Predicting the role of microstructural and biomechanical cues in tumor growth and spreading

The modeling study of the effect of morphological behaviors of extracellular matrix fibers on the dynamic interaction between tumor cells and antitumor immune response

Low response rate limits the effective application of immunotherapy, in which the interactions between tumor cells and immune cells play a significant role. The strength of regulation could be mediated by extracellular matrix (ECM) fibers, which is still insufficiently investigated. In the study, the cellular potts model was utilized to explore the role of morphological properties of ECM in tumor-immune interactions. It was observed that high-density random ECM fibers delayed the interaction between tumor cells and T cells. Moreover, the tumor-immune interactions were ECM morphology-specific. Radial ECM fibers exhibited weaker inhibitory role in the process of contact between tumor cells and T cells. This study provided the useful mechanism of tumor-immune interactions from the viewpoint of morphological effect of ECM fibers, facilitating improving the efficiency of immunotherapy.

Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment

Uncontrolled proliferation of cells in a tissue caused by genetic mutations inside a cell is referred to as a tumor. A tumor which grows rapidly encounters a barrier when it grows to a certain size in presence of preexisting vasculature. This is the time when it has to find a way to go on the growth. The tumor starts to secrete tumor angiogenic factors (TAFs) and stimulate preexisting vessels to grow new sprouts. These new sprouts will find their way to the tumor in the extracellular matrix (ECM) by the gradient of TAF. As these new capillaries anastomose and reach tumor, fresh oxygen is available for the tumor and it will reinitiate the growth. Number of initial sprouts, distance of initial tumor cells from the vessel(s) and initial density of the tumor at the time of sprout formation are questions which are to be investigated. In the present study, the aim is to find the response of tumor cells and vessels to the reciprocal effects of each other in different circumstances in the tissue. Together with a mathematical formulation, a radial basis function (RBF) neural network is established to predict the number of tumor cells at different circumstances including size and distance of initial tumors from the parent vessel. A final formulation is given for the final number of tumor cells as a function of initial tumor size and distance between a parent vessel and a tumor. Results of this simulation demonstrate that, increasing the distance between a tumor and a parent vessel decreases the number of final tumor cells. Specially, this decrement becomes faster beyond a certain distance. Moreover, initial tumors in bigger domains must become much bigger before inducing angiogenesis which makes it harder for them to survive.

Combination of intratumoural micellar paclitaxel with radiofrequency ablation: efficacy and toxicity in rodents

OBJECTIVES

To determine whether radiofrequency ablation (RFA) is more effective when combined with intratumoural injection (IT) than with intravenous injection (IV) of micelles.

MATERIALS AND METHODS

Balb/c mice bearing 4T1 breast cancer were used. The tumour drug accumulation and biodistribution in major organs were evaluated at different time points after IT, IV, IT+RFA and IV+RFA. Periablational drug penetration was evaluated by quantitative analysis and pathologic staining after different treatments. For long-term outcomes, mice bearing tumours were randomised into six groups (n = 7/group): the control, IV, IT, RFA alone, IV+RFA and IT+RFA groups. The end-point survival was estimated for the different treatment groups.

RESULTS

In vivo, intratumoural drug accumulation was always much higher for IT than for IV within 48 h (p < 0.001). The IT+RFA group (3084.7 ± 985.5 μm) exhibited greater and deeper drug penetration than the IV+RFA group (686.3 ± 83.7 μm, p < 0.001). Quantitatively, the intratumoural drug accumulation in the IT+RFA group increased approximately 4.0-fold compared with that in the IV+RFA group (p < 0.001). In addition, compared with the IT treatment, the IT+RFA treatment further reduced the drug deposition in the main organs. Survival was longer in the IT+RFA group than in the IV+RFA (p = 0.033) and RF alone (p = 0.003) groups.

CONCLUSION

The use of IT+RFA achieved much deeper and greater intratumoural drug penetration and accumulation, resulting in better efficacy, and decreased the systemic toxicity of nanoparticle-delivered chemotherapy.

KEY POINTS

• Association of IT+RFA achieved much deeper and greater intratumoural drug penetration than of IV+RFA, leading to better therapeutic efficacy. • Compared with IV or IT chemotherapy alone, the combination with RFA decreased toxicity, especially in the IT+RFA group.

An approach for vascular tumor growth based on a hybrid embedded/homogenized treatment of the vasculature within a multiphase porous medium model

The aim of this work is to develop a novel computational approach to facilitate the modeling of angiogenesis during tumor growth. The preexisting vasculature is modeled as a 1D inclusion and embedded into the 3D tissue through a suitable coupling method, which allows for nonmatching meshes in 1D and 3D domain. The neovasculature, which is formed during angiogenesis, is represented in a homogenized way as a phase in our multiphase porous medium system. This splitting of models is motivated by the highly complex morphology, physiology, and flow patterns in the neovasculature, which are challenging and computationally expensive to resolve with a discrete, 1D angiogenesis and blood flow model. Moreover, it is questionable if a discrete representation generates any useful additional insight. By contrast, our model may be classified as a hybrid vascular multiphase tumor growth model in the sense that a discrete, 1D representation of the preexisting vasculature is coupled with a continuum model describing angiogenesis. It is based on an originally avascular model which has been derived via the thermodynamically constrained averaging theory. The new model enables us to study mass transport from the preexisting vasculature into the neovasculature and tumor tissue. We show by means of several illustrative examples that it is indeed capable of reproducing important aspects of vascular tumor growth phenomenologically.

An adjoint-based method for a linear mechanically-coupled tumor model: Application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging

We present an efficient numerical method to quantify the spatial variation of glioma growth based on subject-specific medical images using a mechanically-coupled tumor model. The method is illustrated in a murine model of glioma in which we consider the tumor as a growing elastic mass that continuously deforms the surrounding healthy-appearing brain tissue. As an inverse parameter identification problem, we quantify the volumetric growth of glioma and the growth component of deformation by fitting the model predicted cell density to the cell density estimated using the diffusion-weighted magnetic resonance imaging (DW-MRI) data. Numerically, we developed an adjoint-based approach to solve the optimization problem. Results on a set of experimentally measured, in vivo rat glioma data indicate good agreement between the fitted and measured tumor area and suggest a wide variation of in-plane glioma growth with the growth-induced Jacobian ranging from 1.0 to 6.0.