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At least 19 recordsLinked to original sources

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

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↗

PubMed2026

SeqUIaSCOPE: multi-omics data integration platform for single-patient clinical oncology pathway exploration.

SUMMARY: SeqUIaSCOPE is an open-source platform designed for routine clinical oncology diagnostics through case-centric integration and visualization of genomic variants, fusion events, and expression profiles. The platform combines molecular-level validation via embedded genome browsing with systems-level interpretation through dynamic pathway visualization, enabling geneticists to assess how alterations converge across biological networks. Flexible reporting with customizable templates accommodates diverse institutional requirements, while secure cluster-based or local deployment ensures compliance with data protection policies, making advanced multi-omics diagnostics accessible to academic and clinical institutions. AVAILABILITY AND IMPLEMENTATION: SeqUIaSCOPE is freely available on GitHub at https://github.com/BioIT-CEITEC/sequiascope under the MIT license and archived at Zenodo (https://zenodo.org/records/21338445). Due to the sensitive nature of patient data, the repository provides simulated datasets that mimic the structure of real clinical data for testing and exploration. Documentation and a live demo accompany these datasets, allowing users to explore the application without any prior setup. The repository also includes a Helm chart for Kubernetes deployment and Docker containers for local deployment, ensuring compatibility across Linux, macOS, and Windows. No user registration is required, and all data remains on local or institutional infrastructure.

Humans↗

PubMed2026

Standardizing stem cell enumeration: A methodological comparison of single and dual flow cytometry platforms.

Two flow cytometry methods are used for stem cell (CD34+) enumeration; single platform (SP) and dual platform (DP). While several studies reported comparable results, others suggested superiority of the SP method. This study evaluated variations between both methods using a modified workflow. A total of 54 fresh and thawed specimens, including mobilized peripheral blood, apheresis products, and umbilical cord blood, were analyzed using both methods. High concordance between SP and DP methods was observed for absolute viable CD34+ counts in fresh and thawed specimens (p = 0.088 and 0.427, respectively), as well as for CD34+ viability (p = 0.085 and 0.801). Absolute viable WBC counts were comparable between methods in thawed specimens (p = 0.124), whereas a modest statistical variation was observed in fresh specimen group (p = 0.039), largely influenced by umbilical cord blood samples. Variation in absolute viable CD34+ counts remained within clinically acceptable limits, with median variations of 2.4 for fresh and 1.4 for thawed samples. SP and DP methods demonstrated high concordance for absolute viable CD34+ enumeration and CD34+ viability in fresh and thawed specimens. Although a modest variation in viable WBC counts was observed in fresh samples, this did not affect CD34+ enumeration and remained clinically acceptable. While SP provides a standardized approach, the DP method offered greater gating flexibility, with fewer technical resources required, and was approximately 70% more cost-effective, supporting its use as a practical alternative in appropriate laboratory settings.

Humans↗

PubMed2025

SLB-msSIM: A Spectral Library-Based Multiplex Segmented SIM Platform for Single-Cell Proteomic Analysis.

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by a multiplex segmented selected ion monitoring (msSIM) technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity in various pancreatic cancer cell lines, revealing common and distinct functional traits among PANC-1, MIA-PaCa2, AsPc-1, HPAF, and normal HPDE cells. Furthermore, for the first time, our novel data revealed the diverse cell trajectories of individual PANC-1 cells during the induction and reversal of epithelial-mesenchymal transition (EMT). Collectively, our results demonstrate that SLB-msSIM is a highly sensitive and robust platform, applicable to a wide range of instruments for single-cell proteomic studies. SUMMARY: We present the SLB-msSIM method, a conceptually unique approach in mass spectrometry-based single-cell proteomics that significantly enhances sensitivity and robustness. This innovative platform enables detailed analysis of the proteome landscape, capturing cellular heterogeneity, trajectories, and phenotypes at a single-cell resolution. Utilizing the SLB-msSIM technique, we identified both common and distinct functional traits among various pancreatic cancer cell lines and normal cells. Moreover, our study unveiled new insights into the diverse cell trajectories of individual cancer cells during the induction and reversal of epithelial-mesenchymal transition (EMT). In summary, the SLB-msSIM method offers a highly sensitive and robust platform for single-cell proteomic studies, with broad applicability across different instruments.

Single-Cell Analysis↗

PubMed2026

Mapping convergent regulators of melanoma drug resistance by PerturbFate.

High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes. However, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference1 perturbations across the full spectrum of gene regulation, from chromatin remodelling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled more than 300,000 cultured melanoma cells to characterize multimodal phenotypic and gene regulatory responses to perturbations in more than 140 vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator complex components, linking module-specific biochemical properties to convergent transcriptional activations. We identified common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineated how perturbations in functionally unrelated genes reshape cell state. Thus, PerturbFate establishes a versatile platform for identifying key molecular regulators by anchoring multimodal regulatory dynamics to disease-relevant phenotypes.

Humans↗

PubMed2026

ONT-only genome assembly of a Korean male individual using a semen sample.

BACKGROUND: Long-read sequencing has enabled the generation of high-quality human genome assemblies, but many previous assemblies were based on blood-derived DNA and often relied on limited data types from a single sequencing strategy. OBJECTIVE: This study aimed to generate high-quality phased genome assemblies of a Korean individual using multiple independent long-read datasets produced from a single sequencing platform and to evaluate their utility for chromosome-scale assembly and variant detection. METHODS: Genomic DNA was extracted from a semen sample of a Korean male. Long-read, ultra-long-read, and chromatin conformation capture sequencing data were generated using Oxford Nanopore Technologies. These datasets were integrated to construct phased genome assemblies, followed by correction of noticeable phasing errors and assessment of assembly continuity, chromosomal representation, telomeric repeat recovery, and variant detection performance. RESULTS: The final phased assemblies spanned approximately 2.9 Gb and represented 23 pairs of chromosomes with an NG50 of 150 Mb. Telomeric repeats were detected at 36 and 37 of the 48 chromosomal ends in the two assemblies, indicating high end-to-end completeness. In addition, we successfully identified structural variants, including small variants. These results demonstrate that combining multiple Oxford Nanopore data types can produce highly continuous and informative phased human genome assemblies. CONCLUSIONS: We generated high-quality phased genome assemblies of a Korean individual using Oxford Nanopore long-read sequencing data derived from semen DNA. This publicly available genome resource will support broader applications of long-read sequencing in human genomics and variant analysis.

Humans↗

PubMed2026

Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancer.

Immunosuppressed patients with non-melanoma skin cancer experience worse clinical outcomes, yet the tumor immune microenvironment associated with systemic immunosuppression remains incompletely defined. Using integrated single-cell, spatial transcriptomic, multiplex immunofluorescence, and spatial epigenomic profiling across immunocompetent and immunosuppressed tumors, we found that overall immune-cell composition was largely preserved despite differences in immune-cell distribution, spatial organization, and T cell clonality. Immunosuppressed tumors demonstrated reduced intratumoral macrophage densities, decreased T cell clonal diversity, altered antigen-presenting cell and T cell spatial interactions, and distinct fibroblast- and macrophage-associated spatial niches. Multi-cohort validation across complementary spatial and single-cell platforms identified consistent alterations in innate-adaptive immune organization in immunosuppressed tumors. Together, these findings define spatial and functional remodeling of the tumor immune microenvironment under systemic immunosuppression and provide a framework for future therapeutic investigation in high-risk patients.

Humans↗

PubMed2026

Detection of antibiotic heteroresistance in clinical microbiology: current and emerging methodologies.

BACKGROUND: Antibiotic heteroresistance (HR) is characterised by the coexistence of susceptible and resistant subpopulations within an apparently isogenic bacterial isolate. Because routine antimicrobial susceptibility testing (AST) primarily assesses the dominant population, HR may escape detection, potentially leading to discrepancies between laboratory susceptibility categorisation and the underlying bacterial population structure. OBJECTIVES: To provide a critical and practice-oriented evaluation of current and emerging methodologies for HR detection and to discuss their strengths, limitations, and potential for clinical implementation. SOURCES: Narrative review based on PubMed searches, complemented by screening of key reference lists and relevant EUCAST and CLSI documents. Peer-reviewed literature was prioritised. CONTENT: Phenotypic approaches, particularly population analysis profiling, remain the reference method for HR definition, but their labour-intensive workflows, long turnaround times, and limited standardisation restrict routine implementation. Alternative strategies, including modified AST assays, metabolic assays, and single-cell platforms, offer gains in speed or throughput but require broader validation. Molecular approaches such as quantitative PCR, droplet digital PCR, targeted deep sequencing, and whole-genome sequencing improve detection of minority resistance determinants. Emerging computational frameworks, including machine learning models integrating phenotypic and genomic data, represent a promising frontier for scalable HR prediction. IMPLICATIONS: Available evidence supports the clinical relevance of HR, although its association with adverse outcomes varies across bacterial species and antibiotic classes. Harmonised methodologies and clinically validated interpretive criteria are needed to support integration of HR assessment into routine diagnostics. Prospective multicentre studies and further standardisation, including engagement with EUCAST and CLSI, will be important to advance clinical implementation.

Antimicrobial resistance↗

PubMed2024

Simultaneous targeting of peripheral and brain tumors with a therapeutic nanoparticle to disrupt metabolic adaptability at both sites.

Brain metastasis of advanced breast cancer often results in deleterious consequences. Metastases to the brain lead to significant challenges in treatment options, as the blood-brain barrier (BBB) prevents conventional therapy. Thus, we hypothesized that creation of a nanoparticle (NP) that distributes to both primary tumor site and across the BBB for secondary brain tumor can be extremely beneficial. Here, we report a simple targeting strategy to attack both the primary breast and secondary brain tumors utilizing a single NP platform. The nature of these mitochondrion-targeted, BBB-penetrating NPs allow for simultaneous targeting and drug delivery to the hyperpolarized mitochondrial membrane of the extracranial primary tumor site in addition to tumors at the brain. By utilizing a combination of such dual anatomical distributing NPs loaded with therapeutics, we demonstrate a proof-of-concept idea to combat the increased metabolic plasticity of brain metastases by lowering two major energy sources, oxidative phosphorylation (OXPHOS) and glycolysis. By utilizing complementary studies and genomic analyses, we demonstrate the utility of a chemotherapeutic prodrug to decrease OXPHOS and glycolysis by pairing with a NP loaded with pyruvate dehydrogenase kinase 1 inhibitor. Decreasing glycolysis aims to combat the metabolic flexibility of both primary and secondary tumors for therapeutic outcome. We also address the in vivo safety parameters by addressing peripheral neuropathy and neurobehavior outcomes. Our results also demonstrate that this combination therapeutic approach utilizes mitochondrial genome targeting strategy to overcome DNA repair-based chemoresistance mechanisms.

Brain Neoplasms↗

PubMed2026

MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics.

Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.

Bioinformatics software↗

PubMed2025

Exome-wide association study of bleeding events in patients receiving direct oral anticoagulants.

BackgroundDirect oral anticoagulants (DOACs) are first-line medications for stroke prevention in non-valvular atrial fibrillation (AF). However, variability in drug response poses risks of hemorrhagic or thromboembolic events.ObjectivesAlthough genetic influences on DOACs safety are increasingly recognized, robust evidence directly linking specific polymorphisms to bleeding risk remains limited.DesignMulti-center observational case-control study including exome-wide association analysis of 196 non-valvular AF patients treated with rivaroxaban or apixaban, comprising 97 with bleeding complications and 99 without.MethodsDOAC plasma concentrations, urinary 6-β-hydroxycortisol and cortisol levels were measured for CYP3A4 phenotyping. Sequencing was performed on the DNBSEQ G-400 platform. Single-nucleotide variant (SNV) associations with bleeding risk were assessed using logistic regression with additive, dominant, and recessive genetic models. Polygenic risk scores (PRSs) were calculated to evaluate cumulative genetic effects.ResultsNo SNVs reached Bonferroni-corrected significance under any model. PRSs showed weak predictive ability for bleeding with apixaban. For rivaroxaban, regression indicated that ln Css min/D + 1 index increased with PRS, age, and 6-β-hydroxycortisol/cortisol ratio, but decreased with higher 6-β-hydroxycortisol and coronary heart disease presence. No statistically significant differences were found for the PharmGKB Level 3 variants rs1045642 (rivaroxaban) and rs2231142 (apixaban). Trends toward statistical significance were observed for the rs2472304-G variant in rivaroxaban users, rs6977165-C in apixaban users, and for the CYP3A4*1/*36 diplotype.ConclusionResidual equilibrium concentration of DOACs, including dose-adjusted, did not independently predict bleeding risk in non-valvular AF patients. Variants rs2472304 and rs6977165 may warrant further investigation as potential contributors to bleeding risk.

Humans↗

PubMed2025

ShortCake: an integrated platform for efficient and reproducible single-cell analysis.

SUMMARY: Recent advances in single-cell analysis have introduced new computational challenges. Researchers often need to use multiple analysis tools written in different programming languages while managing version conflicts between related packages within a single workflow. For the research community, minimizing the time spent on environment setup and installation issues is essential. We present ShortCake, a containerized platform that integrates a suite of single-cell analysis tools written in R and Python. ShortCake isolates competing Python tools into separate virtual environments that can be easily accessed within a Jupyter notebook. This enables users to effortlessly transition between various environments, including R, even within a single notebook. Additionally, ShortCake offers multiple "flavors," enabling users to select container images tailored to their specific needs. ShortCake provides a unified environment with fixed versions of various tools, thus streamlining workflows, reducing setup time, and improving reproducibility. AVAILABILITY AND IMPLEMENTATION: The ShortCake image is available on DockerHub (https://hub.docker.com/r/rnakato/shortcake) and Zenodo (DOIs: 10.5281/zenodo.17116765 and 10.5281/zenodo.17118158). The source code is available on GitHub (https://github.com/rnakato/ShortCake).

Single-Cell Analysis↗

PubMed2025

Mismatch-introduced crRNA guided PCR-CRISPR/Cas12a platform improves EGFR point mutation detection in single tumor cell.

Dynamic monitoring of epidermal growth factor receptor (EGFR) mutations is essential for the early identification of resistance and treatment adaptation. Single-cell heterogeneity analysis is crucial for precision cancer medicine, yet sensitive and specific detection methods for individual tumor cells remain challenging. Here, we develop a PCR-CRISPR/Cas12a platform enhanced by the incorporation of mismatched base in crRNA at specific site for single-cell point mutation detection. This platform demonstrated high specificity and sensitivity, detecting point mutation at a frequency of 0.1% and in as low as 1.02 ng of genomic DNA, which represents an improvement over the amplification-refractory mutation system PCR (ARMS-PCR). Notably, the accuracy of the platform is highly consistent with next-generation sequencing (NGS), as evidenced by Kappa test values surpassing 0.9. By utilizing a conical-pore membrane with optimized porosity for single circulating tumor cell (CTC) enrichment, our platform enables point mutations detection in individual tumor cells, offering potential enhancements in precision and reliability for EGFR mutation analysis. This novel methodology holds potential for more accurate and personalized cancer treatment strategies.

Humans↗

PubMed2026

High-throughput single-cell proteomics and transcriptomics from same cells with a nanoliter-scale, spin-transfer approach.

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

Single-Cell Analysis↗

PubMed2026

transFusion: a novel comprehensive platform for integration analysis of single-cell and spatial transcriptomics.

MOTIVATION: Understanding spatial organization, intercellular interactions, and regulatory networks within the spatial context of tissues is crucial for uncovering complex biological processes and disease mechanisms. Spatial transcriptomics technologies have revolutionized this field by enabling the spatially resolved profiling of gene expression. 10× Visium has emerged as the predominant spatial technology, but its low resolution and the complexity of integrating multimodal datasets present significant analytical challenges, particularly for researchers with limited computational and statistical expertise. Current spatial transcriptomics analysis platforms generally fall short of effectively integrating multimodal data and maximizing the utility of spatial information-such as uncovering complex cellular spatial dependencies, multimodal gradient patterns, and spatial coexpression of ligand-receptor pairs and regulatory networks related to disease or biological states-thereby limiting their ability to provide comprehensive end-to-end analytical workflows when analyzing 10× Visium data. RESULTS: To address these limitations, we developed transFusion, a novel, advanced web-based platform specializing in the most comprehensive and effective integration analysis of scRNA-seq and 10× Visium spatial transcriptomics data. transFusion offers 12 key functions, from basic visualization to advanced analyses, including intercellular dependency analysis, ligand-receptor coexpression identification and visualization, and spatial multimodal gradient variation patterns. Two case studies were used to demonstrate transFusion's capabilities in exploring tissue architecture, intercellular communication, dependency networks, and multimodal gradient variation patterns with minimal computational skills and statistical expertise. transFusion provides a flexible and powerful framework for multimodal data integration analysis. AVAILABILITY AND IMPLEMENTATION: transFusion is freely available at https://github.com/WQLin8/transFusion.

Spatial Transcriptomics↗

PubMed2026

Clinical evidence on non-viral CAR-T cell therapies for solid tumors: a scoping review.

BACKGROUND: Chimeric antigen receptor (CAR) T-cell therapy in solid tumors is hindered by the immunosuppressive tumor microenvironment and by toxicities associated with viral-vector manufacturing. Non-viral gene delivery platforms have emerged as a potential alternative, though clinical evidence remains fragmented. METHODS: Following an a priori protocol registered on the Open Science Framework (OSF; https://doi.org/10.17605/OSF.IO/2TPQS) and adhering to JBI/PRISMA-ScR guidelines, a systematic search was conducted across four databases from inception through May 15, 2026. Patient-level data were extracted to describe cellular persistence and clinical outcomes across strictly non-viral delivery platforms. RESULTS: Four early-phase studies met the inclusion criteria, encompassing 28 heavily pretreated patients with metastatic solid tumors. Two non-viral platforms were identified: mRNA electroporation (n=19; intravenous in 13, intratumoral in 6) and the piggyBac transposon system (n=9). Across both mRNA routes, transient CAR-T persistence (<7 days) was observed, with no objective responses (ORR 0%), though disease stabilization yielded a disease control rate (DCR) of 53%; cross-route comparison is limited by differing distribution profiles. The piggyBac system showed longer persistence (~28 days) and a DCR of 78%, including the only documented objective response (ORR 11%). No Grade ≥3 cytokine release syndrome or neurotoxicity was reported in any of the 28 patients, and no tocilizumab or systemic corticosteroids were required. CONCLUSIONS: Within this limited early-phase evidence base, no severe toxicities attributable to non-viral platforms were reported, and the evidence identifies knowledge gaps warranting prospective investigation. mRNA platforms showed transient persistence and disease stabilization in 53% of patients. One partial response was documented with the piggyBac platform in a single patient; however, this outcome cannot be attributed to the delivery platform given simultaneous differences in target antigen, tumor histology, route of administration, and geographic setting. No firm conclusions regarding comparative platform performance can be drawn from this evidence base. SYSTEMATIC REVIEW REGISTRATION: https://doi.org/10.17605/OSF.IO/2TPQS, identifier OSF.IO/2TPQS.

Humans↗

PubMed2026

Longitudinal In Vivo Imaging at Single-Lesion Resolution Identifies Allele-Associated Response and Resistance Dynamics in EGFR-Mutant Lung Cancer.

Acquired resistance to targeted therapies is inevitable in EGFR-mutant non-small cell lung cancer (NSCLC), yet the principles governing its emergence in vivo remain incompletely understood. In particular, how lesion-level response patterns vary across distinct EGFR allele contexts during therapy has not been systematically examined at single-lesion resolution. Here, we establish a longitudinal in vivo imaging platform enabling single-lesion resolution tracking of tumor behavior during therapy in genetically engineered mouse models representing clinically relevant EGFR alleles. Using high-resolution micro-computed tomography (micro-CT) and three-dimensional reconstruction, we monitor tumor growth, therapeutic response, and resistance during osimertinib treatment. EGFR genotype is associated with distinct patterns of tumor growth, response kinetics, and resistance timing. Therapeutic response is spatially heterogeneous, with coexisting lesions undergoing complete regression, persistence, or progression within the same lung. During treatment, spatially distinct lesion-level behaviors included persistent growth during therapy and initial regression followed by regrowth. These findings demonstrate the utility of longitudinal micro-CT imaging to investigate allele-associated differences in treatment response and resistance timing at single-lesion resolution in vivo.

Animals↗