Biomechanical Properties of Cancer Cells

Abstract

Since the crucial role of the microenvironment has been highlighted, many studies have been focused on the role of biomechanics in cancer cell growth and the invasion of the surrounding environment. Despite the search in recent years for molecular biomarkers to try to classify and stratify cancers, much effort needs to be made to take account of morphological and nanomechanical parameters that could provide supplementary information concerning tissue complexity adaptation during cancer development. The biomechanical properties of cancer cells and their surrounding extracellular matrix have actually been proposed as promising biomarkers for cancer diagnosis and prognosis. The present review first describes the main methods used to study the mechanical properties of cancer cells. Then, we address the nanomechanical description of cultured cancer cells and the crucial role of the cytoskeleton for biomechanics linked with cell morphology. Finally, we depict how studying interaction of tumor cells with their surrounding microenvironment is crucial to integrating biomechanical properties in our understanding of tumor growth and local invasion.

Biophysical reviews top five: atomic force microscopy in biophysics

“…AFM-based mechanobiological measurements are now widely performed, in particular for living normal and cancerous cells (see reviews: Krieg et al 2019 ; Runel et al 2021 ). The elasticity (Young’s modulus) of living cells was first quantified to be 0.013–0.15 MPa from the force-versus-indentation curve measurements on a lung carcinoma cell (Weisenhorn et al 1993 ).…”

Analysis of U87 glioma cells by dielectrophoresis

“…Recent studies have demonstrated effective tools to investigate mostly mechanical properties of glioma cells using fluorescence microscopy [15][16][17][18][19][20][21][22][23][24][25][26][27], 2-photon imaging [11,28], atomic force microscopy [16,29,30], single-cell force spectroscopy [16], and microfluidic technologies [31][32][33][34][35][36][37][38][39]. In contrast to mechanical features, electrical properties of glioma cells have not been extensively interrogated.…”

Nanoscale prognosis of colorectal cancer metastasis from AFM image processing of histological sections

“…Therefore, indexing assessment of the metastatic state and its early prediction is fundamental for enlightening cancer progression, improving early cancer prognosis and developing therapeutic schemes 3,4 . Tissue microenvironmental factors, including stiffness and topography (nuclei's shape, morphology, and texture specificity), contribute to the targeting preferences of metastatic cancers [5][6][7][8][9] because biological and mechanical/topographic parameters are associated with cancer cell proliferation, migration and metastasis 7,10,11 . Cancer cells regulate their stiffness to match the ECM local environment by adjusting viability to different structural proteins' complex ECM topographical environment.…”

“…Along the above lines, a novel methodology of probing the mechanics of tumours emerged as a supportive method to find the link between the mechanical properties of single tumour cells and their metastatic potential [18][19][20] . However, although several techniques exist, including the atomic force microscopy (AFM) 5,21,22 , to measure the mechanical properties of single cells, information on the mechanics of tumour cells in the ECM is missing because most measurements are made on cultured tumour cells 10,23 . Besides, each method has a particular set of parameters that do not consider patient-to-2 patient variations, which is an additional drawback in comparing different studies 5,24 .…”

“…However, although several techniques exist, including the atomic force microscopy (AFM) 5,21,22 , to measure the mechanical properties of single cells, information on the mechanics of tumour cells in the ECM is missing because most measurements are made on cultured tumour cells 10,23 . Besides, each method has a particular set of parameters that do not consider patient-to-2 patient variations, which is an additional drawback in comparing different studies 5,24 . Machine vision and learning methods were also applied as complementary approaches to microscopic histopathological examination and molecular-based approaches for cancer prediction and prognosis [25][26][27] .…”