Accumulating evidence suggests that glioma stem cells (GSCs) are important therapeutic targets in glioblastoma (GBM). In this study, we identified NIMA-related kinase 2 (NEK2) as a functional binding protein of enhancer of zeste homolog 2 (EZH2) that plays a critical role in the posttranslational regulation of EZH2 protein in GSCs. NEK2 was among the most differentially expressed kinase-encoding genes in GSC-containing cultures (glioma spheres), and it was required for in vitro clonogenicity, in vivo tumor propagation, and radioresistance. Mechanistically, the formation of a protein complex comprising NEK2 and EZH2 in glioma spheres phosphorylated and then protected EZH2 from ubiquitination-dependent protein degradation in a NEK2 kinase activity–dependent manner. Clinically, NEK2 expression in patients with glioma was closely associated with EZH2 expression and correlated with a poor prognosis. NEK2 expression was also substantially elevated in recurrent tumors after therapeutic failure compared with primary untreated tumors in matched GBM patients. We designed a NEK2 kinase inhibitor, compound 3a (CMP3a), which efficiently attenuated GBM growth in a mouse model and exhibited a synergistic effect with radiotherapy. These data demonstrate a key role for NEK2 in maintaining GSCs in GBM by stabilizing the EZH2 protein and introduce the small-molecule inhibitor CMP3a as a potential therapeutic agent for GBM.

Patients with IDH-wild-type glioblastomas have a poor five-year survival rate along with limited treatment efficacy due to immune cell (glioma-associated microglia and macrophages) infiltration promoting tumour growth and resistance. To enhance therapeutic options, our study investigated the unique RNA–RNA-binding protein complex LOC–DHX15. This complex plays a crucial role in driving immune cell infiltration and tumour growth by establishing a feedback loop between cancer and immune cells, intensifying cancer aggressiveness. Targeting this complex with blood–brain barrier-permeable small molecules improved treatment efficacy, disrupting cell communication and impeding cancer cell survival and stem-like properties. Focusing on RNA–RNA-binding protein interactions emerges as a promising approach not only for glioblastomas without the IDH mutation but also for potential applications beyond cancer, offering new avenues for developing therapies that address intricate cellular relationships in the body.

Colorectal cancer is the third most common cancer in the world, with an annual incidence of 2 million cases. The success of first-line chemotherapy plays a crucial role in determining the disease outcome. Therefore, there is an increasing demand for precision medicine to predict drug responses and optimize chemotherapy in order to increase patient survival and reduce the related side effects. Patient-derived organoids have become a popular in vitro screening model for drug-response prediction for precision medicine. However, there is no established correlation between oxaliplatin and drug-response prediction. Here, we suggest that organoid culture conditions can increase resistance to oxaliplatin during drug screening, and we developed a modified medium condition to address this issue. Notably, while previous studies have shown that survivin is a mechanism for drug resistance, our study observed consistent survivin expression irrespective of the culture conditions and oxaliplatin treatment. However, clusterin induced apoptosis inhibition and cell survival, demonstrating a significant correlation with drug resistance. This study’s findings are expected to contribute to increasing the accuracy of drug-response prediction in patient-derived APC mutant colorectal cancer organoids, thereby providing reliable precision medicine and improving patient survival rates.

Background/Objectives: Lung cancer is a highly diverse disease, and reliable preclinical models that accurately reflect tumor characteristics are essential for studying lung cancer biology and testing new therapies. This study aimed to establish patient-derived tumor organoids (PDTOs) using small biopsy samples and surgical specimens to create a model system that preserves the genetic and histological features of the original tumors. Methods: PDTOs were generated from 163 lung cancer specimens, including 109 samples obtained using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) or bronchoscopy, 52 surgical specimens, and 2 pleural fluid samples. The organoid establishment rate beyond passage three was assessed, and histological subtypes and genetic profiles were analyzed using immunohistochemical staining and targeted exome sequencing. Results: The overall PDTO establishment rate was 34.4% (56/163), and 44.6% (25/56) of these organoids retained the histological and genetic features of the parental tumors. Genetic analysis identified key mutations, including KRAS G12C, EGFR L858R, MET exon 14 skipping mutation, and ROS1 fusion. PDTOs successfully formed tumors in mice while maintaining the genetic characteristics of the original tumors. Co-culture of PDTOs with cancer-associated fibroblasts (CAFs) resulted in increased resistance to paclitaxel. In the co-culture model of PDTOs with immune cells, dose-dependent growth inhibition of PDTOs was observed in response to immune checkpoint inhibitors. Conclusions: PDTOs established from small biopsy and surgical specimens serve as a valuable model for studying lung cancer biology, tumor microenvironment interactions, and drug response. This model has the potential to improve personalized treatment strategies.

Diffusely infiltrating gliomas (DIGs) are difficult to completely resect and are associated with a high rate of tumor relapse and progression from low- to high-grade glioma. In particular, optimized short-term culture-enriching patient-derived glioma stem cells (GSCs) are essential for customizing the therapeutic strategy based on clinically feasible in vitro drug screening for a wide range of DIGs, owing to the high inter-tumoral heterogeneity. Herein, we constructed a novel high-throughput culture condition screening platform called ‘GFSCAN’, which evaluated the cellular growth rates of GSCs for each DIG sample in 132 serum-free combinations, using 13 previously reported growth factors closely associated with glioma aggressiveness. In total, 72 patient-derived GSCs with available genomic profiles were tested in GFSCAN to explore the association between cellular growth rates in specific growth factor combinations and genomic/molecular backgrounds, including isocitrate dehydrogenase 1 (IDH1) mutation, chromosome arm 1p and 19q co-deletion, ATRX chromatin remodeler alteration, and transcriptional subtype. GSCs were clustered according to the dependency on epidermal growth factor and basic fibroblast growth factor (E&F), and isocitrate dehydrogenase 1 (IDH1) wild-type GSCs showed higher E&F dependencies than IDH1 mutant GSCs. More importantly, we elucidated optimal combinations for IDH1 mutant glioblastoma and lower grade glioma GSCs with low dependencies on E&F, which could be an aid in clinical decision-making for these DIGs. Thus, we demonstrated the utility of GFSCAN in personalizing in vitro cultivation to nominate personalized therapeutic options, in a clinically relevant time frame, for individual DIG patients, where standard clinical options have been exhausted.

Individual cells sense and transition between functional states, and the distribution of these states over time determines drug response, disease progression, and cell manufacturing outcomes. However, repeated measurement is difficult with label‐based acquisition, as photobleaching, phototoxicity, and probe‐induced perturbation accumulate. Label‐free monitoring that leverages intrinsic physical signals circumvents these constraints, shifting the analytical burden from label chemistry to instrumental drift. In this review, we organize this field into three measurement modalities, including imaging‐based, vibrational spectroscopy‐based, and electrical sensing‐based, each linked to distinct intrinsic state variables. Anchoring each modality to biological state variables such as structural organization, molecular composition, or dielectric architecture enables a physics‐grounded framework that clarifies how intrinsic signals map onto functional cellular phenotypes in longitudinal monitoring. We describe the measurement principle, drift sources, and feature space for each modality, and evaluate representative platforms against a frame spanning design, feature definition, quantitative performance, and validation practice. Dominant analytical constraints differ systematically across modalities, motivating integrative architectures in which complementary modalities resolve ambiguities that no single modality can disentangle. We further discuss shared requirements for calibration, standardized reporting, and multimodal integration, and outline requirements in hardware miniaturization, edge inference, and artificial intelligence‐guided molecular attribution to support scalable quantitative single‐cell phenotyping.