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PubMed1978

Anchoring cells (Desmocytes) in the hydrozoan polyp Cordylophora.

Desmocytes or anchoring cells are present on the upright stolons of the athecate hydroid Cordylophora caspia and function to support the soft coenosarc within the rigid tube of perisarc by linking the perisarc with the mesoglea. These cells are characterized by accumulations of 70 A filaments which aggregate into dense rods at the apical end and contact the perisarc. At the base of the desmocytes the filaments are distributed within large cytoplasmic processes which interdigitate with an extension of the mesoglea. Desmocytes in Cordylophora are temporally and spatially formed in sequence as the upright elongates. Depending on their location and structure they can be categorized as forming, functional, or remnant desmocytes. The youngest, forming desmocytes are found in the distal end of the stolon 0.5-1.0 mm from the base of the hydranth. In this region coenosarc is just beginning to separate from the perisarc. Functional desmocytes are scattered 1-3 mm from the base of the hydranth and are associated with perpendicular extensions of the mesoglea. Remnants have lost their mesogleal connection and are located in more proximal, older regions of upright stolon. Support provided by the desmocytes to the upright stolon is limited by three factors that characterize the athecate hydroid: distribution of perisarc, pattern of growth, and extent of movement. The distal location of forming desmocytes is coincident with the hardening of new perisarc. The temporary nature of attachment sites is directly related to upright elongation. It is probable that the orientation of filaments within the cell and the mesogleal extension provide an addition feature of flexibility necessary to permit feeding, growth, and rhythmic pulsation movements characteristic of these hydroids.

Animals↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2026

Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.

Host-microbe crosstalk refers to the reciprocal influences between a host and its resident or invading microorganisms. This crosstalk plays important roles in maintaining host health, regulating physiological functions, and coordinating responses to infection. The rapid rise of spatial omics is transforming how this crosstalk is studied in both animals and plants. Unlike traditional bulk omics, which homogenize tissues and erase spatial context, spatial methods preserve in situ organization and can simultaneously capture molecular information from hosts and microbes. As a result, researchers can characterize the spatial organization of colonization and infection, identify spatial associations between microbial niches and host cell states, and visualize local host response gradients across intact tissues. Current spatial omics technologies encompass sequencing-based, imaging-based, and hybrid platforms. Spatial multi-omics approaches enable the joint measurement or integration of gene expression, protein abundance, and metabolite distributions. Although spatial association alone does not establish causality, spatial omics provides a high-resolution framework for characterizing host-microbe relationships within intact tissues and generating spatially constrained, testable hypotheses. When combined with perturbation experiments and complementary experimental evidence, these hypotheses can contribute to mechanistic interpretation of host-microbe crosstalk. Here, we review spatial omics technologies, compare their suitability and major trade-offs for host-microbe studies, and discuss computational strategies, analytical challenges, and future prospects.

Multiomics↗

PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗

PubMed2026

Volumetric DNA microscopy for mapping spatial transcriptomes in three dimensions.

The architecture and function of biological systems are inherently three-dimensional, yet most existing spatial transcriptomic technologies remain restricted to thin tissue sections, limiting their capacity to resolve cellular organization and microenvironments within intact tissue volumes. To address this limitation, we developed volumetric DNA microscopy, a scalable, optics-free approach for spatial transcriptome profiling directly within intact biological specimens. The method encodes spatial information into DNA molecules that form a dense intermolecular network in situ, enabling the reconstruction of three-dimensional spatial relationships through short-read sequencing and computational analysis. Here we detail the complete workflow including in situ cDNA synthesis, spatial encoding through DNA nanoball formation, dual-scale proximity bridging between neighboring nanoballs and spatial reconstruction via geodesic spectral embedding. Sequencing libraries can be generated within 7-8 d by a competent graduate-level molecular biologist, followed by standardized downstream computational analysis. Because the workflow requires only routine molecular biology reagents and a benchtop sequencer, volumetric DNA microscopy provides a versatile platform for exploring genetic and morphological features in intact tissues.

Spatial Transcriptomics↗

PubMed2025

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗

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↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2026

Spatially guided in vivo single-cell functional genomics of postnatal heart.

Understanding how spatial organization and cell-cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues.

Animals↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p < 0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8 + T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed1976

The evolution of plants and animals under domestication: the contribution of studies at the molecular level.

Protein molecules are essential catalysts in life processes and also form much of the substance of living material. Their three dimensional structures determine their biological function. Their biosynthesis is primarily determined by arrays of nucleic acid macromolecules (DNA and RNA), and the amino acid sequences that constitute their long spatially organized peptide-chain molecules reflect at one remove this DNA coding system, and thus record a step-by-step history of some of the viable genetic events (natural or man-controlled) that have created the organism and the breed. Amino acid sequences can be used to trace the progress of controlled breeding in two ways: by extrapolation back from living breeds, and by analysis of ancient protein material. Of the latter, bone or tendon or skin collagens and hair keratins are the most perfectly preserved as molecular structures through 20,000 years and indeed much longer. Amino acid sequences are expensive to determine (collagen has 1052 amino acid residues), and the potential of this palaeobiological information has been as yet little exploited. The first approach has, however, been more explored, in both plants and animals. Several protein systems must be studied in conjunction to reveal the phylogenetic threads in any one breed. As the three dimensional quaternary structure of protein molecules becomes more appreciated in relation to biological function, and as new techniques and procedures are developed, amino acid sequence data can become more informative in our ultimate understanding of early selective breeding.

Amino Acid Sequence↗

PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗

PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗

PubMed2026

Mycosis Fungoides-Like Atopic Dermatitis Represents a Th22-Dominant Inflammatory Endotype.

BACKGROUND: Early-stage mycosis fungoides (MF) often presents diagnostic challenges because of its clinical overlap with atopic dermatitis (AD). In clinical practice, we encountered a subset of patients with severe AD who fulfilled the MF diagnostic criteria yet remained clinically indistinguishable from AD and presented refractoriness to advanced therapies. We termed this ambiguous entity "mycosis fungoides-like AD" (mfAD) and sought to determine whether it represents malignant transformation or a distinct inflammatory endotype of AD. METHODS: Skin biopsies were obtained from 7 patients with AD and 11 patients with mfAD. We performed paired single-cell RNA sequencing and single-cell T-cell receptor sequencing analyses. Publicly available MF and AD datasets were integrated for comparative analysis. Spatial transcriptomic profiling was used to contextualize single-cell findings within the tissue architecture. RESULTS: Comparative transcriptomic analysis revealed that T cells in mfAD were aligned with those in AD and lacked genomic instability. High-resolution profiling showed that mfAD was characterized by oligoclonal Th22 expansion rather than a single dominant malignant clone. Notably, all patients with mfAD achieved rapid clinical remission with selective JAK1 inhibition, indicating the therapeutic response characteristics of inflammatory dermatoses. CONCLUSION: Our findings demonstrate that mfAD is not a true malignancy, but rather a Th22-driven inflammatory endotype of AD. These results redefine mfAD as an inflammatory subtype within the AD spectrum, providing a mechanistic explanation for both the "pseudo-monoclonality" that leads to MF misdiagnosis and the failure of dupilumab. This study establishes a rationale for the use of JAK inhibitors in precision medicine for this patient population.

JAK inhibitor↗

PubMed2026

Understanding tumor adaptations and resistance to MET inhibitors in MET-altered non-small cell lung cancer.

AIM: Type Ib MET inhibitors are clinically active in selected MET-altered non-small cell lung cancer, particularly tumors with MET exon 14 skipping or MET amplification, but acquired resistance remains incompletely understood. Here, we investigated resistance across biologically distinct MET-altered contexts, including MET exon 14 skipping, MET amplification, and MET overexpression. METHODS: Paired baseline and progression samples from seven patients treated with tepotinib or capmatinib were analyzed using spatial transcriptomics, whole-exome sequencing, RNA sequencing, CRISPR screening, and drug-combination assays. Patient-derived cultures and resistant cell-line models were used to explore resistance-associated changes. RESULTS: MET inhibitor resistance was heterogeneous, with persistence of the initial MET alteration in most evaluable cases and emergence of patient-specific genomic events. Three main resistance-associated, often overlapping, routes were identified: on-target MET evolution through kinase-domain alterations; extracellular matrix and tumor-microenvironment remodeling, including collagen and fibronectin upregulation, complement-related signaling, and partial EMT-associated programs; and bypass signaling involving EGFR/HER, MAPK, and PI3K/Akt pathways. In vitro models reproduced several tumor-cell-intrinsic features but only partially captured microenvironment-associated changes. CONCLUSIONS: MET inhibitor resistance in this cohort involved overlapping, context-dependent genomic, phenotypic, and signaling adaptations, supporting combination strategies for MET-altered lung cancer.

CRISPR screen↗