Adhesion strength of tumor cells predicts metastatic disease in vivo
Madison A. Kane, Katherine G. Birmingham, Benjamin Yeoman, Neal Patel, Hayley Sperinde, Thomas G. Molley, Pranjali Beri, Jeremy Tuler, Aditya Kumar, Sarah Klein, Somaye Zare, Anne M. Wallace, Parag Katira, Adam J. Engler
Abstract
Although only a fraction of tumor cells contribute to metastatic disease, no prognostic biomarkers currently exist to identify these cells. We show that a physical marker-adhesion strength-predicts metastatic potential in a mouse breast cancer model and that it may stratify human disease. Cells disseminating from murine mammary tumors are weakly adherent, and, when pre-sorted by adhesion, primary tumors created from strongly adherent cells exhibit fewer lung metastases than weakly adherent cells do. We demonstrate that admixed cancer lines can be separated by label-free adhesive signatures. When applied to murine metastatic tumors, adhesion retrospectively predicts metastatic disease with 100% specificity, 85% sensitivity, and area under the curve (AUC) of 0.94. Cells from human reduction mammoplasties have a higher adhesion strength versus resected human tumors, which may also be stratified between invasive and more indolent cancers. Thus, highly metastatic cells may have a distinct physical phenotype that may be a predictive marker of clinical outcomes.