Abstract

Accepting the hypothesis that cancers are self-organizing, opportunistic systems, it is crucial to understand the collective behavior of cancer cells in their tumorous heterogeneous environment. In the present paper, we ask the following basic question: Is this self-organization of tumor evolution reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment? We employ a variety of nontrivial statistical microstructural descriptors that arise in the theory of heterogeneous media to characterize the spatial distributions of the nuclei of both benign brain white matter cells and brain glioma cells as obtained from histological images. These descriptors, which include the pair correlation function, structure factor and various nearest neighbor functions, quantify how pairs of cell nuclei are correlated in space in various ways. We map the centroids of the cell nuclei into point distributions to show that while commonly used local spatial statistics (e.g., cell areas and number of neighboring cells) cannot clearly distinguish spatial correlations in distributions of normal and abnormal cell nuclei, their salient structural features are captured very well by the aforementioned microstructural descriptors. We show that the tumorous cells pack more densely than normal cells and exhibit stronger effective repulsions between any pair of cells. Moreover, we demonstrate that brain gliomas are organized in a collective way rather than randomly on intermediate and large length scales. The existence of nontrivial spatial correlations between the abnormal cells strongly supports the view that cancer is not an unorganized collection of malignant cells but rather a complex emergent integrated system.

Highlights

  • Cancer is a highly complex and heterogeneous set of diseases

  • A systematic way of obtaining such statistics is to construct the Voronoi tessellation associated with the distribution of the cells. (A Voronoi tessellation is a subdivision of the plane into polygons, see the Results section for a precise definition.) the statistics of Voronoi polygon areas and number of neighbors can provide useful structural information for certain systems, such as epithelia [15], we find that they are not able to capture well the salient features of the spatial correlations in distributions of the nuclei of benign brain white matter cells and brain glioma cells nor clearly distinguish between the two

  • We have characterized the spatial distributions of the nuclei of both benign brain white matter cells and infiltrating glioma cells via a variety of nontrivial statistical microstructural descriptors, including the pair correlation function, structure factor and various nearest neighbor functions that have been profitably utilized in statistical mechanics and material science

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Summary

Introduction

Cancer is a highly complex and heterogeneous set of diseases. Heterogeneity occurs on a variety of length scales, including the genomic, phenotypic, cellular, tissue and metastatic intra-organ levels [1,2,3,4,5]. It is reasonable to expect that this self-organization would be reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment. Thomlinson and Gray showed that in well-vascularized tumor environment, the malignant cells are often organized around blood vessels into ‘‘solid rods’’ (i.e., Krogh cylinders) with predictable cellular changes in the perivascular space [6]. A crucial question is how to systematically probe and extract the structural information in model-independent manner. It has been suggested recently [7] that the powerful theoretical machinery of heterogenous materials, developed in the physical and mathematical sciences [8], be brought to bear to characterize the structure and bulk properties of the heterogeneous tumor environment. We employ techniques from the theory of heterogeneous media to characterize spatially optical images of the distribution of the nuclei of both benign brain white matter cells and brain glioma cells

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