Developing virtual physiology of human tumor tissue for malignancy assessment.

Abstract

Compressive stresses are linked to the malignancy state of tumors. These stresses can drive cancer cells toward a malignant phenotype. The objective of this study is to investigate how patient-specific heterogeneity of a tumor tissue influences the stresses experienced by tissue components that are believed to play important roles in malignancy state. A unique image-based, physics-driven in silico modeling is developed, replicating a breast tumor tissue with the complexity and heterogeneity as observed in humans. This model employes images acquired by Fourier transform infrared (FTIR) microscopy which images and classifies breast tissues into six components including non-cancerous, malignant, others, dense, loose, and reactive stroma. We show that heterogeneous tissues having small and disconnected pieces of malignant components experience higher stresses, highlighting the dependency of stress magnitude on components' configuration, neighborhood, and initial surface area. Our in silico model predicts stresses on pre-cancerous lesions in the range that drive them to become lethal.