Adhesion strength of tumor cells predicts metastatic disease in vivo

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.

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Publication Details

Journal
Cell Reports
Published
2025-03-01
DOI
https://doi.org/10.1016/j.celrep.2025.115359
Citations
9
Primary Topic
Cellular Mechanics and Interactions
Type
article
Field-Weighted Citation Impact
10.36

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article

Adhesion strength of tumor cells predicts metastatic disease in vivo

Aditya Kumar, Pranjali Beri, Katherine G. Birmingham, Neal Patel et al.
9 citations
Cell Reports
Cellular Mechanics and Interactions
10.36
article

Adhesion strength of tumor cells predicts metastatic disease in vivo

Aditya Kumar, Pranjali Beri, Katherine G. Birmingham, Neal Patel, Benjamin Yeoman, Adam Jeffrey Engler, Jeremy Tuler, Somaye Zare, Thomas G. Molley, Sarah Klein, Madison A. Kane, Parag Katira, Anne M. Wallace, Hayley Sperinde
article en
9 citations

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.

Cell ReportsVol. 44(3)
San Diego State University (US), UC San Diego Health System (US), University of California San Diego (US), Sanford Consortium for Regenerative Medicine (US), La Jolla Bioengineering Institute (US)
National Science Foundation, National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 1%
Cellular Mechanics and Interactions
10.36
DOIOpen Access PDFOpenAlex
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