Quantitative biophysical metrics for rapid evaluation of ovarian cancer metastatic potential

Apratim Mukherjee, Haonan Zhang, Katherine Ladner, Megan Brown, Jacob Urbanski, Joseph P. Grieco, Rakesh K. Kapania, Emil Lou, Bahareh Behkam, Eva M. Schmelz, Amrinder S. Nain

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous submesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here we fabricated extracellular matrix mimicking suspended fiber networks: cross-hatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying interfiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease while a force-disease biphasic relationship exhibited F-actin stress fiber network dependence. However, unique to suspended fibers, coiling occurring at the tips of protrusions and not the length or breadth of protrusions displayed the strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on a timescale of hours.

Original languageEnglish (US)
Article numberar55
JournalMolecular biology of the cell
Issue number6
StatePublished - May 15 2022

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© 2022 Mukherjee et al.


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