Abstract

BackgroundCellular 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.ResultsHere 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.ConclusionSince 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.

Highlights

  • Cellular heterogeneity in tumor cells is a well-established phenomenon

  • We use only two mechanical features calculated from these measurements: 1. Relative long axis deformation at the end of stretch (Deformation EOS), and 2

  • The end-of-experiment deformation (EOE) can be interpreted as the degree of cell plasticity of the cell under a given applied load or strain. This plasticity is in principle a coarse-grained property which contains contributions of the actin, microtubule and intermediate filament network (Kubitschke et al 2017). Since both EOE and EOS are linear measurements, a two-fold change in the observed deformation corresponds to a two-fold change in elastic modulus

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Summary

Introduction

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. Studies have shown that cells from a single cancer typically contain multiple genetically distinct subgroups (Cleary et al 2014; Meacham and Morrison 2013; Gay et al 2016; Marusyk and Polyak 2010). Such high level of heterogeneity contributes to the reason why cancer is hard to cure (McGranahan and Swanton 2017; Mann et al 2016; Koren and Bentires-Alj 2015). Two main theories have been proposed to explain the origin of tumor cell heterogeneity: the existence of cancer stem cells (Magee et al 2012) and clonal evolution (McGranahan and Swanton 2017). Variations in gene expression lead to molecular variations which in turn affect cellular shape and function

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