Mechanobiology-Based Rapid Diagnosis and Early Prognosis of Metastatic Risk in Cancer

by Prof. Daphne Weihs

Faculty Of Biomedical Engineering, Technion - Israel Institute Of Technology
at Biological and soft-matter physics

Thu, 03 Jun 2021, 12:10
ZOOM only - Meeting ID: 874 2021 0979


The main cause of cancer-related death is metastasis; early detection or prediction are crucial. Metastasis is currently predicted via histopathology, disease-statistics, or genetics; those are often-inaccurate, not rapidly available and require known markers. We have developed a rapid (~2hr) mechanobiology-based approach to rapidly diagnose cancer and provide early prognosis of the in vitro invasiveness of cells, which we show here accurately agrees with the clinical likelihood for metastasis. Specifically, invasive cell-subsets forcefully indent impenetrable, physiological-stiffness (2.4 kPa) polyacrylamide gels, while non-invasive/benign cells do not, and the fraction of indenting cells and their attained depths provide the mechanical invasiveness measure. We have evaluated indentations of breast and pancreatic cancer cell and of freshly resected, human pancreatic and skin tissue samples. We show that the sample’s mechanical invasiveness agrees with the in vitro to trans-well migration assay (8 µm pores, 72 hrs) and literature established metastatic potential in cell lines and with the clinically determined invasiveness in tumor samples. The mechanical invasiveness of high or low-metastatic-potential were significantly different from benign/normal cells, allowing determination of clinically relevant prognostic thresholds for the evaluated cancer types. Utilizing our current database, we have developed machine learning models to automatically predict the invasiveness and metastatic risk, where 2- and 5-class models provide high sensitivity and specificity. Our innovative and unique mechanobiology-based approach provides a rapid and accurate cancer diagnosis and early prediction of metastatic likelihood in tumors, already during the time of first diagnosis, which can critically affect patient-specific treatment protocols and disease management.

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Meeting ID: 874 2021 0979

Created on 04-05-2021 by Granek, Rony (rgranek)
Updaded on 03-06-2021 by Granek, Rony (rgranek)