Metastatic cancer cells for many cancers are known to have altered cytoskeletal properties, in particular to be more deformable and contractile. Consequently, shape characteristics of more metastatic cancer cells may be expected to have diverged from those of their parental cells. To examine this hypothesis we study shape characteristics of paired osteosarcoma cell lines, each consisting of a less metastatic parental line and a more metastatic line, derived from the former by in vivo selection. Two-dimensional images of four pairs of lines were processed. Statistical analysis of morphometric characteristics shows that shape characteristics of the metastatic cell line are partly overlapping and partly diverged from the parental line. Significantly, the shape changes fall into two categories, with three paired cell lines displaying a more mesenchymal-like morphology, while the fourth displaying a change towards a more rounded morphology. A neural network algorithm could distinguish between samples of the less metastatic cells from the more metastatic cells with near perfect accuracy. Thus, subtle changes in shape carry information about the genetic changes that lead to invasiveness and metastasis of osteosarcoma cancer cells.

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We study the shape characteristics of osteosarcoma cancer cell lines on surfaces of differing hydrophobicity using Zernike moments to represent cell shape. We compare the shape characteristics of four invasive cell lines with a corresponding less-invasive parental line on three substrates. Cell shapes of each pair of cell lines are quite close and display overlapping characteristics. To quantitatively study shape changes in high-dimensional parameter space we project down to principal component space and define a vector that summarizes average shape differences. Using this vector we find that three of the four pairs of cell lines show similar changes in shape, while the fourth pair shows a very different pattern of changes. We find that shape differences are sufficient to enable a neural network to classify cells accurately as belonging to the highly invasive or the less invasive phenotype. The patterns of shape changes were also reproducible for repetitions of the experiment. We also find that shape changes on different substrates show similarities between the eight cells studied, but the differences were typically not enough to permit classification. Our paper strongly suggests that shape may provide a means to read out the phenotypic state of some cell types, and shape analysis can be usefully performed using a Zernike moment representation.