Long-term fluorescence hyperspectral imaging of on-chip treated co-culture tumour spheroids to follow clonal evolution

Paracrine Ovarian Cancer Cell-Derived CSF1 Signaling Regulates Macrophage Migration Dynamics in a 3D Microfluidic Model that Recapitulates In Vivo Infiltration Patterns in Patient-Derived Xenografts

A high density of macrophages in the ovarian cancer microenvironment is associated with disease progression and poor outcomes. Understanding cancer-macrophage interaction mechanisms that establish this pro-tumorigenic microenvironment is critical for developing macrophage-targeted therapies. Here, 3D microfluidic assays and patient-derived xenografts are utilized to define the role of cancer-derived colony stimulating factor 1 (CSF1) on macrophage infiltration dynamics toward ovarian cancer cells. It is demonstrated that multiple ovarian cancer models promote the infiltration of macrophages into a 3D extracellular matrix in vitro in a cell density-dependent manner. Macrophages exhibit directional migration and increased migration speed under both direct interactions with cancer cells embedded within the matrix and paracrine crosstalk with cancer cells seeded in an independent microchannel. It is also found that platinum-based chemotherapy increases macrophage recruitment and the levels of cancer cell-derived CSF1. Targeting CSF1 signaling under baseline or chemotherapy-treatment conditions reduces the number of infiltrated macrophages. It is further shown that results obtained with the 3D microfluidic model reflect the recruitment profiles of macrophages in patient-derived xenografts in vivo. These findings highlight the role of CSF1 signaling in establishing macrophage-rich ovarian cancer microenvironments, as well as the utility of microfluidic models in recapitulating 3D tumor ecosystems and dissecting cancer-macrophage signaling.

Radiotherapy on-chip: microfluidics for translational radiation oncology

The clinical importance of radiotherapy in the treatment of cancer patients justifies the development and use of research tools at the fundamental, pre-clinical, and ultimately clinical levels, to investigate their toxicities and synergies with systemic agents on relevant biological samples. Although microfluidics has prompted a paradigm shift in drug discovery in the past two decades, it appears to have yet to translate to radiotherapy research. However, the materials, dimensions, design versatility and multiplexing capabilities of microfluidic devices make them well-suited to a variety of studies involving radiation physics, radiobiology and radiotherapy. This review will present the state-of-the-art applications of microfluidics in these fields and specifically highlight the perspectives offered by radiotherapy on-a-chip in the field of translational radiobiology and precision medicine. This body of knowledge can serve both the microfluidics and radiotherapy communities by identifying potential collaboration avenues to improve patient care.

Carboplatin response in preclinical models for ovarian cancer: comparison of 2D monolayers, spheroids, ex vivo tumors and in vivo models

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Among the key challenges in developing effective therapeutics is the poor translation of preclinical models used in the drug discovery pipeline. This leaves drug attrition rates and costs at an unacceptably high level. Previous work has highlighted the discrepancies in therapeutic response between current in vitro and in vivo models. To address this, we conducted a comparison study to differentiate the carboplatin chemotherapy response across four different model systems including 2D monolayers, 3D spheroids, 3D ex vivo tumors and mouse xenograft models. We used six previously characterized EOC cell lines of varying chemosensitivity and performed viability assays for each model. In vivo results from the mouse model correlated with 2D response in 3/6 cell lines while they correlated with 3D spheroids and the ex vivo model in 4/6 and 5/5 cell lines, respectively. Our results emphasize the variability in therapeutic response across models and demonstrate that the carboplatin response in EOC cell lines cultured in a 3D ex vivo model correlates best with the in vivo response. These results highlight a more feasible, reliable, and cost-effective preclinical model with the highest translational potential for drug screening and prediction studies in EOC.

Technological Advances in Tumor-On-Chip Technology: From Bench to Bedside

Simple Summary

Various 3D in vitro tumor models are rapidly advancing cancer research. Unlike animal models, they can be produced quickly and are amenable to high-throughput studies. Growing tumor spheroids in microfluidic tumor-on-chip platforms has particularly elevated the capabilities of such models. Tumor-on-chip devices can mimic multiple aspects of the dynamic in vivo tumor microenvironment in a precisely controlled manner. Moreover, new technologies for the on- and off-chip analysis of these tumor mimics are continuously emerging. There is thus an urgent need to review the latest developments in this rapidly progressing field. Here, we present an overview of the technological advances in tumor-on-chip technology by reviewing state-of-the-art tools for on-chip analysis. In particular, we evaluate the potential for tumor-on-chip technology to guide personalized cancer therapies. We strive to appeal to cancer researchers and biomedical engineers alike, informing on current progress, while provoking thought on the outstanding developments needed to achieve clinical-stage research.

Abstract

Tumor-on-chip technology has cemented its importance as an in vitro tumor model for cancer research. Its ability to recapitulate different elements of the in vivo tumor microenvironment makes it promising for translational medicine, with potential application in enabling personalized anti-cancer therapies. Here, we provide an overview of the current technological advances for tumor-on-chip generation. To further elevate the functionalities of the technology, these approaches need to be coupled with effective analysis tools. This aspect of tumor-on-chip technology is often neglected in the current literature. We address this shortcoming by reviewing state-of-the-art on-chip analysis tools for microfluidic tumor models. Lastly, we focus on the current progress in tumor-on-chip devices using patient-derived samples and evaluate their potential for clinical research and personalized medicine applications.

Carboplatin sensitivity in epithelial ovarian cancer cell lines: The impact of model systems

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy in North America, underscoring the need for the development of new therapeutic strategies for the management of this disease. Although many drugs are pre-clinically tested every year, only a few are selected to be evaluated in clinical trials, and only a small number of these are successfully incorporated into standard care. Inaccuracies with the initial in vitro drug testing may be responsible for some of these failures. Drug testing is often performed using 2D monolayer cultures or 3D spheroid models. Here, we investigate the impact that these different in vitro models have on the carboplatin response of four EOC cell lines, and in particular how different 3D models (polydimethylsiloxane-based microfluidic chips and ultra low attachment plates) influence drug sensitivity within the same cell line. Our results show that carboplatin responses were observed in both the 3D spheroid models tested using apoptosis/cell death markers by flow cytometry. Contrary to previously reported observations, these were not associated with a significant decrease in spheroid size. For the majority of the EOC cell lines (3 out of 4) a similar carboplatin response was observed when comparing both spheroid methods. Interestingly, two cell lines classified as resistant to carboplatin in 2D cultures became sensitive in the 3D models, and one sensitive cell line in 2D culture showed resistance in 3D spheroids. Our results highlight the challenges of choosing the appropriate pre-clinical models for drug testing.