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Breast cancer is the most common cancer in women worldwide, and while screening, diagnostic tools and treatments have improved over the decades, many women still succumb to the disease. The heterogeneity of breast cancer and the many subtypes and sites of origin mean that it is remains a difficult disease to fully elucidate. Additionally, the extracellular matrix plays a major role in the development, progression and metastasis of the disease and these cell-ECM interactions are still poorly understood. Animal and two-dimensional models have been the gold standard for understanding the disease and developing novel treatments. However, these models possess undesirable characteristics and do not fully represent the complexity of the mammary tumor microenvironment. This has created a need for mammary biomimetic tools, which in recent years have been developed. These tools are known as three-dimensional (3D) models and they all aim to replicate the physiological complexity of breast cancer by facilitating 3D cell-cell interactions, providing scaffolds or by encapsulating cells and tissues in a extracellular matrix. These bioengineered tools have already shown to be a promising field of research for breast cancer, and have already facilitated deeper researcher into hallmarks such as angiogenesis and the relationship with metastasis, local invasion and also has allowed complex co- and tri-cultures consisting of many cell types. We postulate that 3D models will be the next large step needed to study breast cancer at a more deeper and complex level and will provide chemotherapeutics and pharmaceutical companies platforms for more reliable drug development.

Background: Understanding how medical experts and their patients process and transfer information is of critical importance for efficient health care provision. Behavioral economics has explored similar credence markets where economic incentives, information asymmetry, and cognitive bias can impact patient and surgeon choice. The aim of the current study is to explore how framing and behavioral bias affect elective restorative surgery decision-making, such as breast reconstruction following cancer treatment. Methods: The authors' study uses a cross-sectional survey data set of specialist surgeons (n = 53), breast care nurses (n = 101), and former or current breast cancer patients (n = 689). Data collected include participant demographics, medical history, a battery of cognitive bias tests, and a behavioral framing experiment. Results: This study finds statistically significant differences in breast reconstruction surgery preference by patients and nurses when decision options are framed in different ways (i.e., positively versus negatively). The authors' analysis of surgeons, nurses, and patients shows no statistically significant difference across eight common forms of cognitive bias. Rather, the authors find that the behavioral biases are prevalent to the same extent in each group. This may indicate that differences in experience and education seem not to mitigate biases that may affect patient choices and medical professional's recommendations. The authors' multivariate analysis identifies patient age (p < 0.0001), body mass index, and self-perceived health (p < 0.05) as negative correlates for choice of implant-based reconstruction. Conclusion: For surgeons, nurses, and patients, the authors find uniform evidence of cognitive bias; more specifically, for patients and nurses, the authors find inconsistency in preference for type of surgical therapy chosen when alternative procedures are framed in different ways (i.e., framing bias).

BACKGROUND: Optical slice microscopy is commonly used to observe cellular morphology in 3D tissue culture, e.g., the formation of cell-derived networks. The morphometric quantification of these networks is essential to study the cellular phenotype. Commonly, the quantitative measurements are performed on 2D projections of the image stack, resulting in the loss of information in the third dimension. Currently available 3D image analysis tools rely on manual interactions with the software and are therefore not feasible for large datasets.FINDINGS: Here we present Qiber3D, an open-source image processing toolkit. The software package includes the essential image analysis procedures required for image processing, from the raw image to the quantified data. Optional pre-processing steps can be switched on/off depending on the input data to allow for analyzing networks from a variety of sources. Two reconstruction algorithms are offered to meet the requirements for a wide range of network types. Furthermore, Qiber3D's rendering capabilities enable the user to inspect each step of the image analysis process interactively to ensure the creation of an optimal workflow for each application.CONCLUSIONS: Qiber3D is implemented as a Python package, and its source code is freely available at https://github.com/theia-dev/Qiber3D. The toolkit was designed using a building block principle to enable the analysis of a variety of structures, such as vascular networks, neuronal structures, or scaffolds from numerous input formats. While Qiber3D can be used interactively in the Python console, it is aimed at unsupervised automation to process large image datasets efficiently.

Local drug delivery offers a means of achieving a high concentration of therapeutic agents directly at the tumor site, whilst minimizing systemic toxicity. For heterogenous cancers such as glioblastoma, multimodal therapeutic approaches hold promise for better efficacy. Herein, we aimed to create a well-defined and reproducible drug delivery system that also incorporates gold nanorods for photothermal therapy. Solvent-assisted micromolding was used to create uniform sacrificial templates in which microscale hydrogels were formed with and without gold nanorods throughout their structure. The microscale hydrogels could be loaded with doxorubicin, releasing it over a period of one week, causing toxicity to glioma cells. Since these microscale hydrogels were designed for direct intratumoral injection, therefore bypassing the blood-brain barrier, the highly potent breast cancer therapeutic doxorubicin was repurposed for use in this study. By contrast, the unloaded hydrogels were well tolerated, without decreasing cell viability. Irradiation with near-infrared light caused heating of the hydrogels, showing that if concentrated at an injection site, these hydrogels maybe able to cause anticancer activity through two separate mechanisms.

Three-dimensional (3D) cell culture models that provide a biologically relevant microenvironment are imperative to investigate cell-cell and cell-matrix interactions in vitro. Semi-synthetic star-shaped poly(ethylene glycol) (starPEG)-heparin hydrogels are widely used for 3D cell culture due to their highly tuneable biochemical and biomechanical properties. Changes in gene expression levels are commonly used as a measure of cellular responses. However, the isolation of high-quality RNA presents a challenge as contamination of the RNA with hydrogel residue, such as polymer or glycosaminoglycan fragments, can impact template quality and quantity, limiting effective gene expression analyses. Here, we compare two protocols for the extraction of high-quality RNA from starPEG-heparin hydrogels and assess three subsequent purification techniques. Removal of hydrogel residue by centrifugation was found to be essential for obtaining high-quality RNA in both isolation methods. However, purification of the RNA did not result in further improvements in RNA quality. Furthermore, we show the suitability of the extracted RNA for cDNA synthesis of three endogenous control genes confirmed via quantitative polymerase chain reaction (qPCR). The methods and techniques shown can be tailored for other hydrogel models based on natural or semi-synthetic materials to provide robust templates for all gene expression analyses.

Here we highlight 35 researchers approximately under the age of 35. Age, of course, is just a number—our target was emerging early-career academics. Contributors were recruited in a self-propagating “pay-it-forward” manner, with each invitee being suggested by a peer who had already contributed. The final collection is an inspiring look at the challenges the current generation of materials researchers are tackling.

Precision medicine approaches that inform clinical management of individuals with cancer are progressively advancing. Patient-derived explants (PDEs) provide a patient-proximal ex vivo platform that can be used to assess sensitivity to standard of care (SOC) therapies and novel agents. PDEs have several advantages as a patient-proximal model compared to current preclinical models, as they maintain the phenotype and microenvironment of the individual tumor. However, the longevity of PDEs is not compatible with the timeframe required to incorporate candidate therapeutic options identified by whole exome sequencing (WES) of the patient's tumor. This review investigates how PDE longevity varies across tumor streams and how this is influenced by tissue preparation. Improving longevity of PDEs will enable individualized therapeutics testing, and thus contribute to improving outcomes for people with cancer.

Breast cancer is a leading cause of cancer‐associated death in women. The clinical management of breast cancers is normally carried out using a combination of chemotherapy, surgery and radiation therapy. The majority of research investigating breast cancer therapy until now has mainly utilized two‐dimensional (2D) in vitro cultures or murine models of disease. However, there has been significant uptake of three‐dimensional (3D) in vitro models by cancer researchers over the past decade, highlighting a complimentary model for studies of radiotherapy, especially in conjunction with chemotherapy. In this review, we underline the effects of radiation therapy on normal and malignant breast cells and tissues, and explore the emerging opportunities that pre‐clinical 3D models offer in improving our understanding of this treatment modality.

Breast cancer is primarily derived from mammary epithelial cells, the main cell type in human mammary glands. The majority of knowledge gained thus far around breast cancer has come from research using immortalized epithelial cell lines. The use of primary cells derived from breast tissue can be used in research to provide more biological relevance representative of the heterogeneous nature of breast cancer development and metastasis in its natural microenvironment. However, the successful isolation and propagation of human primary mammary gland cells can be costly and difficult due to their complex in vivo microenvironment and sensitivity when isolated. Here, we present a gentle isolation method for viable human mammary epithelial cells (hMECs) and donor-matched human mammary fibroblasts (hMFbs) from human mammary gland tissue. We isolated, expanded and passaged the hMECs and hMFbs in vitro and characterized cultures using cell-specific markers. A total of four primary cell lines were isolated and established from normal breast tissue and characterized through various markers, including pan cytokeratin (panCK), CK14, CD44, CD31, fibronectin and vimentin by immunofluorescence. To determine functional potential for subsequent studies, epithelial cells were examined via Matrigel® assays to assess spheroid development. Both cell type cultures expressed lineage specific markers with hMECs but not hMFbs forming spheroid structures in 3D Matrigel® assays. Our analyses confirm the successful isolation of two different cell phenotypes from normal breast tissues. This robust technique provides an inexpensive and accessible approach for mammary cell isolation.

Optical slice microscopy is commonly used to characterize the morphometric features of 3D cellular cultures, such as in vitro vascularization. However, the quantitative analysis of those structures is often performed on a single 2D maximum intensity projection image, limiting the accuracy of data obtained from 3D cultures. Here, we present a protocol for the quantitative analysis of z stack images, utilizing Fiji, Amira, and WinFiber3D. This protocol facilitates the in-depth examination of vascular-like structures within 3D cell culture models. For complete details on the use and execution of this protocol, please refer to Koch et al. (2020).

The use of animal models along with the employment of advanced and sophisticated stereological methods for assessing bone quality combined with the use of statistical methods to evaluate the effectiveness of bone therapies has made it possible to investigate the pathways that regulate bone responses to medical devices. Image analysis of histomorphometric measurements remains a time-consuming task, as the image analysis software currently available does not allow for automated image segmentation. Such a feature is usually obtained by machine learning and with software platforms that provide image-processing tools such as MATLAB. In this study, we introduce a new MATLAB algorithm to quantify immunohistochemically stained critical-sized bone defect samples and compare the results with the commonly available Aperio Image Scope Positive Pixel Count (PPC) algorithm. Bland and Altman analysis and Pearson correlation showed that the measurements acquired with the new MATLAB algorithm were in excellent agreement with the measurements obtained with the Aperio PPC algorithm, and no significant differences were found within the histomorphometric measurements. The ability to segment whole slide images, as well as defining the size and the number of regions of interest to be quantified, makes this MATLAB algorithm a potential histomorphometric tool for obtaining more objective, precise, and reproducible quantitative assessments of entire critical-sized bone defect image data sets in an efficient and manageable workflow.

The extracellular matrix (ECM) provides cues to direct mammogenesis, tumourigenesis and metastatic processes. Over the past several decades, two-dimensional (2D) culture models have been invaluable in furthering our understanding of the tumor microenvironment (TME), however, they still do not accurately emulate the associated biological complexities. In contrast, three-dimensional (3D) culture models provide a more physiologically relevant platform to study relevant physicochemical signals, stromal-epithelial cell interactions, vascular and immune components, and cell-ECM interactions in the human breast microenvironment. A common thread that may weave these multiple interactions are the proteoglycans (PGs), a prominent family of molecules in breast tissue. This review will discuss how these PGs contribute to the breast cancer TME and provide a summary of the traditional and emerging technologies that have been utilized to better understand the role of PGs during malignant transformation. Furthermore, this review will emphasize the differences that PGs exhibit between normal tissues and tumor ECM, providing a rationale for the investigation of underexplored roles of PGs in breast cancer progression using state-of-the-art 3D culture models.

Despite the bone marrow microenvironment being widely recognised as a key player in cancer research, the current animal models that represent a human haematopoietic system lack the contribution of the humanised marrow microenvironment. Here we describe a murine model that relies on the combination of an orthotopic humanised tissue-engineered bone construct (ohTEBC) with patient-specific bone marrow (BM) cells to create a humanised bone marrow (hBM) niche capable of supporting the engraftment of human haematopoietic cells. Results showed that this model supports the engraftment of human CD34+ cells from a healthy BM with human haematopoietic cells migrating into the mouse BM, human BM compartment, spleen and peripheral blood. We compared these results with the engraftment capacity of human CD34+ cells obtained from patients with multiple myeloma (MM). We demonstrated that CD34+ cells derived from a diseased BM had a reduced engraftment potential compared to healthy patients and that a higher cell dose is required to achieve engraftment of human haematopoietic cells in peripheral blood. Finally, we observed that hematopoietic cells obtained from the mobilised peripheral blood of patients yields a higher number of CD34+, overcoming this problem. In conclusion, this humanised mouse model has potential as a unique and patient-specific pre-clinical platform for the study of tumour–microenvironment interactions, including human bone and haematopoietic cells, and could, in the future, serve as a drug testing platform.

The plasticity of the tumour microenvironment is a key contributor to cancer development and progression. Here, we present a bioengineered breast tumour angiogenesis model comprised of mammary derived epithelial, endothelial and fibroblast cells, to dissect the mechanisms of cancer-associated fibroblasts (CAFs) on microvascular-like network formation and epithelial spheroid morphology. Primary patient-derived mammary endothelial cells, normal breast fibroblasts (NBF, patient matched) and CAFs were cultured within three-dimensional (3D) semi-synthetic hydrogels where CAFs promoted an increase in the density and morphology of the microvascular-like network. The mammary microenvironment also increased the number of MCF-10a epithelial spheroids when compared with a non-mammary microenvironment, and a malignant mammary microenvironment resulted in further morphological differences in the epithelial spheroids. The morphological changes observed following interactions between breast CAFs and endothelial cells, highlight the plasticity of the malignant stroma in tumour vascularisation. Our in vitro bioengineered breast cancer microenvironment provides a robust model to study cell-cell and cell-matrix interactions. Statement of Significance In recent years there has been an increase in the sophistication of 3D culture models, however less attention has been paid to the cell source utilised. In this study, we describe the influence of a normal and malignant stromal microenvironment on vessel-like behaviour in a 3D model. Using a semi-synthetic hydrogel, we studied the effects of mammary-derived cancer-associated fibroblasts and normal fibroblasts on human umbilical vein endothelial cells or human mammary microvascular endothelial cells. An increase in vessel-like network and epithelial cell density was seen in a mammary versus non-mammary microenvironment. This study highlights the importance of using tissue-specific endothelial cells in cancer research and demonstrates th

Multiphasic in vitro models with cross-scale heterogeneity in matrix properties and/or cellular composition can reflect the structural and compositional complexity of living tissues more faithfully, thereby creating new options for pathobiology and drug development studies. Herein, a new class of tunable microgel-in-gel materials is reported that build on a versatile platform of multifunctional poly(ethylene glycol)-heparin gel types and integrates monodisperse, cell-laden microgels within cell-laden bulk hydrogel matrices. A novel microfluidic approach was developed to enable the high-throughput fabrication of microgels of in situ adjustable diameters, stiffness, degradability and biomolecular functionalization. By choosing structure and composition of the microgel and the bulk gel compartments independently, our microgel-in-gel arrangements provide cross-scale control over tissue-mimetic features and pave the way for culture systems with designed mesoenvironmental characteristics. The potentialities of the introduced approach are exemplarily shown by creating a reductionistic in vitro model of vascularized prostate cancer tissue.