Comparison of EMT-Related and Multi-Drug Resistant Gene Expression, Extracellular Matrix Production, and Drug Sensitivity in NSCLC Spheroids Generated by Scaffold-Free and Scaffold-Based Methods

Yttrium-90-doped metal-organic frameworks (MOFs) for low-dose rate internal radiation therapy of tumors

Brachytherapy, or internal radiation therapy, is a highly effective treatment option for localized tumors. Herein, injectable and biodegradable metal-organic frameworks (MOFs) were engineered to deliver the therapeutic radioisotope yttrium-90 (90Y). Particles of bimetallic MIL-100(Fe,Y) and Y-BTC, doped with 90Y and 88Y, were synthesized in a single step and retained radioyttrium in various buffer solutions. Tumor injectability and radioisotope retention were evaluated using tumor-bearing mice. In vivo analysis and calculations showed that radiolabeled MIL-100(Fe,Y) emitted more than 38% of its radioactivity, while Y-BTC emitted greater than 75% of its radioactivity, for 7 days at the tumor site upon intratumoral injection, without significant yttrium accumulation in off-target tissues. The anticancer effects of MIL-100(Fe,Y,90Y) and 90Y,Y-BTC particles were assessed using 3D multicellular tumor spheroids and a tumor-bearing mouse model, respectively. 90Y-doped MIL-100(Fe,Y) particles penetrated A549 tumor spheroids and caused superior cytotoxic effects compared to non-radioactive particles or 90YCl3, added at the same dose. Brachytherapy with 90Y-doped Y-BTC MOFs induced inhibition of B16F1 melanoma tumor growth and resulted in an increased median survival of 8.5 days compared to 4.5 days in untreated mice. This study shows the feasibility of preparing radioactive 90Y-containing biodegradable non-toxic MOF particles that are advantageous for low-dose rate internal radiotherapy.

Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation

Multicellular spheroids (MCSs) are increasingly employed as 3D cell culture models in biomedical research due to their ability to effectively replicate in vivo cell interactions, making them suitable for high-throughput drug screening. Accurate cell counting is critical for data normalization, therapeutic evaluation, and exploration of culture conditions; however, affordable software solutions for 3D cell counting using microscopic images are limited. To fill this gap, we created SUNSHINE, an innovative on-chip analytical workflow that uniquely merges optical clearing, histogram matching (HM)-assisted fluorescence calibration, and simple linear iterative clustering (SLIC) supervoxel segmentation. This tool offers an efficient method for analyzing the characteristics and counts of fluorescence-labeled nuclei within MCSs. While optical clearing improves the penetration depth of microscopic imaging, deeper regions of thicker samples often yield faint fluorescence signals. SUNSHINE resolves this issue through the HM image post-processing algorithm. Moreover, SLIC is an effective alternative to traditional contour-wise segmentation, enabling the identification of irregularly shaped fluorescent nuclei. We found that SUNSHINE generated results comparable to commercial software like Imaris and machine learning (ML)-based tools, such as StarDist and Cellpose, in our analysis of the effects of seeding density and cell type on spheroid growth. We also used it to measure the volume and spatial distribution of nuclei, focusing on the hypoxic and peripheral regions of spheroids. Overall, this study finds that SUNSHINE serves as a valuable and economical approach for characterizing cellular activity and interactions in 3D, diminishing the reliance on costly proprietary software.

Plasticity of Expression of Stem Cell and EMT Markers in Breast Cancer Cells in 2D and 3D Culture Depend on the Spatial Parameters of Cell Growth; Mathematical Modeling of Mechanical Stress in Cell Culture in Relation to ECM Stiffness

The majority of the current cancer research is based on two-dimensional cell cultures and animal models. These methods have limitations, including different expressions of key factors involved in carcinogenesis and metastasis, depending on culture conditions. Addressing these differences is crucial in obtaining physiologically relevant models. In this manuscript we analyzed the plasticity of the expression of stem cell and epithelial/mesenchymal markers in breast cancer cells, depending on culture conditions. Significant differences in marker expression were observed in different growth models not only between 2D and 3D conditions but also between two different 3D models. Differences observed in the levels of adherent junction protein E-cadherin in two different 3D models suggest that spatial parameters of cell growth and physical stress in the culture may affect the expression of junction proteins. To provide an explanation of this phenomenon on the grounds of mechanobiology, these parameters were analyzed using a mathematical model of the 3D bioprinted cell culture. The finite element mechanical model generated in this study includes an extracellular matrix and a group of regularly placed cells. The single-cell model comprises an idealized cytoskeleton, cortex, cytoplasm, and nucleus. The analysis of the model revealed that the stress generated by external pressure is transferred between the cells, generating specific stress fields, depending on growth conditions. We have analyzed and compared stress fields in two different growth conditions, each corresponding to a different elasticity of extracellular matrix. We have demonstrated that soft matrix conditions produce more stress than a stiff matrix in the single cell as well as in cellular spheroids. The observed differences can explain the plasticity of E-cadherin expression in response to mechanical stress. These results should contribute to a better understanding of the differences between various growth models.

Tumor Spheroids, Tumor Organoids, Tumor Explants, and Tumoroids: What Are the Differences between Them?

Three-dimensional (3D) cell cultures that mimic tumor microenvironment have become an essential tool in cancer research and drug response analysis, significantly enhancing our understanding of tumor biology and advancing personalized medicine. Currently, the most widely mentioned 3D multicellular culture models include spheroids, organoids, tumor explants, and tumoroids. These 3D structures, exploited for various applications, are generated from cancer and non-cancer cells of different origin using multiple techniques. However, despite extensive research and numerous studies, consistent definitions of these 3D culture models are not clearly established. The manuscript provides a comprehensive overview of these models, detailing brief history of their research, unique biological characteristics, advantages, limitations, and specific applications.

Multicellular tumor spheroids: A convenient in vitro model for translational cancer research

In the attempts to mitigate uncertainties in the results of monolayer culture for the identification of cancer therapeutic targets and compounds, there has been a growing interest in using 3D cancer spheroid models, which include tumorospheres (TSs), tissue-derived tumor spheres (TDTSs), organotypic multicellular tumor spheroids (OMSs), and multicellular tumor spheroids (MCTSs). The MCTSs, either Mono-MCTSs or Hetero-MCTSs, with or without scaffold, in particular, offer numerous advantages over other spheroid models, including easy cultivation, high reproducibility, accessibility, high throughput, controllable size, well-rounded shape, simplicity of genetic manipulation, economical and availability of various biological methods for their development. In this review, we have attempted to discuss the role of MCTSs concerning various aspects of translational cancer research, such as drug response and penetration, cell-cell interaction, and invasion and metastasis. However, the Mono-MCTSs, either scaffold-free or scaffold-based, may not adequately represent the cellular heterogeneity and complexity of clinical tumors, limiting their utility in translational cancer research. Conversely, Hetero-MCTS models, both scaffold-free and scaffold-based, show better suitability due to the presence of a similar in vivo type tumor microenvironment. Nonetheless, scaffold-based Hetero-MCTS models show batch variability and challenges in performing quantitative assays due to difficulties extracting spheroids and cells from scaffolds. Further, incorporating stromal cells with cancer cells in a more precise ratio to develop Hetero-MCTSs can enhance the model's relevance, yielding more clinically reliable outcomes for drug candidates and improving insights into tumor biology.

A Comprehensive Review of Protein Biomarkers for Invasive Lung Cancer

Invasion and metastasis are important hallmarks of lung cancer, and affect patients' survival. Early diagnostics of metastatic potential are important for treatment management. Recent findings suggest that the transition to an invasive phenotype causes changes in the expression of 700-800 genes. In this context, the biomarkers restricted to the specific type of cancer, like lung cancer, are often overlooked. Some well-known protein biomarkers correlate with the progression of the disease and the immunogenicity of the tumor. Most of these biomarkers are not exclusive to lung cancer because of their significant role in tumorigenesis. The dysregulation of others does not necessarily indicate cell invasiveness, as they play an active role in cell division. Clinical studies of lung cancer use protein biomarkers to assess the invasiveness of cancer cells for therapeutic purposes. However, there is still a need to discover new biomarkers for lung cancer. In the future, minimally invasive techniques, such as blood or saliva analyses, may be sufficient for this purpose. Many researchers suggest unconventional biomarkers, like circulating nucleic acids, exosomal proteins, and autoantibodies. This review paper aims to discuss the advantages and limitations of protein biomarkers of invasiveness in lung cancer, to assess their prognostic value, and propose novel biomarker candidates.

EMMPRIN promotes spheroid organization and metastatic formation: comparison between monolayers and spheroids of CT26 colon carcinoma cells

Background

In vitro studies often use two-dimensional (2D) monolayers, but 3D cell organization, such as in spheroids, better mimics the complexity of solid tumors. To metastasize, cancer cells undergo the process of epithelial-to-mesenchymal transition (EMT) to become more invasive and pro-angiogenic, with expression of both epithelial and mesenchymal markers.

Aims

We asked whether EMMPRIN/CD147 contributes to the formation of the 3D spheroid structure, and whether spheroids, which are often used to study proliferation and drug resistance, could better model the EMT process and the metastatic properties of cells, and improve our understanding of the role of EMMPRIN in them.

Methods

We used the parental mouse CT26 colon carcinoma (CT26-WT) cells, and infected them with a lentivirus vector to knock down EMMPRIN expression (CT26-KD cells), or with an empty lentivirus vector (CT26-NC) that served as a negative control. In some cases, we repeated the experiments with the 4T1 or LLC cell lines. We compared the magnitude of change between CT26-KD and CT26-WT/NC cells in different metastatic properties in cells seeded as monolayers or as spheroids formed by the scaffold-free liquid overlay method.

Results

We show that reduced EMMPRIN expression changed the morphology of cells and their spatial organization in both 2D and 3D models. The 3D models more clearly demonstrated how reduced EMMPRIN expression inhibited proliferation and the angiogenic potential, while it enhanced drug resistance, invasiveness, and EMT status, and moreover it enhanced cell dormancy and prevented CT26-KD cells from forming metastatic-like lesions when seeded on basement membrane extract (BME). Most interestingly, this approach enabled us to identify that EMMPRIN and miR-146a-5p form a negative feedback loop, thus identifying a key mechanism for EMMPRIN activities. These results underline EMMPRIN role as a gatekeeper that prevents dormancy, and suggest that EMMPRIN links EMT characteristics to the process of spheroid formation.

Conclusions

Thus, 3D models can help identify mechanisms by which EMMPRIN facilitates tumor and metastasis progression, which might render EMMPRIN as a promising target for anti-metastatic tumor therapy.

PPP2R1B abolishes colorectal cancer liver metastasis and sensitizes Oxaliplatin by inhibiting MAPK/ERK signaling pathway

BACKGROUND

Patients with colorectal cancer (CRC) with liver metastasis or drug resistance have a poor prognosis. Previous research has demonstrated that PPP2R1B inactivation results in the development of CRC. However, the role of PPP2R1B in CRC metastasis and drug resistance is unclear.

METHODS

Venny 2.1 was used to determine the intersection between survival-related differentially expressed genes (DEGs) and liver metastasis-related DEGs according to RNA-seq data from The Cancer Genome Atlas (TCGA) and the GEO database (GSE179979). LC‒MS/MS and coimmunoprecipitation were performed to predict and verify the substrate protein of PPP2R1B. Gene Set Variation Analysis (GSVA) was subsequently utilized to assess pathway enrichment levels. The predictive performance of PPP2R1B was assessed by regression analysis, Kaplan-Meier (KM) survival analysis and drug sensitivity analysis. Immunohistochemistry (IHC), qRT-PCR and western blotting were performed to measure the expression levels of related mRNAs or proteins. Biological features were evaluated by wound healing, cell migration and invasion assays and CCK-8 assays. A mouse spleen infection liver metastasis model was generated to confirm the role of PPP2R1B in the progression of liver metastasis in vivo.

RESULTS

According to bioinformatics analysis, PPP2R1B is significantly associated with liver metastasis and survival in CRC patients, and these findings were further verified via immunohistochemistry (IHC), qPCR and Western blotting. Pathway enrichment and LC‒MS/MS analysis revealed that PPP2R1B is negatively associated with the MAPK/ERK signalling pathway. Additionally, PD98059, a MAPK/ERK pathway inhibitor, inhibited EMT in vitro by reversing the changes in key proteins involved in EMT signalling (ZEB1, E-cadherin and Snail) and ERK/MAPK signalling (p-ERK) mediated by PPP2R1B. Oxaliplatin sensitivity prediction and validation revealed that PPP2R1B silencing decreased Oxaliplatin chemosensitivity, and these effects were reversed by PD98059 treatment. Moreover, PPP2R1B was coimmunoprecipitated with p-ERK in vitro. A negative correlation between PPP2R1B and p-ERK expression was also observed in clinical CRC samples, and the low PPP2R1B/high p-ERK coexpression pattern indicated a poor prognosis in CRC patients. In vivo, PPP2R1B silencing significantly promoted liver metastasis.

CONCLUSIONS

This study revealed that PPP2R1B induces dephosphorylation of the p-ERK protein, inhibits liver metastasis and increases Oxaliplatin sensitivity in CRC patients and could be a potential candidate for therapeutic application in CRC.

Interstitial flow potentiates TGF-β/Smad-signaling activity in lung cancer spheroids in a 3D-microfluidic chip

Within the tumor microenvironment (TME), cancer cells use mechanotransduction pathways to convert biophysical forces to biochemical signals. However, the underlying mechanisms and functional significance of these pathways remain largely unclear. The upregulation of mechanosensitive pathways from biophysical forces such as interstitial flow (IF), leads to the activation of various cytokines, including transforming growth factor-β (TGF-β). TGF-β promotes in part via a Smad-dependent signaling pathway the epithelial-mesenchymal transition (EMT) in cancer cells. The latter process is linked to increased cancer cell motility and invasion. Current research models have limited ability to investigate the combined effects of biophysical forces (such as IF) and cytokines (TGF-β) in a 3D microenvironment. We used a 3D-matrix based microfluidic platform to demonstrate the potentiating effect of IF on exogenous TGF-β induced upregulation of the Smad-signaling activity and the expression of mesenchymal marker vimentin in A549 lung cancer spheroids. To monitor this, we used stably integrated fluorescent based reporters into the A549 cancer cell genome. Our results demonstrate that IF enhances exogenous TGF-β induced Smad-signaling activity in lung cancer spheroids embedded in a matrix microenvironment. In addition, we observed an increased cell motility for A549 spheroids when exposed to IF and TGF-β. Our 3D-microfluidic model integrated with real-time imaging provides a powerful tool for investigating cancer cell signaling and motility associated with invasion characteristics in a physiologically relevant TME.

Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids

Mathematical models of non-small-cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with "omics" approaches will improve our understanding of the nature of non-small-cell lung cancer. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.