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Sci Rep. 2019 Mar 27;9(1):5276. doi: 10.1038/s41598-019-40888-w.

Tumor Ensemble-Based Modeling and Visualization of Emergent Angiogenic Heterogeneity in Breast Cancer.

Author information

1
Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Maryland, USA.
2
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, USA.
3
Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Maryland, USA.
4
Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Maryland, USA. pathak@mri.jhu.edu.
5
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, USA. pathak@mri.jhu.edu.
6
Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Maryland, USA. pathak@mri.jhu.edu.

Abstract

There is a critical need for new tools to investigate the spatio-temporal heterogeneity and phenotypic alterations that arise in the tumor microenvironment. However, computational investigations of emergent inter- and intra-tumor angiogenic heterogeneity necessitate 3D microvascular data from 'whole-tumors' as well as "ensembles" of tumors. Until recently, technical limitations such as 3D imaging capabilities, computational power and cost precluded the incorporation of whole-tumor microvascular data in computational models. Here, we describe a novel computational approach based on multimodality, 3D whole-tumor imaging data acquired from eight orthotopic breast tumor xenografts (i.e. a tumor 'ensemble'). We assessed the heterogeneous angiogenic landscape from the microvascular to tumor ensemble scale in terms of vascular morphology, emergent hemodynamics and intravascular oxygenation. We demonstrate how the abnormal organization and hemodynamics of the tumor microvasculature give rise to unique microvascular niches within the tumor and contribute to inter- and intra-tumor heterogeneity. These tumor ensemble-based simulations together with unique data visualization approaches establish the foundation of a novel 'cancer atlas' for investigators to develop their own in silico systems biology applications. We expect this hybrid image-based modeling framework to be adaptable for the study of other tissues (e.g. brain, heart) and other vasculature-dependent diseases (e.g. stroke, myocardial infarction).

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