The extracellular matrix (ECM) is a critical cue to direct tumorigenesis and metastasis. Although two-dimensional (2D) culture models have been widely employed to understand breast cancer microenvironments over the past several decades, the 2D models still exhibit limited success. Overwhelming evidence supports that three dimensional (3D), physiologically relevant culture models are required to better understand cancer progression and develop more effective treatment. Such platforms should include cancer-specific architectures, relevant physicochemical signals, stromal–cancer cell interactions, immune components, vascular components, and cell-ECM interactions found in patient tumors. This review briefly summarizes how cancer microenvironments (stromal component, cell-ECM interactions, and molecular modulators) are defined and what emerging technologies (perfusable scaffold, tumor stiffness, supporting cells within tumors and complex patterning) can be utilized to better mimic native-like breast cancer microenvironments. Furthermore, this review emphasizes biophysical properties that differ between primary tumor ECM and tissue sites of metastatic lesions with a focus on matrix modulation of cancer stem cells, providing a rationale for investigation of underexplored ECM proteins that could alter patient prognosis. To engineer breast cancer microenvironments, we categorized technologies into two groups: (1) biochemical factors modulating breast cancer cell-ECM interactions and (2) 3D bioprinting methods and its applications to model breast cancer microenvironments. Biochemical factors include matrix-associated proteins, soluble factors, ECMs, and synthetic biomaterials. For the application of 3D bioprinting, we discuss the transition of 2D patterning to 3D scaffolding with various bioprinting technologies to implement biophysical cues to model breast cancer microenvironments.

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering.