Progression-dependent transport heterogeneity of breast cancer liver metastases as a factor in therapeutic resistance

Abstract

Metastatic disease is a major cause of mortality in cancer patients. While many drug delivery strategies for anticancer therapeutics have been developed in preclinical studies of primary tumors, the drug delivery properties of metastatic tumors have not been sufficiently investigated. Therapeutic efficacy hinges on efficient drug permeation into the tumor microenvironment, which is known to be heterogeneous thus potentially making drug permeation heterogeneous, also. In this study, we have identified that 4T1 liver metastases, treated with pegylated liposomal doxorubicin, have unfavorable and heterogeneous transport of doxorubicin. Our drug extravasation results differ greatly from analogous studies with 4T1 tumors growing in the primary site. A probabilistic tumor population model was developed to estimate drug permeation efficiency and drug kinetics of liver metastases by integrating the transport and structural properties of tumors and delivered drugs. The results demonstrate significant heterogeneity in metastases with regard to transport properties of doxorubicin within the same animal model, and even within the same organ. These results also suggest that the degree of heterogeneity depends on the stage of tumor progression and that differences in transport properties can define transport-based tumor phenotypes. These findings may have valuable clinical implications by illustrating that therapeutic agents can permeate and eliminate metastases of “less resistant” transport phenotypes, while sparing tumors with more “resistant” transport properties. We anticipate that these results could challenge the current paradigm of drug delivery into metastases, highlight potential caveats for therapies that may alter tumor perfusion, and deepen our understanding of the emergence of drug transport-based therapeutic resistance.

Extension of a multiphase tumour growth model to study nanoparticle delivery to solid tumours

“…But even though we base our values on literature, extensive further experimental validation is essential and should include in vitro and in vivo studies as well as clinical analysis. The focus of further research will be the further verification and validation of our model and the employed parameters based on experimental data, as for instance provided by Ziemys et al [36] and similar studies.…”

“…Here, we use a constant permeability coefficient of P v = 3.5 × 10 −4 mm/s [53] and a pore radius of r 0 = 150 nm. In order to investigate nanoparticle distributions in different transport regimes, we now compare dextrans with different molecular weights, similar to the experiments performed by Ziemys et al [36]. The interstitial diffusivities for 3.3 kDa, 10 kDa, 70 kDa and 2 MDa dextran determined by Chou et al [55] result in Péclet numbers in the range of 0.5 to 56 (see Table 2).…”

“…We then use this result of a grown tumour and its microenvironment as a starting point for a systematic nanoparticle transport study in Section 3.2. The setup of the geometry is inspired by the experiments conducted by Ziemys et al [36] and thus is of practical biological and clinical relevance. We study the influence of different parameters which also are of biological interest in order to characterise the transport phenotype of the tumour.…”

“…Based on those findings, Chauhan et al [65] suggest that vascular normalisation improves the convective delivery of nanoparticles. Ziemys et al [36] conclude from their results that the dominant transport mechanism is diffusion. Our results show that only if diffusive transport in the IF is significant, do nanoparticles penetrate into the centre of the tumour [66].…”

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

“…Furthermore, it is more than questionable if a fully resolved blood vessel network offers more information on the actual quantities of interest especially when considering the inherently stochastic nature of angiogenesis and tumor vasculature remodeling which precludes predicting the specific in vivo network topology. Relevant quantities which could be obtained from in silico models and can actually be acquired through imaging are microvascular densities, hotspots of vascularization or very badly vascularized regions inside the tumor, and averaged transport properties to detect hypoxic and drug resistant areas . To obtain this information from resolved models, averaging over several different simulations is necessary .…”