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Abstract Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts, a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that carcinoma-associated fibroblasts promote cancer cell proliferation and stemness primarily through hepatocyte growth factor–MET paracrine signaling and activation of cyclin-dependent kinases. Disrupting these tumor–stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor–stromal interactions into a drug testing workflow that could identify effective therapies for individual patients. 

Imaging of surface-enhanced Raman scattering (SERS) nanoparticles (NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context. This is not practical and suitable for clinical applications. Here, we propose a custom-made Raman spectrometer with computer-vision-based positional tracking and monocular depth estimation using deep learning (DL) for the visualization of 2D and 3D SERS NPs imaging, respectively. In addition, the SERS NPs used in this study (hyaluronic acid-conjugated SERS NPs) showed clear tumor targeting capabilities (target CD44 typically overexpressed in tumors) by anex vivoexperiment and immunohistochemistry. The combination of Raman spectroscopy, image processing, and SERS molecular imaging, therefore, offers a robust and feasible potential for clinical applications. 

Abstract The tumor microenvironment (TME) promotes proliferation, drug resistance, and invasiveness of cancer cells. Therapeutic targeting of the TME is an attractive strategy to improve outcomes for patients, particularly in aggressive cancers such as triple-negative breast cancer (TNBC) that have a rich stroma and limited targeted therapies. However, lack of preclinical human tumor models for mechanistic understanding of tumor–stromal interactions has been an impediment to identify effective treatments against the TME. To address this need, we developed a three-dimensional organotypic tumor model to study interactions of patient-derived cancer-associated fibroblasts (CAF) with TNBC cells and explore potential therapy targets. We found that CAFs predominantly secreted hepatocyte growth factor (HGF) and activated MET receptor tyrosine kinase in TNBC cells. This tumor–stromal interaction promoted invasiveness, epithelial-to-mesenchymal transition, and activities of multiple oncogenic pathways in TNBC cells. Importantly, we established that TNBC cells become resistant to monotherapy and demonstrated a design-driven approach to select drug combinations that effectively inhibit prometastatic functions of TNBC cells. Our study also showed that HGF from lung fibroblasts promotes colony formation by TNBC cells, suggesting that blocking HGF-MET signaling potentially could target both primary TNBC tumorigenesis and lung metastasis. Overall, we established the utility of our organotypic tumor model to identify and therapeutically target specific mechanisms of tumor–stromal interactions in TNBC toward the goal of developing targeted therapies against the TME. Implications: Leveraging a state-of-the-art organotypic tumor model, we demonstrated that CAFs-mediated HGF-MET signaling drive tumorigenic activities in TNBC and presents a therapeutic target. 

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%. 

The extracellular matrix (ECM) influences biological processes associated with tissue development and disease progression. However, robust cell‐free techniques to control fiber alignment of naturally derived ECM proteins, such as fibronectin (Fn), remain elusive. It is demonstrated that controlled hydrodynamics of Fn solutions at the air/fluid interface of porous tessellated polymer scaffolds (TPSs) generates suspended 3D fibrillar networks with alignment across multiple length scales (<1, 1–20 μm, extended to >1 mm). The direction of the fluid flow and the architecture of the polymeric supports influence protein solution flow profiles and, subsequently, the alignment of insoluble Fn fibrils. Aligned networks of fibrillar Fn characteristically alter fibroblast phenotype, indicated by increased directional orientation, enhanced nuclear and cytoskeletal polarity, and highly anisotropic and persistent cell motility when compared with nonaligned 3D networks and 2D substrates. Engineered extracellular matrices (EECMs) establish a critically needed tool for both fundamental and applied cell biology studies, with potential applications in diverse areas such as cancer biology and regenerative medicine.