Abstract 4604: Can breast cancer cells be distinguished from blood cells by mechanical parameters? A label-free CTC detection approach

Background: Even years after successful treatment of the primary tumor about one third of breast cancer patients are suffering from metastatic relapse. A possible reason might be hematogenous spread. Therefore circulating tumor cells (CTCs) in the blood might be interesting and easy accessible surrogate markers for disease monitoring. Usually, detection methods are based on cellular parameters like cell surface markers, size, density or flexibility. Due to phenotypical, mechanical and functional changes during cancer progression and treatment response, isolation of CTC subpopulations remains very challenging. Here we focused on the characterization of CTC mechanics to evaluate the utility of mechanical parameters for CTC separation from blood. Methods: For the first time, we investigated the active and passive mechanical CTC properties such as elasticity, viscosity and contractile force exertion. In an optical rheometer, cells are deformed non-invasively by a dual beam trap. Thus, the relative deformation of a single cell can be measured while phase contrast and fluorescent images are taken. For proof of premise we used peripheral blood mononuclear cells (PBMC) isolated from a healthy donor and human GFP-expressing MDA-MB 231 breast cancer cells to mimic a CTC model system and to measure cell type specific mechanical deformation profiles. For translational experiments blood samples were collected from breast cancer patients. Hematopoietic cells were depleted using CD45. The cell suspensions were applied to the optical stretcher and rheological parameters were measured in the same manner. Phase contrast images of potentially interesting cells were analyzed manually to eliminate dead cells and to identify possible CTC candidates. Results: We were able to reveal distinct mechanical profiles from hematopoietic cells and breast cancer cells, respectively. The optical deformability at the end of the step stress creep experiment (EOS) was significantly different in both, the model and the translational, systems comparing healthy PPMC to MDA-MB 231 cells respective possible CTC from breast cancer patients. Evaluation of secondary parameters like viscosity and activity using a machine learning algorithm allowed for a clear distinction between hematopoietic and cancer cells in the model system. Preliminary results of the translational system suggest that identification of CTC candidates exclusively based on mechanical parameters might be possible. Conclusion: Togeth