Modeling the Effect of the Metastatic Microenvironment on Phenotypes Conferred by Estrogen Receptor Mutations Using a Human Liver Microphysiological System

Abstract

Reciprocal coevolution of tumors and their microenvironments underlies disease progression, yet intrinsic limitations of patient-derived xenografts and simpler cell-based models present challenges towards a deeper understanding of these intercellular communication networks. To help overcome these barriers and complement existing models, we have developed a human microphysiological system (MPS) model of the human liver acinus, a common metastatic site, and have applied this system to estrogen receptor (ER)+ breast cancer. In addition to their hallmark constitutive (but ER-dependent) growth phenotype, different ESR1 missense mutations, prominently observed during estrogen deprivation therapy, confer distinct estrogen-enhanced growth and drug resistant phenotypes not evident under cell autonomous conditions. Under low molecular oxygen within the physiological range (~5–20%) of the normal liver acinus, the estrogen-enhanced growth phenotypes are lost, a dependency not observed in monoculture. In contrast, the constitutive growth phenotypes are invariant within this range of molecular oxygen suggesting that ESR1 mutations confer a growth advantage not only during estrogen deprivation but also at lower oxygen levels. We discuss the prospects and limitations of implementing human MPS, especially in conjunction with in situ single cell hyperplexed computational pathology platforms, to identify biomarkers mechanistically linked to disease progression that inform optimal therapeutic strategies for patients.

Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors

“…We refer to this type of MPS as a “human biomimetic MPS”. 3 We have continuously evolved and improved the human biomimetic liver MPS models to address the complexity of studying absorption, distribution, metabolism, excretion and toxicity (ADMET), 19,51,54 as well as disease models including MAFLD/metabolic syndrome 38,39,51 and liver metastases, 55,56 using different versions of the models including: the sequentially layered, self-assembly liver (SQL-SAL); 51 the liver acinus MPS (LAMPS); 38 and the vascularized liver acinus MPS (vLAMPS). 39 A variety of other human liver MPS have been harnessed in the study of NAFLD/MAFLD.…”

Applications of the microphysiology systems database for experimental ADME-Tox and disease models

“…A metastatic breast cancer disease model has been developed using the LAMPS model to understand how cancer cells behave and function within the metastatic microenvironment of the liver. 43 Shown in Fig. 10 is an example dataset for this disease model downloaded from the MPS-Db.…”

“…10B). 43 One workflow utilizing the MPS-Db disease portals to develop a MPS disease model would be to start with the clinical data portal to identify clinically relevant phenotypes that the MPS model would need to recapitulate. Molecular targets and pathways related to the disease phenotype are then identified using the links to Gene Expression Omnibus or KEGG databases.…”

“…The Y537S and D538G mutant cells confer a proliferative advantage compared to the wild type. 43 In this study, three breast cancer cell lines were transduced with an mCherry fluorescent protein biosensor, and using non-invasive fluorescence imaging, the proliferation of the cancer cells was followed by monitoring the increase in intensity of the biosensor over time ( Fig. 10A ).…”

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

“…Imaging, especially with the many commercial and custom biosensors (Newman and Zhang 2014; Senutovitch et al 2015), can provide important real-time functional readouts including cell tracking, protein expression, ion concentration, enzyme activity, ROS, apoptosis, and other functions, provided the device design supports online imaging. Imaging of the 3D spatial relationships in the model can be important in establishing the organization of the cells, and interrogating subsets of the cells, such as the growth of cancer cells in an organ model of a metastatic niche (Miedel et al 2019; Rao et al 2019). For high-resolution confocal imaging, it is important that the device is constructed with an optical-quality, coverslip-thick “window” through which to image the cells and that the cells in the device are within the working distance of the objective, which may be >1 mm at 20× and <0.2 mm at 40× (Vernetti et al 2016).…”