Cell Drug Response Research Articles

Unveiling the Role of SLC6A17 in Lung Adenocarcinoma: Prognosis, Pathways, and Therapeutic Implications.

The role of solute carrier family 6 member 17 (SLC6A17) in lung adenocarcinoma (LUAD) is unclear. To address this gap in knowledge, we employed bioinformatics analysis and experimental validation. This research aimed to scrutinize the expression patterns of the SLC6A17 gene across a spectrum of cancers and specifically within LUAD, utilizing data extracted from The Cancer Genome Atlas (TCGA). The correlation between SLC6A17 expression and LUAD prognosis was investigated to assess its diagnostic relevance. The study delved into the possible regulatory mechanisms of SLC6A17, focusing on its links to immune cell infiltration and drug response in LUAD. The examination of SLC6A17 expression was extended to single-cell sequencing data in LUAD, alongside an evaluation of the gene's genomic alterations and clinical implications within this disease context. Validation of SLC6A17 expression levels was conducted using datasets from GSE87340 and various cell lines, employing quantitative real-time polymerase chain reaction (qRTPCR) techniques. SLC6A17 exhibited aberrant expression in both pan-cancer and LUAD. Increased expression of SLC6A17 in LUAD patients was significantly associated with poorer overall survival (p = 0.008), progress-free survival (p = 0.019), and disease specific survival (p = 0.030). In LUAD patients, the levels of SLC6A17 expression were found to be a significant standalone indicator of prognosis, with a p-value of 0.031. SLC6A17 exhibited associations with various pathways, including focal adhesion, ECM receptor interaction, cell cycle, linoleic acid metabolism, pathways in cancer, and more. SLC6A17 expression demonstrated correlations with immune infiltration in LUAD. SLC6A17 expression revealed a notably inverse relationship with several substances, including AR-42, T0901317, tubastatin A, SB52334, and amuvatinib, within the context of LUAD. SLC6A17 was found to be significantly positively regulated in LUAD cell lines. These findings suggest that SLC6A17 indicates the potential of a potential prognostic biomarker and immunotherapeutic target for patients with LUAD.

BRAFV600E induces key features of LCH in iPSCs with cell type-specific phenotypes and drug responses.

Langerhans cell histiocytosis (LCH) is a clonal hematopoietic disorder defined by tumorous lesions containing CD1a+/CD207+ cells. Two severe complications of LCH are systemic hyperinflammation and progressive neurodegeneration. The scarcity of primary samples and lack of appropriate models limit our mechanistic understanding of LCH pathogenesis and affect patient care. We generated a human in vitro model for LCH using induced pluripotent stem cells (iPSCs) harboring the BRAFV600E mutation, the most common genetic driver of LCH. We show that BRAFV600E/WT iPSCs display myelo-monocytic skewing during hematopoiesis and spontaneously differentiate into CD1a+/CD207+ cells that are similar to lesional LCH cells and are derived from a CD14+ progenitor. We show that BRAFV600E modulates the expression of key transcription factors regulating monocytic differentiation and leads to an upregulation of pro-inflammatory molecules and LCH marker genes early during myeloid differentiation. In vitro drug testing revealed that BRAFV600E-induced transcriptomic changes are reverted upon treatment with MAPK-pathway inhibitors (MAPKi). Importantly, MAPKi do not affect myeloid progenitors but reduces only the mature CD14+ cell population. Furthermore, iPSC-derived neurons (iNeurons) co-cultured with BRAFV600E/WT iPSC-derived microglia-like cells (iMGL), differentiated from iPSC-derived CD34+ progenitors, exhibit signs of neurodegeneration with neuronal damage and release of neurofilament light chain. In summary, the iPSC-based model described here provides a platform to investigate the effects of BRAFV600E in different hematopoietic cell types and provides a tool to compare and identify novel approaches for the treatment of BRAFV600E-driven diseases.

DDDR-34. A SYSTEMS MEDICINE PLATFORM FOR INDIVIDUALIZED TREATMENT OF GLIOBLASTOMA

Abstract Glioblastoma is an aggressive brain cancer characterized by complex intertumoral heterogeneity. Yet, no methods have successfully stratified patients for effective individualized treatments. We aimed to use a systems medicine approach to individualize treatments for patients. From 27 patients undergoing surgery for either a newly diagnosed or a progressive glioblastoma, we collected biopsies and cultured the cells as spheroids. Drug sensitivity testing for >500 drugs from the FDA-approved oncology collection was performed. Drug efficacy was quantified using a modified area under the dose-response curve (Drug Sensitivity Score, DSS), and individualized by normalizing the DSS to the drug response in bone marrow cells from healthy donors (selective DSS, sDSS). All samples were assayed by WES, WGS, and RNA-sequencing. We found that glioblastomas can be stratified into three DSS groups. These were associated with expression of genes both known (e.g. ALDH1A1), and not known (e.g. VRK2) to be linked to drug resistance. By unsupervised clustering of sDSS, we found a robust patient stratification primarily driven by drug sensitivity to four groups of targets: i) EGFR, ii) FGFR/PDGFR/VEGFR, iii) MDM2 and iv) MEK/ERK. We found differentially expressed genes (N = 20-90 genes, varying by group) between the responders and non-responders in each group. We explored differential pathway activity between the responders and non-responders in each group to correlate drug responses to transcriptional networks to inform underlying disease mechanisms. The transcriptional networks further provide a molecular basis for drug predictions for additional treatment prioritizations. We conclude that our platform can stratify glioblastomas into subgroups, inform the underlying biology of drug sensitivity, and prioritize treatments for patients. Clinical utility is currently under evaluation in a formal trial.

Drug-Induced Differential Gene Expression Analysis on Nanoliter Droplet Microarrays: Enabling Tool for Functional Precision Oncology.

Drug-induced differential gene expression analysis (DGEA) is essential for uncovering the molecular basis of cell phenotypic changes and understanding individual tumor responses to anticancer drugs. Performing high throughput DGEA is challenging due to the high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. A novel, miniaturized, nanoliter-scale method for drug-induced DGEA is introduced, enabling high-throughput and parallel analysis of patient-derived cell drug responses, overcoming the limitations and laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA), a microscope glass slide with hydrophilic spots on a superhydrophobic background, facilitating droplet formation for cell testing. DMA allows microscopy-based phenotypic analysis, cDNA extraction, and DGEA. The procedure includes cell lysis for mRNA isolation and cDNA conversion followed by droplet pooling for qPCR analysis. In this study, the drug-induced DGEA protocol on the DMA platform is demonstrated using patient-derived chronic lymphocytic leukemia (CLL) cells. This methodology is critical for DGEA with limited cell numbers and promise for applications in functional precision oncology. This method enables molecular profiling of patient-derived samples after drug treatment, crucial for understanding individual tumor responses to anticancer drugs.

Paper Spray Ionization Mass Spectrometry Coupled with Paper-Based Three-Dimensional Tumor Model for Rapid Metabolic Gradient Profiling.

The tumor microenvironment (TME), especially with its complicated metabolic characteristics, will dynamically affect the proliferation, migration, and drug response of tumor cells. Rapid metabolic analysis brings out a deeper understanding of the TME, while the susceptibility and environmental dependence of metabolites extremely hinder real-time metabolic profiling since the TME is easily disrupted. Here, we directly integrated paper spray ionization mass spectrometry with a paper-based three-dimensional (3D) tumor model, realizing the rapid capture of metabolic gradients. The entire procedure, from sample preparation to mass spectrometry detection, took less than 4 min, which was able to provide metabolic results close to real time and contributed to understanding the real metabolic processes. At present, our method successfully detected 160 metabolites; notably, over 40 significantly gradient metabolites were revealed across the six layers of the paper-based 3D tumor model. At least 22 gradient metabolites were reported to be associated with cell viability. This strategy was powerful enough to rapidly profile metabolic gradients of a paper-based 3D tumor model for revealing cell viability changes from a metabolomics perspective.

Assessing the metastatic potential of circulating tumor cells using an organ-on-chip model.

Metastatic lung cancer remains a leading cause of death worldwide, with its intricate metastatic cascade posing significant challenges to researchers and clinicians. Despite substantial progress in understanding this cascade, many aspects remain elusive. Microfluidic-based vasculature-on-chip models have emerged as powerful tools in cancer research, enabling the simulation of specific stages of tumor progression. In this study, we investigate the extravasation behaviors of A549 lung cancer cell subpopulations, revealing distinct differences based on their phenotypes. Our results show that holoclones, which exhibit an epithelial phenotype, do not undergo extravasation. In contrast, paraclones, characterized by a mesenchymal phenotype, demonstrate a notable capacity for extravasation. Furthermore, we observed that paraclones migrate significantly faster than holoclones within the microfluidic model. Importantly, we found that the depletion of vascular endothelial growth factor (VEGF) effectively inhibits the extravasation of paraclones. These findings highlight the utility of microfluidic-based models in replicating key aspects of the metastatic cascade. The insights gained from this study underscore the potential of these models to advance precision medicine by facilitating the assessment of patient-specific cancer cell dynamics and drug responses. This approach could lead to improved strategies for predicting metastatic risk and tailoring personalized cancer therapies, potentially involving the sampling of cancer cells from patients during tumor resection or biopsies.

Graph-Based Spatial Proximity of Super-Resolved Protein-Protein Interactions Predicts Cancer Drug Responses in Single Cells.

Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models. Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells. GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54. The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance. The online version contains supplementary material available at 10.1007/s12195-024-00822-1.

In vitro testing of drug response in primary multiple myeloma cells using a microwell-based technology

Multiple myeloma is an aggressive neoplasm of plasma cells. While numerous drugs have gained approval, the absence of established predictive markers for individual drug responses poses a challenge. In this study, we explored the microwell- and fluorescence-based Cellply CC-Array® technology for high-throughput analysis of in vitro drug responses as a potential predictive marker for patient treatment outcomes. Furthermore, we investigated its application for evaluating effector cell effectiveness. Mononuclear cells were isolated from the bone marrow of 22 patients, and in vitro drug response of primary myeloma cells was analyzed. In vitro responses towards melphalan, bortezomib, and dexamethasone in primary patient samples correlated with clinical response of the patients. The approach exhibited limitations in identifying sensitivity towards lenalidomide, daratumumab, and elotuzumab due to limited culturing time caused by poor myeloma viability in vitro. Through the analysis of cell proximity, the platform enabled the assessment of individual anti-tumor activity from NK and T cells. In summary, the CC-Array microwell technology allowed assessment of myeloma cell responses to selected drugs used in multiple myeloma therapy in vitro. To further validate these in vitro results against in vivo outcomes, screening a larger cohort is necessary.

Hyaluronan-Based Hydrogels for 3D Modeling of Tumor Tissues.

Although routine two-dimensional (2D) cell culture techniques have advanced basic cancer research owing to their simplicity, cost-effectiveness, and reproducibility, they have limitations that necessitate the development of advanced three-dimensional (3D) tumor models that better recapitulate the tumor microenvironment. Various biomaterials have been used to establish these 3D models, enabling the study of cancer cell behavior within different matrices. Hyaluronic acid (HA), a key component of the extracellular matrix (ECM) in tumor tissues, has been widely studied and employed in the development of multiple cancer models. This review first examines the role of HA in tumors, including its function as an ECM component and regulator of signaling pathways that affect tumor progression. It then explores HA-based models for various cancers, focusing on HA as a central component of the 3D matrix and its mobilization within the matrix for targeted studies of cell behavior and drug testing. The tumor models discussed included those for breast cancer, glioblastoma, fibrosarcoma, gastric cancer, hepatocellular carcinoma, and melanoma. The review concludes with a discussion of future prospects for developing more robust and high-throughput HA-based models to more accurately mimic the tumor microenvironment and improve drug testing. Impact Statement This review underscores the transformative potential of hyaluronic acid (HA)-based hydrogels in developing advanced tumor models. By exploring HA's dual role as a critical extracellular matrix component and a regulator of cancer cell dynamics, we highlight its unique contributions to replicating the tumor microenvironment. The recent advancements in HA-based models provide new opportunities for more accurate studies of cancer cell behavior and drug responses. Looking ahead, these innovations pave the way for high-throughput, biomimetic platforms that could revolutionize drug testing and accelerate the discovery of effective cancer therapies.

MicroRNA-506 as a tumor suppressor in prostate cancer by regulation of Hippo signaling pathway

IntroductionProstate cancer (PCa) is considered as one of the leading causes of cancer related deaths among males globally. There is a high rate of tumor relapse and metastasis in PCa patients that is associated with chemo-resistance. Beside the elimination of tumor cells, chemotherapy has an adverse effect in normal cells. Therefore, it is required to clarify the molecular tumor biology to introduce novel markers to predict the drug response in PCa patients. Hippo signaling pathway is an important regulator of cell proliferation, migration, and drug response that can be modulated by microRNAs (miRNAs). Since, miR-506 deregulation has been reported in PCs, in the present study we assessed a probable role of miR-506 in regulation of Hippo signaling pathway during prostate tumor cell migration and drug resistance. Materials and methodsThe mRNA expression levels of Hippo signaling components were assessed in miR-506 ectopic expressed in comparison with control PC3 cells. Apoptosis assay, drug response, and cell migration were also assessed to find the role of miR-506 in prostate tumor cell aggressiveness. ResultsMiR-506 promoted the Hippo signaling pathway via TAZ and CTGF up regulations, while BIRC5, FAT, and C-MYC down regulations in PC3 cells. MiR-506 significantly reduced prostate tumor cell migration (p = 0.019) and Paclitaxel (TXL) resistance (p = 0.0006), while promoted apoptosis (p < 0.0001). ConclusionmiR-506 exerted a tumor suppressor role during PCa progression by activation of Hippo pathway. MiR-506 targeted the FAT to activate the YAP/TAZ and nuclear translocation where it can bind with TEAD to up regulate the CTGF that resulted in reduced PCa cell migration. The WNT inhibition and C-MYC down regulation by the CTGF resulted in BIRC5 down regulation that induced apoptosis in PCa cells. Therefore, miR-506 can be introduced as a therapeutic marker in PCa following the confirmation by the animal studies and further clinical trials.

The m5C/m6A/m7G-related non-apoptotic regulatory cell death genes for the prediction of the prognosis and immune infiltration status in hepatocellular carcinoma.

5-methylcytosine/N6-methyladenosine/N7-methylguanosine (m5C/m6A/m7G)-related genes play a critical role in tumor occurrence and progression, and non-apoptotic regulatory cell death (NARCD) is closely linked to tumor development and immunity. However, the role of m5C/m6A/m7G-related NARCD genes in hepatocellular carcinoma (HCC) remains unclear. We used m5C/m6A/m7G-related NARCD genes to construct a prognostic model of HCC for prognostic prediction and clinical treatment of patients. We obtained transcriptome data for HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Using the least absolute shrinkage and selection operator (LASSO) regression, we identified m5C/m6A/m7G-related NARCD genes and constructed a prognostic model through multivariate Cox regression. Model performance was assessed using Kaplan-Meier and receiver operating characteristic (ROC) curves, with external validation using the ICGC. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to study differentially expressed genes between high- and low-risk groups. We also examined immune cell infiltration, drug response, and cell communication between tumor cells and immune cells in high-risk groups. We identified 140 m5C/m6A/m7G-related NARCD genes, using five of them to build the prognostic model. Functional enrichment analysis revealed enrichment in tumor and immune-related pathways for risk genes. The high-risk group displayed increased immune cell infiltration and better responses to immune checkpoint inhibitors (ICIs). High-risk patients were more responsive to cisplatin, doxorubicin, and mitomycin C, while low-risk patients were more sensitive to erlotinib. Cell communication analysis indicated that high-risk tumor cells used insulin-like growth factor (IGF) and macrophage migration inhibitory factor (MIF) signaling pathways to send signals to immune cells and received signals through the bone morphogenetic protein (BMP) and lymphotoxin-related inducible ligand (LIGHT) pathways. We have developed a prognostic model with m5C/m6A/m7G-related NARCD genes to predict the prognosis of HCC patients. This model can offer insights into the effectiveness of immunotherapy and chemotherapy for HCC patients.

A Database Tool Integrating Genomic and Pharmacologic Data from Adrenocortical Carcinoma Cell Lines, PDX, and Patient Samples.

ACC_CellMinerCDB, a comprehensive database of cell lines, patient-derived xenografts, surgical samples, and drug responses, reveals shared genomic pathways and treatment-relevant markers in ACC. This resource offers insights into potential therapeutic targets and the opportunity to repurpose existing drugs for ACC therapy.

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids.

Multicellular tumor spheroids are a popular 3D tissue microaggregatemodel for reproducing tumor microenvironment, testing and optimizing drug therapiesand using bio- and nanosensors in a 3D context. Their ease of production, predictable size, growth, and observed nutrient and metabolite gradients are important to recapitulate the 3D niche-likecell microenvironment. However, spheroid heterogeneity and variability of their production methods can influence overall cell metabolism, viability, and drug response. This makes it difficult to choose the most appropriatemethodology, considering the requirements in size, variability, needs of biofabrication, and use as in vitro 3D tissue models in stem and cancer cell biology. In particular, spheroid production can influence their compatibility with quantitative live microscopies, such as optical metabolic imaging, fluorescence lifetime imaging microscopy (FLIM), monitoring of spheroidhypoxia with nanosensors, or viability. Here, a number of conventional spheroid formation protocols are presented, highlighting their compatibility with the live widefield, confocal, and two-photon microscopies. The follow-up imaging to analysispipeline with multiplexed autofluorescence FLIM and, using various types of cancer and stem cell spheroids, is also presented.

Stress pathway outputs are encoded by pH-dependent clustering of kinase components

Signal processing by intracellular kinases controls near all biological processes but how signal pathway functions evolve with changed cellular context is poorly understood. Functional specificity of c-Jun N-terminal Kinases (JNK) are partly encoded by signal strength. Here we reveal that intracellular pH (pHi) is a significant component of the JNK network and defines signal response to specific stimuli. We show pHi regulates JNK activity in response to cell stress, with the relationship between pHi and JNK activity dependent on specific stimuli and upstream kinases activated. Using the optogenetic clustering tag CRY2, we show that an increase in pHi promotes the light-induced phase transition of ASK1 to augment JNK activation. While increased pHi similarly promoted CRY2-tagged JNK2 to form light-induced condensates, this attenuated JNK activity. Mathematical modelling of feedback signalling incorporating pHi and differential contributions by ASK1 and JNK2 condensates was sufficient to delineate signal responses to specific stimuli. Taking pHi and ASK1/JNK2 signal contributions into consideration may delineate oncogenic versus tumour suppressive JNK functions and cancer cell drug responses.

Mechanisms of RNA alternative splicing dysregulation in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype with poor prognosis. RNA alternative splicing dysregulation plays a critical role in the initiation and progression of TNBC. This article systematically introduces the basic process of RNA splicing and then focuses on reviewing the aberrant alternative splicing events and their biological effects in TNBC: 1) Multiple splicing-related factors promote tumor cell proliferation and mediate chemotherapy resistance by regulating the alternative splicing of genes involved in cell survival and drug response; 2) dysregulation of splicing regulatory networks leads to altered splicing of multiple metastasis-related genes, promoting tumor invasion and metastasis; 3) aberrant alternative splicing events participate in tumor progression by affecting the expression of DNA damage repair genes; 4) dysregulation of alternative splicing is also involved in the regulation of tumor immune evasion and stem cell properties. A deeper understanding of the mechanisms underlying RNA alternative splicing dysregulation in TNBC is essential for elucidating its molecular pathology, identifying novel prognostic markers, and developing therapeutic strategies.

Integrating network pharmacology and computational biology to propose Yiqi Sanjie formula's mechanisms in treating NSCLC: molecular docking, ADMET, and molecular dynamics simulation.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally. Current treatments often do not fully meet efficacy and quality of life expectations. Traditional Chinese medicine (TCM), particularly the Yiqi Sanjie formula, shows promise but lacks clear mechanistic understanding. This study addresses this gap by investigating the therapeutic effects and underlying mechanisms of Yiqi Sanjie formula in NSCLC. We utilized network pharmacology to identify potential NSCLC drug targets of the Yiqi Sanjie formula via the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Compounds with favorable oral bioavailability and drug-likeness scores were selected. Molecular docking was conducted using AutoDock Vina with structural data from the Protein Data Bank and PubChem. Molecular dynamics (MD) simulations were performed with Desmond Molecular Dynamics System, analyzing interactions up to 500 nanoseconds using the OPLS4 force field. ADMET predictions were executed using SwissADME and ADMETlab 2.0, assessing pharmacokinetic properties. Using network pharmacology tools, we performed Search Tool for the Retrieval of Interaction Genes/Proteins (STRING) analysis for protein-protein interaction, Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway enrichment, and gene ontology (GO) for functional enrichment, identifying crucial signaling pathways and biological processes influenced by the hit compounds bifendate, xambioona, and hederagenin. STRING analysis indicated substantial connectivity among the targets, suggesting significant interactions within the cell cycle regulation and growth factor signaling pathways as outlined in our KEGG results. The GO analysis highlighted their involvement in critical biological processes such as cell cycle control, apoptosis, and drug response. Molecular docking simulations quantified the binding efficiencies of the identified compounds with their targets-CCND1, CDK4, and EGFR-selected based on high docking scores that suggest strong potential interactions crucial for NSCLC inhibition. Subsequent MD simulations validated the stability of these complexes, supporting their potential as therapeutic interventions. Additionally, the novel identification of ADH1B as a target underscores its prospective significance in NSCLC therapy, further expanded by our comprehensive bioinformatics approach. Our research demonstrates the potential of integrating network pharmacology and computational biology to elucidate the mechanisms of the Yiqi Sanjie formula in NSCLC treatment. The identified compounds could lead to novel targeted therapies, especially for patients with overexpressed targets. The discovery of ADH1B as a therapeutic target adds a new dimension to NSCLC treatment strategies. Further studies, both in vitro and in vivo, are needed to confirm these computational findings and advance these compounds towards clinical trials.

Construction of a three-dimensional culture system based on Gelatin methacryloyl hydrogel for lung cancer cells

Abstract Hypoxia and acidity are key characteristics of the tumor microenvironment (TME). Gelatin methacryloyl (GelMA) is a versatile biomaterial extensively utilized in various biomedical fields. Studies have shown that during the photocrosslinking process of GelMA hydrogel, the commonly used photoinitiator Irgacure 2959 consumes oxygen and induces an acidic and hypoxia environment. However, there is currently limited research on its involvement in the three-dimensional (3D) culture of tumor cells. Therefore, we constructed a 3D culture system utilizing GelMA hydrogel and investigated its influence on the growth and drug response of lung cancer cells (A549). The results demonstrated that the GelMA hydrogel-based 3D culture system exhibited excellent biocompatibility and robust mechanical properties and provided an acidic microenvironment conducive to tumor cell proliferation. Furthermore, the cancer cells exhibited heightened sensitivity to chemotherapeutics in this 3D culture model. These results suggest that the GelMA hydrogel-based 3D culture system can serve as a perfect model for in vitro study of tumor cells.

Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessitates non-toxic staining methods, as specific fluorescent tags may not be suitable, and immunofluorescence staining can be cytotoxic for prolonged live cell cultures. We aim to perform machine learning-based cell classification within a live heterogeneous cell culture population grown in a 3D tissue culture relying only on reflectance, transmittance, and nuclei counterstained images obtained by confocal microscopy. In this study, we employed a supervised convolutional neural network (CNN) to classify tumor cells and fibroblasts within 3D-grown spheroids. These cells are first segmented using the marker-controlled watershed image processing method. Training data included nuclei counterstaining, reflectance, and transmitted light images, with stained fibroblast and tumor cells as ground-truth labels. Our results demonstrate the successful marker-controlled watershed segmentation of 84% of spheroid cells into single cells. We achieved a median accuracy of 67% (95% confidence interval of the median is 65-71%) in identifying cell types. We also recapitulate the original 3D images using the CNN-classified cells to visualize the original 3D-stained image's cell distribution. This study introduces a non-invasive toxicity-free approach to 3D cell culture evaluation, combining machine learning with confocal microscopy, opening avenues for advanced cell studies.

Identification of a Macrophage marker gene signature to evaluate immune infiltration and therapeutic response in hepatocellular carcinoma

BackgroundOnly a minority of hepatocellular carcinoma (HCC) patients can benefit from systemic regimens. Macrophages, which abundantly infiltrate in HCC, could mediate tumour microenvironment remodelling and immune escape, proving to be powerful weapons in combating HCC. Thus, a deeper understanding of macrophages is necessary for improving existing antitumour treatments. MethodsWith a series of bioinformatic approaches, we comprehensively explored the role of macrophage-related genes in human HCCs from multiple single-cell and bulk RNA sequencing datasets. Unsupervised clustering was performed to cluster the macrophage marker genes (MMGs). GSVA and functional enrichment analysis were used to elucidate the functional differences among the MMG-associated clusters. Subsequently, a component analysis algorithm was used to construct a Macrosig score, and the prognosis, biological characteristics, mutation profile, TME cell infiltration status and drug response of patients with different Macrosig scores were further analysed. ResultsWe identified 13 MMGs in 574 HCC samples, based on which three MMG-associated clusters were defined. Overall survival time, clinicopathological features and immune infiltration scores differed among the different clusters. On this basis, 12 hub genes were identified among these clusters; subsequently, a scoring system was constructed to determine the Macrosig score. Importantly, patients with low-Macrosig scores, characterized by increased immune infiltration, increased mutation frequency and increased immune checkpoint expression, including CTLA-4, LAG3, PDCD1 and TIGIT, exhibited enhanced efficacy of immunotherapy when validated in an external database. Moreover, a low-Macrosig score indicates increased sensitivity to AZD.2281, A.443654, ABT.263, ABT.888, AG.014699 and ATRA, while a high Macrosig score indicates increased sensitivity to AZD6482, AKT inhibitor VIII, AS601245, AZ628, AZD.0530 and AZD6244. ConclusionsA novel scoring system was constructed to guide more effective prognostic evaluation and tailoring therapeutic regimens for HCC patients.

YY2/BUB3 Axis promotes SAC Hyperactivation and Inhibits Colorectal Cancer Progression via Regulating Chromosomal Instability.

Spindle assembly checkpoint (SAC) is a crucial safeguard mechanism of mitosis fidelity that ensures equal division of duplicated chromosomes to the two progeny cells. Impaired SAC can lead to chromosomal instability (CIN), a well-recognized hallmark of cancer that facilitates tumor progression; paradoxically, high CIN levels are associated with better therapeutic response and prognosis. However, the mechanism by which CIN determines tumor cell survival and therapeutic response remains poorly understood. Here, using a cross-omics approach, YY2 is identified as a mitotic regulator that promotes SAC activity by activating the transcription of budding uninhibited by benzimidazole 3 (BUB3), a component of SAC. While both conditions induce CIN, a defect in YY2/SAC activity enhances mitosis and tumor growth. Meanwhile, hyperactivation of SAC mediated by YY2/BUB3 triggers a delay in mitosis and suppresses growth. Furthermore, it is revealed that YY2/BUB3-mediated excessive CIN causes higher cell death rates and drug sensitivity, whereas residual tumor cells that survived DNA damage-based therapy have moderate CIN and increased drug resistance. These results provide insights into the role of SAC activity and CIN levels in influencing tumor cell survival and drug response, as well as suggest a novel anti-tumor therapeutic strategy that combines SAC activity modulators and DNA-damage agents.