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Description
When considering factors that contribute to cancer progression, modifications to both the biological and mechanical pathways play significant roles. However, less attention is placed on how the mechanical pathways can specifically contribute to cancerous behavior. Experimental studies have found that malignant cells are significantly softer than healthy, normal cells. In a tissue environment where healthy or malignant cells exist, a distribution of cell stiffness values is observed, with the mean values used to differentiate between these two populations. Rather than focus on the mean values, emphasis will be placed on the distribution, where instances of soft and stiff cells exist in the healthy tissue environment. Since cell deformability is a trait associated with cancer, the question arises as to whether the mechanical variation observed in healthy tissue cell stiffness distributions can influence any instances of tumor growth. To approach this, a 3D discrete model of cells is used, able to monitor and predict the behavior of individual cells while determining any instances of tumor growth in a healthy tissue. In addition to the mechanical variance, the spatial arrangement of cells will also be modeled, as cell interaction could further implicate any incidences of tumor-like malignant populations within the tissue. Results have shown that the likelihood of tumor incidence is driven by both by the increases in the mechanical variation in the distributions as well as larger clustering of cells that are mechanically similar, quantified primarily through higher proliferation rates of tumor-like soft cells. This can be observed though prominent negative shifts in the mean of the distribution, as it begins to transition and show instances of earlystage tumor growth. The model reveals the impact that both the mechanical variation and spatial arrangement of cells has on tumor progression, suggesting the use of these parameters as potential novel biomarkers. With a patient-specific approach in mind, the model may be applied for early-stage cancer detection, useful to establish a timeline on tumor progression.
