Different contractility modes control cell escape from multicellular spheroids and tumor explants.

Cells can adapt their active contractile properties to switch between dynamical migratory states and static homeostasis. Collective tissue surface tension, generated among others by the cortical contractility of single cells, can keep cell clusters compact, while a more bipolar, anisotropic contractility is predominantly used by mesenchymal cells to pull themselves into the extracellular matrix (ECM). Here, we investigate how these two contractility modes relate to cancer cell escape into the ECM. We compare multicellular spheroids from a panel of breast cancer cell lines with primary tumor explants from breast and cervical cancer patients by measuring matrix contraction and cellular spreading into ECM mimicking collagen matrices. Our results in spheroids suggest that tumor aggressiveness is associated with elevated contractile traction and reduced active tissue surface tension, allowing cancer cell escape. We show that it is not a binary switch but rather the interplay between these two contractility modes that is essential during this process. We provide further evidence in patient-derived tumor explants that these two contractility modes impact cancer cells' ability to leave cell clusters within a primary tumor. Our results indicate that cellular contractility is an essential factor during the formation of metastases and thus may be suitable as a prognostic criterion for the assessment of tumor aggressiveness.

The Mechanical Fingerprint of Circulating Tumor Cells (CTCs) in Breast Cancer Patients.

Circulating tumor cells (CTCs) are a potential predictive surrogate marker for disease monitoring. Due to the sparse knowledge about their phenotype and its changes during cancer progression and treatment response, CTC isolation remains challenging. Here we focused on the mechanical characterization of circulating non-hematopoietic cells from breast cancer patients to evaluate its utility for CTC detection. For proof of premise, we used healthy peripheral blood mononuclear cells (PBMCs), human MDA-MB 231 breast cancer cells and human HL-60 leukemia cells to create a CTC model system. For translational experiments CD45 negative cells-possible CTCs-were isolated from blood samples of patients with mamma carcinoma. Cells were mechanically characterized in the optical stretcher (OS). Active and passive cell mechanical data were related with physiological descriptors by a random forest (RF) classifier to identify cell type specific properties. Cancer cells were well distinguishable from PBMC in cell line tests. Analysis of clinical samples revealed that in PBMC the elliptic deformation was significantly increased compared to non-hematopoietic cells. Interestingly, non-hematopoietic cells showed significantly higher shape restoration. Based on Kelvin-Voigt modeling, the RF algorithm revealed that elliptic deformation and shape restoration were crucial parameters and that the OS discriminated non-hematopoietic cells from PBMC with an accuracy of 0.69, a sensitivity of 0.74, and specificity of 0.63. The CD45 negative cell population in the blood of breast cancer patients is mechanically distinguishable from healthy PBMC. Together with cell morphology, the mechanical fingerprint might be an appropriate tool for marker-free CTC detection.

Detecting heterogeneity in and between breast cancer cell lines.

BACKGROUND: Cellular heterogeneity in tumor cells is a well-established phenomenon. Genetic and phenotypic cell-to-cell variability have been observed in numerous studies both within the same type of cancer cells and across different types of cancers. Another known fact for metastatic tumor cells is that they tend to be softer than their normal or non-metastatic counterparts. However, the heterogeneity of mechanical properties in tumor cells are not widely studied. RESULTS: Here we analyzed single-cell optical stretcher data with machine learning algorithms on three different breast tumor cell lines and show that similar heterogeneity can also be seen in mechanical properties of cells both within and between breast tumor cell lines. We identified two clusters within MDA-MB-231 cells, with cells in one cluster being softer than in the other. In addition, we show that MDA-MB-231 cells and MDA-MB-436 cells which are both epithelial breast cancer cell lines with a mesenchymal-like phenotype derived from metastatic cancers are mechanically more different from each other than from non-malignant epithelial MCF-10A cells. CONCLUSION: Since stiffness of tumor cells can be an indicator of metastatic potential, this result suggests that metastatic abilities could vary within the same monoclonal tumor cell line.