Bioengineered Hydrogels Recapitulate Fibroblast Heterogeneity in Cancer.

Recently mapped transcriptomic landscapes reveal the extent of heterogeneity in cancer-associated fibroblasts (CAFs) beyond previously established single-gene markers. Functional analyses of individual CAF subsets within the tumor microenvironment are critical to develop more accurate CAF-targeting therapeutic strategies. However, there is a lack of robust preclinical models that reflect this heterogeneity in vitro. In this study, single-cell RNA sequencing datasets acquired from head and neck squamous cell carcinoma tissues to predict microenvironmental and cellular features governing individual CAF subsets are leveraged. Some of these features are then incorporated into a tunable hyaluronan-based hydrogel system to culture patient-derived CAFs. Control over hydrogel degradability and integrin adhesiveness enabled derivation of the predominant myofibroblastic and inflammatory CAF subsets, as shown through changes in cell morphology and transcriptomic profiles. Last, using these hydrogel-cultured CAFs, microtubule dynamics are identified, but not actomyosin contractility, as a key mediator of CAF plasticity. The recapitulation of CAF heterogeneity in vitro using defined hydrogels presents unique opportunities for advancing the understanding of CAF biology and evaluation of CAF-targeting therapeutics.

Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation.

Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.

Erratum: Publisher's Note: "Magnetic force-based cell manipulation for in vitro tissue engineering" [APL Bioeng. 7, 031504 (2023)].

[This corrects the article DOI: 10.1063/5.0138732.].

Magnetic force-based cell manipulation for in vitro tissue engineering.

Cell manipulation techniques such as those based on three-dimensional (3D) bioprinting and microfluidic systems have recently been developed to reconstruct complex 3D tissue structures in vitro. Compared to these technologies, magnetic force-based cell manipulation is a simpler, scaffold- and label-free method that minimally affects cell viability and can rapidly manipulate cells into 3D tissue constructs. As such, there is increasing interest in leveraging this technology for cell assembly in tissue engineering. Cell manipulation using magnetic forces primarily involves two key approaches. The first method, positive magnetophoresis, uses magnetic nanoparticles (MNPs) which are either attached to the cell surface or integrated within the cell. These MNPs enable the deliberate positioning of cells into designated configurations when an external magnetic field is applied. The second method, known as negative magnetophoresis, manipulates diamagnetic entities, such as cells, in a paramagnetic environment using an external magnetic field. Unlike the first method, this technique does not require the use of MNPs for cell manipulation. Instead, it leverages the magnetic field and the motion of paramagnetic agents like paramagnetic salts (Gadobutrol, MnCl2, etc.) to propel cells toward the field minimum, resulting in the assembly of cells into the desired geometrical arrangement. In this Review, we will first describe the major approaches used to assemble cells in vitro-3D bioprinting and microfluidics-based platforms-and then discuss the use of magnetic forces for cell manipulation. Finally, we will highlight recent research in which these magnetic force-based approaches have been applied and outline challenges to mature this technology for in vitro tissue engineering.

Making In Vitro Tumor Models Whole Again.

As a reductionist approach, patient-derived in vitro tumor models are inherently still too simplistic for personalized drug testing as they do not capture many characteristics of the tumor microenvironment (TME), such as tumor architecture and stromal heterogeneity. This is especially problematic for assessing stromal-targeting drugs such as immunotherapies in which the density and distribution of immune and other stromal cells determine drug efficacy. On the other end, in vivo models are typically costly, low-throughput, and time-consuming to establish. Ex vivo patient-derived tumor explant (PDE) cultures involve the culture of resected tumor fragments that potentially retain the intact  TME of the original tumor. Although developed decades ago, PDE cultures have not been widely adopted likely because of their low-throughput and poor long-term viability. However, with growing recognition of the importance of patient-specific TME in mediating drug response, especially in the field of immune-oncology, there is an urgent need to resurrect these holistic cultures. In this Review, the key limitations of patient-derived tumor explant cultures are outlined and technologies that have been developed or could be employed to address these limitations are discussed. Engineered holistic tumor explant cultures may truly realize the concept of personalized medicine for cancer patients.

Hepatocellular carcinoma organoid co-cultures mimic angiocrine crosstalk to generate inflammatory tumor microenvironment.

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the second leading cause of cancer worldwide. Despite approvals of several therapeutics to treat advanced HCC in the past few years, the impact of anti-angiogenic treatment on HCC patient overall survival remains limited. This suggests there may be alternative, perfusion-independent roles of endothelial cells that support tumor progression. Thus, we leveraged a well-defined hydrogel system to establish co-culture models to mimic and characterize the angiocrine crosstalk between HCC and endothelial cells in vitro. Co-cultures of HCC cell lines or patient-derived xenograft organoids with endothelial cells exhibited the upregulation of MCP-1, IL-8 and CXCL16, suggesting that the HCC-endothelial interactions established in our models recapitulate known angiocrine signaling. Additionally, by subjecting co-cultures and mono-cultures to RNA sequencing, transcriptomic analysis revealed an upregulation in the expression of genes associated with tumor necrosis factor (TNF) signaling, such as that of chemokines, suggesting that endothelial cells induce HCC cells to generate an inflammatory microenvironment by recruiting immune cells. Finally, HCC-endothelial angiocrine crosstalk in the co-culture models polarized macrophages towards a pro-inflammatory and pro-angiogenic phenotype, paralleling a tumor-associated macrophage subset previously reported in HCC. Together, these findings suggest that these HCC-endothelial co-culture models may serve as important models to understand and target the interplay between angiogenesis and the immune milieu.

Clinical translation of patient-derived tumour organoids- bottlenecks and strategies.

Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.

Organoids.

Organoids have attracted increasing attention because they are simple tissue-engineered cell-based in vitro models that recapitulate many aspects of the complex structure and function of the corresponding in vivo tissue. They can be dissected and interrogated for fundamental mechanistic studies on development, regeneration, and repair in human tissues. Organoids can also be used in diagnostics, disease modeling, drug discovery, and personalized medicine. Organoids are derived from either pluripotent or tissue-resident stem (embryonic or adult) or progenitor or differentiated cells from healthy or diseased tissues, such as tumors. To date, numerous organoid engineering strategies that support organoid culture and growth, proliferation, differentiation and maturation have been reported. This Primer serves to highlight the rationale underlying the selection and development of these materials and methods to control the cellular/tissue niche; and therefore, structure and function of the engineered organoid. We also discuss key considerations for generating robust organoids, such as those related to cell isolation and seeding, matrix and soluble factor selection, physical cues and integration. The general standards for data quality, reproducibility and deposition within the organoid community is also outlined. Lastly, we conclude by elaborating on the limitations of organoids in different applications, and key priorities in organoid engineering for the coming years.

Engineering stromal heterogeneity in cancer.

Based on our exponentially increasing knowledge of stromal heterogeneity from advances in single-cell technologies, the notion that stromal cell types exist as a spectrum of unique subpopulations that have specific functions and spatial distributions in the tumor microenvironment has significant impact on tumor modeling for drug development and personalized drug testing. In this Review, we discuss the importance of incorporating stromal heterogeneity and tumor architecture, and propose an overall approach to guide the reconstruction of stromal heterogeneity in vitro for tumor modeling. These next-generation tumor models may support the development of more precise drugs targeting specific stromal cell subpopulations, as well as enable improved recapitulation of patient tumors in vitro for personalized drug testing.

Hot or cold: Bioengineering immune contextures into in vitro patient-derived tumor models.

In the past decade, immune checkpoint inhibitors (ICI) have proven to be tremendously effective for a subset of cancer patients. However, it is difficult to predict the response of individual patients and efforts are now directed at understanding the mechanisms of ICI resistance. Current models of patient tumors poorly recapitulate the immune contexture, which describe immune parameters that are associated with patient survival. In this Review, we discuss parameters that influence the induction of different immune contextures found within tumors and how engineering strategies may be leveraged to recapitulate these contextures to develop the next generation of immune-competent patient-derived in vitro models.

Three-Dimensional Macroporous Sponge for the Culture of Hepatocellular Carcinoma Patient-Derived Xenograft Organoids.

This protocol focuses on the culture of cells harvested from hepatocellular carcinoma (HCC) patient-derived xenografts (PDXs) as organoids using a cellulosic macroporous sponge scaffold. Compared with many other epithelial cancer types, the viability of HCC cells directly derived from patients or PDX models is notoriously challenging to maintain in vitro. We previously developed a macroporous sponge scaffold uniquely designed to provide biochemical and mechanical cues that support the culture of normal hepatocytes as spheroids with maintained functionality. Leveraging our success using this sponge scaffold to maintain normal hepatocytes in vitro, we recently demonstrated that a similar sponge scaffold enables the maintenance of HCC PDX cells as organoids with preserved viability, molecular features, and heterogeneity.

Hydrogel-based colorectal cancer organoid co-culture models.

The lack of cancer-associated fibroblasts (CAFs) in patient-derived organoid (PDO) models is a major limitation as CAFs contribute to tumor progression and drug resistance. In the present study, we addressed this problem by establishing in vitro conditions that enable the co-culture of colorectal cancer (CRC) PDO with patient-derived CAFs. Considering that the CRC extracellular matrix is high in hyaluronan and collagen I, we hypothesized that hyaluronan-gelatin hydrogels may serve as a suitable alternative 3D matrix to traditionally used basement membrane extracts to support the co-culture of CRC PDO and CAFs. We report the development of in vitro models consisting of CRC PDO encapsulated within a well-defined three-dimensional (3D) hyaluronan-gelatin hydrogel and co-cultured with patient-derived CAFs. Through RNA- and whole -exome sequencing, we first show that these hydrogels are capable of maintaining key molecular characteristics of the original patient tumors in CRC PDO but not support the culture of CAFs. Further, based on our findings that CRC PDO culture medium poorly supports CAF viability, we developed a co-culture strategy that maintains the viability of both CRC PDO and CAFs. We found that even in the absence of growth factors conventionally used to support CRC PDO culture, CAFs were able to maintain the proliferation of the cultured CRC PDO in the hydrogels and restore distinct biological pathways absent in the PDO culture alone but present in patient tissues. Lastly, we demonstrate that these CRC PDO-CAFs co-culture models are suitable for evaluating standard-of-care drugs, making them potentially very useful for realizing personalized cancer medicine. STATEMENT OF SIGNIFICANCE: We report the development of an engineered tumor microenvironment consisting of colorectal cancer patient-derived organoids (CRC PDO) encapsulated within a well-defined three-dimensional (3D) hyaluronan-gelatin hydrogel and co-cultured with patient-derived cancer-associated fibroblasts (CAFs). Through sequential culture, we found that in the absence of growth factors added to the co-culture, CAFs were able to maintain the proliferation of the cultured CRC PDO in the hydrogels and restore distinct biological pathways absent in the PDO culture alone but present in patient tissues. Lastly, we demonstrate that these CRC PDO-CAFs models are suitable for evaluating standard-of-care drugs, making them potentially very useful for realizing personalized cancer medicine.

Hydrogels to engineer tumor microenvironments in vitro.

In recent years, hydrogel-based three-dimensional tumor models have become increasingly mainstream for cancer research. Hydrogels enable recapitulation of biochemical and biophysical cues in the tumor microenvironment (TME) for the culture of cancer and stromal cells. While there is increasing insight into how cancer-stromal interactions support tumor progression and drug resistance, much remains to be understood for the successful development of therapeutic targets that are capable of controlling tumors in patients. This review aims to first describe both acellular and cellular characteristics of the TME, focusing on cancer cell interactions with the extracellular matrix, fibroblasts, endothelial cells and immune cells. We will then discuss hydrogel systems that have been developed in the past four years to mimic these interactions in the TME and finally propose future directions in the field of in vitro tumor modeling.

Deep learning enables automated scoring of liver fibrosis stages.

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85-0.95 versus ANN (AUROC of up to 0.87-1.00), MLR (AUROC of up to 0.73-1.00), SVM (AUROC of up to 0.69-0.99) and RF (AUROC of up to 0.94-0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

Datasets describing the growth and molecular features of hepatocellular carcinoma patient-derived xenograft cells grown in a three-dimensional macroporous hydrogel.

This data article presents datasets associated with the research article entitled "Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer" (Fong et al., 2018) [1]. A three-dimensional macroporous sponge system was used to generate in vitro counterparts to various hepatocellular carcinoma patient-derived xenograft (HCC-PDX) lines. This article describes the viability, proliferative capacity and molecular features (genomic and transcriptomic profiles) of the cultured HCC-PDX cells. The sequencing datasets are made publicly available to enable critical or further analyzes.

Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, often manifesting at the advanced stage when cure is no longer possible. The discrepancy between preclinical findings and clinical outcome in HCC is well-recognized. So far, sorafenib is the only targeted therapy approved as first-line therapy for patients with advanced HCC. There is an urgent need for improved preclinical models for the development of HCC-targeted therapies. Patient-derived xenograft (PDX) tumor models have been shown to closely recapitulate human tumor biology and predict patient drug response. However, the use of PDX models is currently limited by high costs and low throughput. In this study, we engineered in vitro conditions conducive for the culture of HCC-PDX organoids derived from a panel of 14 different HCC-PDX lines through the use of a three-dimensional macroporous cellulosic sponge system. To validate the in vitro HCC-PDX models, both in vivo and in vitro HCC-PDX models were subjected to whole exome sequencing and RNA-sequencing. Correlative studies indicate strong concordance in genomic and transcriptomic profiles as well as intra-tumoral heterogeneity between each matched in vitro-in vivo HCC-PDX pairs. Furthermore, we demonstrate the feasibility of using these in vitro HCC-PDX models for drug testing, paving the way for more efficient preclinical studies in HCC drug development.

Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85-0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.