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At least 19 recordsLinked to original sources

PubMed2026

Temporal mismatch in allogeneic iPSC therapies: biological risks and implications for clinical translation.

INTRODUCTION: The clinical translation of pluripotent stem cell-derived therapies has entered a new phase following conditional approval of first-in-class allogeneic induced pluripotent stem cell (iPSC)-derived products in Japan. These approvals highlight both the therapeutic promise of iPSC technologies and regulatory challenges associated with evaluating complex cell-based interventions. AREAS COVERED: This report examines the evidentiary basis supporting recent approvals and reviews key biological characteristics of allogeneic iPSC-derived therapies, including pluripotency-associated instability, immunological constraints, and manufacturing-related genomic variability. Drawing on recent clinical studies and relevant experimental literature, we analyze how these multilayered risks evolve over extended time horizons and assess their implications for the interpretation of early-phase clinical data and current regulatory frameworks. EXPERT OPINION: We argue that the central challenge extends beyond limited clinical evidence to a fundamental mismatch between the temporal dynamics of biological risk and the duration of conventional clinical evaluation. As a result, early clinical observations may systematically underestimate long-term risks. Conditional approval pathways should therefore incorporate safeguards aligned with this temporal uncertainty, including long-term follow-up, rigorous post-approval evaluation, and enhanced transparency in biological and manufacturing data. Aligning regulatory design with intrinsic properties of pluripotent stem cell-derived therapies will be essential for ensuring safe and responsible clinical translation.

Humans↗

PubMed2026

Engineering extracellular vesicles for targeted siRNA delivery: Advances, therapeutic applications, and clinical translation.

Small interfering RNA (siRNA) therapeutics have emerged as a transformative approach for sequence-specific gene silencing, offering the potential to treat a broad spectrum of diseases by selectively suppressing disease-associated genes. However, the clinical translation of siRNA remains limited by rapid enzymatic degradation, poor cellular uptake, inadequate endosomal escape, and off-target effects, necessitating the development of efficient delivery systems. Extracellular vesicles (EVs) have gained considerable attention as natural nanocarriers owing to their excellent biocompatibility, low immunogenicity, intrinsic targeting capability, and ability to protect therapeutic cargo while traversing complex biological barriers. This review comprehensively discusses the biological characteristics of EVs, the molecular basis of RNA interference, and the major challenges associated with siRNA delivery [Fig. 1]. Recent advances in EV engineering, including cargo-loading strategies such as electroporation, sonication, extrusion, parent-cell engineering, and microfluidic approaches, together with surface functionalization using peptides, antibodies, aptamers, and hybrid nanoplatforms, are critically evaluated for improving targeting specificity and intracellular delivery. Furthermore, the therapeutic applications of engineered EV-mediated siRNA delivery in cancer, neurological disorders, liver diseases, cardiovascular diseases, inflammatory disorders, and infectious diseases are systematically summarized, highlighting their potential to enhance gene silencing while minimizing systemic toxicity. Current challenges related to large-scale manufacturing, cargo-loading efficiency, standardization, quality control, regulatory approval, and clinical translation are also discussed, together with emerging technologies involving synthetic biology, genome engineering, artificial intelligence, and multifunctional hybrid vesicles. Overall, engineered extracellular vesicles represent a highly versatile and biologically inspired platform for targeted siRNA delivery, providing a promising foundation for the development of next-generation precision RNA therapeutics and accelerating the clinical translation of gene-silencing strategies.

Extracellular vesicle engineering↗

PubMed2026

Gene therapy for genodermatoses at the crossroads of innovation and clinical translation.

Inherited genodermatoses are a heterogeneous group of rare monogenic disorders. Among these, epidermolysis bullosa (EB) and ichthyoses represent paradigmatic disorders characterized by severe skin fragility and hyperkeratosis, respectively, and impaired barrier function, often with profound effects on quality of life and systemic health. Current management remains largely palliative, underscoring the urgent need for disease-modifying therapies. Over the past 2 decades, advances in epithelial stem cell biology, vector engineering and genome editing technologies have transformed the therapeutic landscape for genodermatoses. Ex vivo gene therapy has provided the first proof that genetically corrected epidermal stem cells can achieve long-term tissue regeneration in EB skin patients, establishing a new paradigm for regenerative medicine. In parallel, the emergence of programmable genome engineering platforms, including CRISPR/Cas nucleases, base editors and prime editors, have enabled increasingly precise strategies for mutation-specific correction in both recessive and dominant disorders. Furthermore, the development of in vivo topical approaches is expanding the possibility of directly targeting the skin. Despite these advances, substantial translational barriers continue to limit broad clinical implementation. Efficient and durable targeting of epidermal stem cells within a highly regenerative tissue, together with safe delivery across the skin barrier, stringent control of off-target activity, scalable manufacturing and demonstration of long-term safety, remain major challenges for the clinical translation of these approaches. In this Review, we discuss the current state of gene therapy for genodermatoses, highlighting key clinical milestones, emerging genome editing technologies and next-generation delivery systems. We further examine the biological and regulatory challenges that need to be overcome to bridge the gap between experimental innovation and clinically accessible therapies for patients with inherited skin diseases.

epidermolysis bullosa (EB)↗

PubMed2025

Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.

Diabetes mellitus encompasses several disorders, each with differing clinical presentation, prognoses and pathophysiology. Distinct polygenic architectures underlie type 1 diabetes mellitus and type 2 diabetes mellitus, and govern numerous pathophysiological pathways that converge on dysglycaemia. Over the previous decade, polygenic risk scores (PRS) derived from large genome-wide association studies have become broadly recognized for their potential in precision medicine. PRS, and now partitioned polygenic scores generated by clustering of risk variants, can quantify individual genetic predisposition to diabetes mellitus and reveal molecular heterogeneity responsible for variation in clinical presentation and prognoses. In this Review, we examine and contrast progress in the development of type 1 diabetes mellitus PRS and type 2 diabetes mellitus PRS, and discuss paths to further methodological advances. We examine how studies in the past 10 years have harnessed PRS and novel partitioned polygenic scores to reveal insights into diabetes mellitus aetiology and characterize changes in cellular and tissue-specific disease-modifying molecular pathways. Additionally, we discuss advances and opportunities in areas of clinical translation, including improved classification of diabetes mellitus type, screening of those at risk and personalized interventions informed by PRS. Finally, we emphasize the urgent need to overcome ancestry-related challenges and highlight current progress and gaps in ensuring the equitable translation of PRS for diabetes mellitus precision medicine.

Humans↗

PubMed2026

Induced pluripotent stem cell reprogramming: methodological evolution and challenges in clinical translation.

Cell reprogramming can transform somatic cells into induced pluripotent stem cells providing a platform for patient-specific disease modeling, drug screening and regenerative medicine research. Since the advent of OKSM-mediated reprogramming, the system of technical approaches has evolved continuously - from integrated viral vectors to non-integrated episomal systems and, more recently, chemical reprogramming and CRISPR approaches. The simultaneous advances in single-cell multi-omics, biomaterials engineering, and artificial intelligence have further refined the controllability and precision of the reprogramming process. Despite these innovations, problems persist that hinder clinical translation: incomplete epigenetic resetting, ongoing clonal heterogeneity, genomic instability in long-term culture, and the lack of standardized Good Manufacturing Practice protocols for large-scale manufacturing. This review summarizes the trajectory of iPSC reprogramming technologies, with special emphasis on the translational applicability of each modality. We evaluated viral and nonviral delivery systems, chemical reprogramming, strategies that aid gene editing, and emerging engineering platforms, including microfluidics, smart biomaterials, and artificial-intelligence-driven process optimization. We further identify the core "translational triltrilas", namely, the inherent tradeoffs between security, homogeneity, and scalability, and propose a comprehensive strategy to overcome these bottlenecks. By linking basic mechanistic understandings with industrial and regulatory considerations, this review aims to provide a route for transitioning iPSC technology from a laboratory tool to a clinically viable manufacturing platform.

clinical translation↗

PubMed2026

An international framework for clinical translation of molecular classifiers in osteosarcoma.

Despite well-recognized biological heterogeneity, osteosarcoma has been treated as a single disease for over four decades with minimal improvement in survival. Clinical features are inadequate for risk stratification, and no molecular classifiers guide therapy. An international working group evaluated candidate prognostic biomarkers for clinical translation. Pre-treatment circulating tumor DNA is positioned for clinical implementation, while additional classifiers warrant prospective validation. This work establishes a path to risk-adapted, biologically informed treatment.

Journal Article↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Fatty acids and breast cancer: Epidemiology, subtype-specific metabolism, immune regulation, and clinical translation.

Fatty acids (FAs) are bioactive dietary and metabolic molecules that participate in membrane architecture, energy homeostasis, inflammatory signaling, gene regulation and immune function, all of which intersect with breast cancer (BC) risk, progression and treatment response. In this narrative review we integrate epidemiological, clinical, translational and mechanistic evidence on the role of FAs in BC. Saturated, monounsaturated, trans- and polyunsaturated FAs (PUFAs) are treated as distinct biological exposures rather than interchangeable measures of total fat intake. Similarly, evidence from dietary assessment, circulating biomarkers, erythrocyte membrane composition, adipose tissue stores and tumor lipid signatures is interpreted separately, because each captures exposure and biology at a different level. BC subtypes differ in FA synthesis, uptake, oxidation, storage and remodeling: luminal tumors are frequently linked to hormone-regulated lipogenesis, human epidermal growth factor receptor 2 (HER2)-positive tumors to growth-factor-driven lipid metabolism, and triple-negative tumors to exogenous FA uptake, inflammatory lipid mediators and ferroptosis-related vulnerabilities. FA-derived mediators also shape immune-cell polarization, cytokine signaling and the tumor microenvironment, and dietary FAs may reshape the gut microbiota; the fiber-derived short-chain FAs it produces, distinct from dietary FAs, likewise help regulate immune and inflammatory tone. Clinical data suggest possible roles for fat-quality modification and selected n-3 PUFA interventions, but findings are heterogeneous and not yet sufficient to support routine biomarker-guided precision onco-nutrition. Candidate biomarkers, such as erythrocyte n-6:n-3 composition, require prospective validation before clinical implementation. FA biology thus represents a modifiable but complex axis in BC prevention, tumor biology and supportive care.

Humans↗

PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗

PubMed2026

Nanocarrier-Based Gene Delivery Systems: Mechanisms, Clinical Translation, and Future Perspectives.

Gene therapy holds revolutionary potential for managing genetic disorders, cancers and infectious illnesses. However, one of the biggest challenges is delivering DNA or RNA into targeted cells and in the safe and effective way. In this review, nano carrier-based approaches for gene delivery are critically examined, focusing on both viral and non-viral systems. The advancement of CRISPR-Cas genome editing, machine learning-assisted nanocarrier optimization, and biologically inspired delivery systems is being quickly pushed forward in this area. In this review, a comparative analysis of gene delivery systems is being provided, and the key challenges to clinical translation are being pointed out. In addition, expert opinions on future research directions are being offered, with a heavy focus on the development of multifunctional, precisely targeted, and easily scalable delivery systems that can be integrated with next-generation therapeutic technologies.

Humans↗

PubMed2026

Unlocking the Circulating Proteome: Toward Clinical Translation.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.

Cellular senescence (CS) exerts dual roles in tumorigenesis, yet its pan-cancer molecular characteristics and clinical value remain unclear, hindering its translation to oncology and personalized therapy. To address the lack of specific and universal tools for senescence assessment and immunotherapy response prediction, this study systematically analyzed 1259 CS-related genes from the CellAge database across 31 cancer types by integrating multi-omics data, including bulk RNA-seq, single-cell/spatial transcriptomics, and CRISPR screening. We developed a rank-based algorithm SenScoreR (publicly available at https://gxhub.shinyapps.io/SenScoreR/ ) for senescence quantification, validated with 10 independent datasets, and constructed a machine learning-based predictive model CS.Sig for immunotherapy response. Results showed that tumors had significantly lower Rank-based Senescence Score (RSS) than normal tissues across 31 cancers (average diagnostic AUC = 0.895), with low RSS linked to poor survival; high RSS correlated with reduced genomic instability, enriched CD8⁺ T/NK cell/macrophage infiltration, upregulated PD-L1 expression, and elevated immune cytolytic activity. CS.Sig demonstrated robust performance in predicting ICI response (AUC = 0.716 across 10 cohorts), outperforming 13 existing signatures, while CRISPR screening identified 17 senescence-related targets (e.g., CEP55, PPP1CC) whose knockout enhanced anti-tumor immunity. Our findings clarify CS's role in maintaining tumor genomic stability and shaping immune microenvironments, and the developed SenScoreR, CS.Sig, and identified targets bridge basic CS research with clinical oncology, providing a translational resource and hypothesis basis for future experimental and clinical validation.

Journal Article↗

PubMed2026

MicroRNAs in Veterinary Viral Diseases: A Comprehensive Review from Molecular Mechanisms to Clinical Translation.

MicroRNAs (miRNAs) are small non-coding RNA molecules, approximately 22 nucleotides in length, that regulate post-transcriptional gene expression and have emerged as pivotal modulators of host-virus interactions. Veterinary viral diseases continue to pose substantial challenges to animal health, livestock productivity, food security, and public health, particularly due to their zoonotic potential. While miRNA research has advanced considerably, a comprehensive and critically integrated understanding of their biological functions and clinical applications across veterinary viral diseases remains incomplete. This comprehensive critical narrative synthesis addresses four overarching research questions: (1) What conserved and species-specific miRNA-mediated mechanisms govern major veterinary viral diseases? (2) What contextual factors determine antiviral vs. proviral duality? (3) To what extent do circulating miRNA signatures offer diagnostic and prognostic utility? (4) What translational barriers currently prevent clinical implementation, and how can the One Health framework help overcome them? Integrating three interconnected dimensions-molecular mechanisms, pathogen-specific responses, and translational applications-the review synthesizes evidence across PRRSV, avian oncogenic viruses (MDV, ALV), the immunosuppressive IBDV, FMD, BVDV, Ebola, Hendra, Rabies, and aquatic viral diseases. A key contribution of this review is the proposal of a four-axis contextual framework that explains the antiviral/proviral duality of miRNAs, and a 'One miRNA, One Health' convergence model with a concrete implementation roadmap. Key findings include: (a) a four-axis contextual framework (cell type, infection stage, viral strain, host-viral miRNA competition) that explains the antiviral/proviral duality; (b) virus-encoded miRNAs (v-miRNAs) as lower-risk therapeutic targets due to their absence from uninfected host genomes; (c) circulating miRNA biomarkers validated only at proof-of-concept stage (TRL 1-3), with no veterinary product yet at TRL ≥4; and (d) zoonotic conservation of miR-155, miR-146a, miR-21, and miR-122 across human and veterinary pathogens, supporting a 'One miRNA, One Health' convergence strategy. Critical short-term priorities are standardized pre-analytical protocols, open-access veterinary miRNA databases, and multicenter validation in natural infection cohorts.

Antiviral therapy↗

PubMed2026

Non-coding RNAs in cancer: multi-omics insights, liquid biopsy advances, drug resistance mechanisms, and the road to clinical translation.

For most of the twentieth century, the transcriptional output of the human genome was thought to be biologically inert-a characterization that has been proven wrong in almost every important respect. Non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), small nucleolar RNAs (snoRNAs) and PIWI-interacting RNAs (piRNAs) are now thought of as vital regulators of gene expression in all the stages of cancer pathogenesis, including the initial epigenetic changes, metastatic spread and the development of therapeutic resistance. This review highlights four areas where the clinical potential of ncRNAs is most promising: reconstruction of ncRNA regulatory networks by multi-omics integration; circulating ncRNAs as minimally invasive cancer biomarkers; causal roles of ncRNAs in drug resistance through epithelial-mesenchymal plasticity, metabolic reprogramming, and stromal communication; and translation of ncRNA targeting strategies to clinical trials. We will need to invest equally in mechanistic rigor and translational infrastructure to move forward.

antisense oligonucleotides↗

PubMed2026

DNA sequencing for microbial surveillance in cystic fibrosis airways: advances, challenges, and clinical translation.

SUMMARYDNA sequencing has revolutionized microbial surveillance in cystic fibrosis (CF), transforming pathogen identification from culture-dependent to total microbial community identification using molecular-based approaches. Techniques such as 16S rRNA gene sequencing have uncovered the complexity of the CF airway microbiome, while shotgun metagenomics, metatranscriptomics, and viromics now provide strain-level, functional, and viral insights beyond bacterial identification. Despite these advances, key technical and logistical challenges remain, including the processing of high-viscosity sputum samples, overwhelming host DNA contamination, managing large data sets, and the integration of complex bioinformatic outputs into clinical workflows. Emerging innovations such as host DNA depletion protocols, targeted enrichment panels, and adaptive sampling on Oxford Nanopore platforms are helping to overcome these barriers, improving microbial recovery and sequencing efficiency. As cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies are changing the lives of people with cystic fibrosis (pwCF), sequencing offers an unprecedented opportunity to track potential microbial adaptation in response. This review investigates current advances, limitations, and translational opportunities in DNA sequencing for CF airway microbiome surveillance, highlighting how these technologies can help reshape research and clinical microbiology in the post-modulator era.

Cystic Fibrosis↗

PubMed2026

Syncytium-forming HSV-1 in cancer gene therapy: From molecular mechanisms to clinical translation.

Gene therapy has emerged as a promising strategy for cancer treatment, yet challenges in efficient gene delivery remain a major barrier. Herpes simplex virus type 1 (HSV-1), as an oncolytic virus, has garnered attention for its potential in cancer therapy due to its replicative capacity, large genomic payload, and relatively low toxicity. Notably, syncytium-forming HSV-1 (SF-HSV-1) not only exhibits enhanced and sustained antitumor efficacy but also triggers profound immune responses. However, the exact molecular mechanisms orchestrating HSV-1-induced syncytium formation, its resulting cytotoxicity, and its precise role in immune modulation remain incompletely understood. This review aims to provide an in-depth exploration of the mechanisms underlying HSV-1 syncytium formation and its therapeutic implications in cancer gene therapy.

Humans↗

PubMed2026

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive genomic, cellular, spatial, and microenvironmental heterogeneity. Multi-omics studies have revealed that GBM follows complex evolutionary trajectories involving genetic, epigenetic, transcriptional, and immune-microenvironmental remodeling as tumors grow, adapt to the brain microenvironment, and acquire therapeutic resistance. Increasing evidence suggests that GBM may originate from aberrant neural stem or progenitor cells, including those residing in the subventricular zone, and that glioblastoma stem cells (GSCs) contribute to tumor propagation, heterogeneity, and recurrence. A key conceptual challenge is to reconcile hierarchical cancer stem cell models, in which GSCs are viewed as relatively stable tumor-propagating subpopulations, with dynamic state plasticity models, in which stem-like properties can be reversibly acquired or lost during transitions among proneural-like, mesenchymal-like, invasive, and therapy-tolerant states. Recent advances in single-cell profiling, spatial transcriptomics, lineage tracing, organoid culture, 3D bioprinting, genetically engineered models, and artificial intelligence (AI)-assisted computational modeling have substantially improved the ability to study these processes. However, no currently available model fully recapitulates human GBM heterogeneity, recurrence, treatment history, and tumor-microenvironment interactions. Therefore, model selection should be guided by clearly defined mechanistic questions rather than by reliance on any single platform. This review summarizes current advances in in vitro, ex vivo, in vivo, and computational models for studying GBM evolution and heterogeneity, and discusses how integrated model pipelines may improve preclinical drug testing, treatment-response prediction, and precision neuro-oncology.

Humans↗