 5
• Start Page: 13855
• End Page: 13855
• Grant/Funding Information: Funding was also provided in part by a generous donation from the Giglio Family to the Wallace H. Coulter Department of Biomedical Engineering (MOP).; We would like to thank Georgia Research Alliance for financial support (MOP) as well as partial support from American Heart Association Pre-Doctoral Fellowship (KYP).
• Research Categories: Health Sciences, Oncology; Engineering, Biomedical

Abstract

Patient-to-patient variability in breast cancer progression complicates clinical treatment decisions. Of women undergoing prophylactic mastectomies, many may not have progressed to indolent forms of disease and could have benefited from milder, localized therapy. Tumor associated macrophages contribute significantly to tumor invasion and metastasis, with cysteine cathepsin proteases as important contributors. Here, a method is demonstrated by which variability in macrophage expression of cysteine cathepsins, their inhibitor cystatin C, and kinase activation can be used to train a multivariate model and score patients for invasion risk. These enzymatic profiles were used to predict macrophage-assisted MCF-7 breast cancer cell invasion in the trained computational model. To test these predictions, a priori, signals from monocytes isolated from women undergoing mastectomies were input to score their cancer invasion potential in a patient-specific manner, and successfully predicted that patient monocytes with highest predicted invasion indices matched those with more invasive initial diagnoses of the nine patients tested. Together this establishes proof-of-principle that personalized information acquired from minimally invasive blood draws may provide useful information to inform oncologists and patients of invasive/metastatic risk, helping to make decisions regarding radical mastectomy or milder, conservative treatments to save patients from hardship and surgical recovery.