Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth

Abstract

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Nonlinear Modeling and Simulation of Tumor Growth

“…The model provides resolution at various tissue physical scales, including the microvasculature, and quantifies functional links of molecular factors to phenotype that currently for the most part can only be tentatively established through laboratory or clinical observation. This mathematical and computational approach allows observable properties of a tumor, e.g., its morphology, to be used to both understand the underlying cellular physiology and predict subsequent growth (or treatment outcome), providing a bridge between observable, morphologic properties of the tumor and its prognosis [56,182].…”

Computational modeling of brain tumors: discrete, continuum or hybrid?

“…Firstly, in contrast to conventional wet-lab experimental methods, such in silico models offer a powerful platform to reproducibly alter parameters and thus investigate their impact on the cancer system studied, at a rapid pace and in a cost-efficient way [42]. Secondly, computational models have demonstrated the ability of providing a useful hypothesis generating tool for refocusing experimental in vitro and in vivo works [54]. Thirdly, from a practical clinical perspective, computational modeling has already been applied, with some promise, to simulating the impact of chemotherapy, radiotherapy, and drug delivery on brain tumors [19].…”

“…For example, GBM cells exhibit a variety of point mutations (molecular level) [35] that can affect microvascular remodeling (microscopic level) which in turn impacts tumor size, shape, and composition (macroscopic level) [33]. To date, while some brain tumor modeling studies have dealt with the interaction of processes between cellular and macroscopic levels (for a recent review, see [54]), only very few works made an attempt to quantitatively establish the relationship between the molecular and cellular levels. 2) Level of complexity versus computational cost.…”

“…producing (auto-and paracrine) signals and degrading the neighboring tissue with proteases [34]. However, despite a vast amount of qualitative findings, conventional cancer research has made few gains in exploring the quantitative relationship between these very complicated intra-and intercellular signaling processes and the behavior they trigger on the microscopic and macroscopic scales [54]. It is here where we and others argue that systems biology [42] can provide useful insights, which may eventually promote the development of new cancer diagnostic and therapeutic techniques.…”

Multiscale Cancer Modeling

“…Macroscopic scale-Models at this scale focus on the dynamics of the gross tumor behavior including morphology, shape, extent of vascularization, and invasion, under different environmental conditions (17). Microscopic details of tissue structure are averaged over short spatial scales to produce a description of the macroscopic-level tissue properties.…”