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PubMed2025

Individualized patient tumor organoids faithfully preserve human brain tumor ecosystems and predict patient response to therapy.

Tumor organoids are important tools for cancer research, but current models have drawbacks that limit their applications for predicting response to therapy. Here, we developed a fast, efficient, and complex culture system (IPTO, individualized patient tumor organoid) that accurately recapitulates the cellular and molecular pathology of human brain tumors. Patient-derived tumor explants were cultured in induced pluripotent stem cell (iPSC)-derived cerebral organoids, thus enabling culture of a wide range of human tumors in the central nervous system (CNS), including adult, pediatric, and metastatic brain cancers. Histopathological, genomic, epigenomic, and single-cell RNA sequencing (scRNA-seq) analyses demonstrated that the IPTO model recapitulates cellular heterogeneity and molecular features of original tumors. Crucially, we showed that the IPTO model predicts patient-specific drug responses, including resistance mechanisms, in a prospective patient cohort. Collectively, the IPTO model represents a major breakthrough in preclinical modeling of human cancers, which provides a path toward personalized cancer therapy.

Humans↗

PubMed2026

Spatially defined microenvironmental niches are associated with clinical outcome and tumor ecosystem diversity in head and neck cancer.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) exhibits substantial biological heterogeneity that is not fully explained by human papillomavirus (HPV) status. The spatial organization of tumor, immune, and stromal cell populations and its relationship to clinical outcome remain incompletely understood. METHODS: We performed single-cell spatial transcriptomic and proteomic profiling of 44 primary HNSCC tumors, generating a spatial atlas of 19,471,501 cells across whole-slide tissue sections. Spatial niches and ecosystem states were identified through integrated computational analyses and evaluated for associations with tumor programs, clinicopathologic features, and patient outcomes. FINDINGS: HPV-negative tumors were enriched for fibroblast-rich, immune-poor niches associated with epithelial-mesenchymal transition and hypometabolic tumor programs, whereas HPV-positive tumors displayed more diverse immune, stromal, and vascular niche combinations and were enriched for immunogenic ecosystem states. Approximately 20% of HPV-positive tumors exhibited fibroblast-rich ecosystem architectures resembling HPV-negative disease and were associated with less favorable outcomes than other HPV-positive tumors of similar stage. In patient-derived co-culture models, extracellular matrix-associated fibroblasts were associated with epithelial-mesenchymal transition (EMT)-like tumor states, CD8+ T cell dysfunction, and chemotherapy resistance-associated phenotypes. CONCLUSIONS: Spatial ecosystem architecture is associated with clinically relevant heterogeneity beyond conventional HPV-based classification. Fibroblast-rich, immune-poor ecosystem states characterize a high-risk subset of HPV-positive tumors and may provide a framework for improved biological classification and risk stratification in HNSCC. FUNDING: This work was supported by the National Institutes of Health (R01CA291607 and R21CA267527-01) and the Feldstein Medical Foundation.

Humans↗

PubMed2025

The advent of precision nutrigeroscience in cancer: from clinic towards molecular biology.

Nutrimental patterns have been deemed to have an impact on cancer development and the response to cancer therapy. Our growing understanding of cancer and host metabolism has highlighted that nutrient availability in the tumor ecosystem is a key factor in inhibiting tumor development. Subsequently, dietary interventions must take into account the specific characteristics of both the cancer and the host, which requires a detailed understanding of the mechanisms that determine the metabolic vulnerabilities in the tumor ecosystem. In this review, we provide an overview of various dietary regimens as interventions in both preclinical models and clinical studies. We discuss how dietary intervention can affect the homeostasis of the tumor ecosystem, including neoplastic, micro- and macro-environmental states that impact cancer progression and therapy. Our emphasis is on the prospects of precision nutrigeroscience, which involves developing individualized therapeutic approaches and predictors based on a thorough exploration of the mechanisms and critical factors. This approach has the potential to enhance the efficacy of anti-cancer treatments and prevention strategies.

Humans↗

PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗

PubMed2026

Integrative multi-omics profiling of insomnia-related molecular features reveals microbiome, immune, and therapy-relevant heterogeneity in colorectal cancer.

Emerging evidence implicates insomnia as a potential risk factor in carcinogenesis, potentially involving systemic inflammation, circadian disruption, and microbiome alterations. However, the molecular associations linking insomnia-related features to colorectal cancer (CRC), particularly with respect to tumor biology, immune microenvironmental states, and therapy-relevant phenotypes, remain largely unexplored. Multi-omics integration of genomic, transcriptomic, and microbiome data from 3,026 CRC patients across seven independent cohorts, including a large, well-annotated Clinical Omics study of Colorectal Cancer in China (COCC) cohort, enabled insomnia-based molecular classification through unsupervised non-negative matrix factorization (NMF) clustering. The insomnia subtype (IS) was biologically characterized via pathway enrichment, immune deconvolution, microbial profiling, and single-cell transcriptomics. Furthermore, an insomnia score (ISscore) was developed and validated in multiple cohorts for risk stratification and assessment of treatment-response-related indicators in CRC. Unsupervised clustering revealed two distinct molecular subtypes (IS1/IS2), with IS2 demonstrating significantly poorer survival. IS2 exhibited marked activation of EMT/angiogenesis pathways versus cell cycle activation in IS1. The IS2 microenvironment showed increased immunosuppression-related infiltration and exhausted T cell signatures, together with intratumoral microbiome variation characterized by depletion of Ruminococcaceae UCG-002 and enrichment of Hungatella/Selenomonas. The ISscore system stratified survival risk and was associated with computational indicators of immunotherapy response. Single-cell analysis nominated PPIA-BSG as a potential cell-cell communication signal involving high-ISscore tumor cells, CXCL12+ endothelial cells, and CLEC9A+ dendritic cell subsets. This multi-omics characterization of insomnia-CRC interplay suggests that insomnia-related molecular features are associated with an immunologically distinct and microbiome-altered tumor ecosystem. The ISscore provides a reproducible framework for capturing insomnia-related molecular heterogeneity, supporting risk stratification and future evaluation of therapy-relevant phenotypes.IMPORTANCEChronic insomnia affects millions, but it is not typically considered a cancer risk factor. Our study, analyzing vast biological data from over 3,000 colorectal cancer patients, uncovers a potential link between a person's predisposition to insomnia and their risk of developing this disease. This suggests that the biological pathways related to sleep may play a role in cancer development. Understanding this connection opens up new avenues for identifying individuals at higher risk and developing novel prevention strategies for colorectal cancer.

colorectal cancer↗

PubMed2025

Sustained NF-κB activation allows mutant alveolar stem cells to co-opt a regeneration program for tumor initiation.

Disruptions to regulatory signals governing stem cell fate open the pathway to tumorigenesis. To determine how these programs become destabilized, we fate-map thousands of murine wild-type and KrasG12D-mutant alveolar type II (AT2) stem cells in vivo and find evidence for two independent AT2 subpopulations marked by distinct tumorigenic capacities. By combining clonal analyses with single-cell transcriptomics, we unveil striking parallels between lung regeneration and tumorigenesis that implicate Il1r1 as a common activator of AT2 reprogramming. We show that tumor evolution proceeds through the acquisition of lineage infidelity and reversible transitions between mutant states, which, in turn, modulate wild-type AT2 dynamics. Finally, we discover how sustained nuclear factor κB (NF-κB) activation sets tumorigenesis apart from regeneration, allowing mutant cells to subvert differentiation in favor of tumor growth.

Animals↗

PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2026

A Functionally Constrained Immune Ecosystem in Microsatellite-stable Colorectal Cancer Resolved by Single-cell and Exome Profiling.

BACKGROUND/AIM: Microsatellite-stable (MSS) colorectal cancer (CRC) generally responds poorly to immune checkpoint blockade, but some MSS tumors are T-cell rich. We examined whether such infiltration reflected effective immunity or functional immune constraint. CASE REPORT: A 77-year-old woman underwent resection of a mismatch repair-proficient (pMMR), MSS, low-mutational-burden CRC with a synchronous adenoma. Whole-exome sequencing of tumor, adenoma and adjacent normal tissue detected no shared high-confidence somatic mutations between tumor and adenoma within the sensitivity of this WES analysis and identified tumor-specific APC, KRAS and TP53 alterations. Tumor single-cell RNA sequencing yielded 7,569 cells, with T-lineage populations comprising 83.5%. Cytotoxic T cells showed cytolytic and dysfunction-associated features, regulatory T cells (Tregs) showed suppressive remodeling, and Th17 cells showed inflammatory/profibrotic programs. CellChat nominated stromal MIF/FN1-CD74/CD44 and extracellular-matrix communication with T-cell compartments. CONCLUSION: This molecular case report shows that T-cell abundance and immune effectiveness can be uncoupled in MSS CRC.

Humans↗

PubMed2026

Single-cell and spatial transcriptomic technologies for lung cancer tumor microenvironment analysis.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide; beyond its rising incidence, its marked molecular heterogeneity and complex tumor microenvironment (TME) hinder treatment response and drive resistance, contributing directly to its high mortality rate. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) provide complementary approaches for dissecting these features. scRNA-seq enables high-resolution analysis of cellular diversity and transcriptional states but requires tissue dissociation and therefore loses spatial context. In contrast, ST preserves tissue architecture and provides insights into how gene-expression programs within the TME are organized, although no currently available spatial platform combines whole-transcriptome coverage with true single-cell resolution over large tissue areas. Together, these technologies have enabled detailed mapping of tumor, immune and stromal populations and of their spatial interactions, revealing functionally distinct cellular niches that contribute to immune evasion, metastasis and response to therapy. In this narrative review we organize the primary literature around a single question, how spatially structured cellular ecosystems, rather than individual cell types, determine therapeutic response and resistance in lung cancer - and we explicitly separate observations that are reproducible across independent cohorts and platforms from those that remain confined to single studies. We further summarize the technical, analytical and logistic barriers that currently prevent spatially resolved signatures from entering routine diagnostic pathology. Understanding dysregulated pathways and spatially constrained intercellular communication within the TME helps identify candidate biomarkers and may support the identification of therapeutic approaches directed at tumor-intrinsic programs as well as at microenvironment-driven resistance mechanisms.

Cell-cell communication↗

PubMed2026

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans↗

PubMed2025

Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer.

BACKGROUND: Occult lymph node metastasis (LNM) is a key prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). This study was to establish radiomics models derived from intratumoral, peritumoral, and habitat regions for identifying occult LNM in HNSCC. METHODS: Patients with pathologically confirmed HNSCC from three medical Centers (from March 2014 to April 2024) and The Cancer Genome Atlas (TCGA) were enrolled. Center 1 was split into training (n = 330) and internal test sets (n = 154), while Center 2 and Center 3 served as the external test set (n = 183). Genomic set (n = 50) from TCGA and single-cell RNA sequencing set (n = 6) from Center 1 were used for biological analysis. We used the intratumoral, peritumoral, and habitat volumes of interest (VOIs) to extract radiomics features, respectively. Based on Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers, nine radiomics models were built to confirm the optimal predictive performance. The best-performing model, along with clinical-radiologic data, was combined to develop a hybrid model. The log-rank test was used to evaluate the model's prognostic performance. Additionally, bulk and single-cell RNA sequencing were applied for investigating the biological mechanisms underlying the optimal model. RESULTS: The RF-habitat radiomics model showed the best performance, achieving AUCs of 0.835-0.919 across all datasets. Survival analysis further confirmed the prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. CONCLUSIONS: The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.

Humans↗

PubMed2026

Cyclin-dependent kinase 4 and 6 inhibitors and the breast cancer immune ecosystem: immune remodeling, resistance, and therapeutic reprogramming.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6 inhibitors) combined with endocrine therapy have become a therapeutic backbone for hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, yet durable disease control is frequently limited by intrinsic and acquired resistance. Canonical tumor-cell mechanisms, including retinoblastoma-pathway escape, cyclin E-cyclin-dependent kinase 2 (CDK2) activation, endocrine adaptation, and phosphoinositide 3-kinase (PI3K)-AKT-mechanistic target of rapamycin (mTOR) signaling, explain only part of this failure because they do not fully capture dynamic immune and stromal remodeling. Preclinical and translational studies indicate that early CDK4/6 inhibition can enhance antigen presentation, activate interferon-related programs, restrain regulatory T cells, and promote a T-cell-inflamed state. These effects are conditional and may not persist during prolonged treatment. Sustained therapy can instead drive heterogeneous resistant niches characterized by stromal remodeling, myeloid recruitment, checkpoint adaptation, and T-cell dysfunction. This immune-state dependence provides a rationale for immune checkpoint blockade, although clinical combinations have shown mixed efficacy and clinically relevant hepatic, pulmonary, and hematologic toxicities. Sequential or lead-in strategies therefore warrant prospective evaluation. Oxidative phosphorylation (OXPHOS) and redox adaptation may sustain selected resistant states and expose context-dependent ferroptotic vulnerabilities. Ferroptosis may connect tumor-cell killing with immune regulation, whereas nanomedicine may improve tumor-selective delivery. Both strategies remain largely preclinical and require further evaluation of pharmacokinetics, biodistribution, toxicity, manufacturability, and immune-cell safety. This Review distinguishes intrinsic from acquired resistance across interpatient, intratumoral, spatial, and temporal dimensions. It integrates tumor-cell escape with cytokine, immune, stromal, vascular, and metabolic remodeling and summarizes emerging therapeutic strategies. We further propose a candidate biomarker-informed framework that integrates genomic profiling, spatial immune architecture, circulating biomarkers, T-cell receptor (TCR) dynamics, transcriptomic and single-cell analyses, artificial intelligence (AI)-assisted multimodal integration, and longitudinal sampling. This framework is intended to support biomarker development and prospective trial design rather than current clinical decision-making, providing a translational basis for testing state-informed and sequence-aware therapeutic strategies.

Humans↗

PubMed2026

Single-cell and spatial transcriptomics define a progenitor subpopulation and fibroinflammatory niche at the leading edge of parathyroid carcinoma.

Parathyroid carcinoma (PC) is a rare but clinically aggressive endocrine malignancy with limited treatment options and a poorly defined tumor microenvironment (TME). To elucidate its cellular heterogeneity and spatial architecture, we integrated single-cell and spatial transcriptomic profiling with whole-exome sequencing and multiplex immunohistochemistry on eight parathyroid neoplasm specimens, including PC, parathyroid adenoma, and atypical parathyroid tumor. We identified a distinct progenitor-like endocrine subpopulation (Ca-1) enriched in CDC73-mutant PC, exhibiting stem-like properties, elevated cell cycle activity, and pronounced genomic instability. Spatial mapping revealed that Ca-1 cells preferentially localize at the leading edge, forming a fibroinflammatory niche characterized by the enrichment of inflammatory cancer-associated fibroblasts (iCAFs) and SPP1+ macrophages. Within this niche, the dipeptidyl peptidase 4 (DPP4) is selectively expressed in Ca-1 cells and iCAFs, implicating a potential paracrine axis driving stromal remodeling and immunosuppression. These findings suggest that a spatially organized ecosystem may promote PC progression through TME remodeling and highlight the DPP4-CXCL2 axis as a candidate pathway for future investigation in aggressive parathyroid neoplasms.

Humans↗

PubMed2026

Uncovering Immune Niches in Health and Disease Using Spatial Transcriptomics.

Spatial transcriptomics allows for the investigation of complex cellular ecosystems directly in their native tissues and enables the dissection of immune niches as spatially organized and functionally diverse microenvironments across homeostatic, inflammatory, and malignant settings. In this review, we examine how spatial transcriptomics tools have been applied to interrogate the cellular and molecular architecture of immune niches, including the emerging studies of B and T cell clonal niches. We focus on immune niches in intestinal and tumor tissues due to their importance to both health and pathology, discuss pressing immunological questions these technologies may help to address, and highlight future developments in the field.

Humans↗

PubMed2026

Reframing early gastric carcinogenesis through lineage, niche, and evolution.

Early gastric cancer is still commonly conceptualized as the endpoint of a linear sequence from chronic gastritis to intestinal metaplasia, dysplasia, and invasion. Yet recent single-cell, spatial, genomic, and functional studies indicate that this model incompletely captures the biology of early gastric carcinogenesis. Malignant potential is established progressively within a precancerous gastric field already shaped by somatic evolution, chronic inflammatory injury, and epithelial lineage distortion. Within this field, progression is concentrated in a restricted set of precursor states, particularly incomplete, hybrid, and stem-like metaplastic populations that display plasticity, persistence, and increasing compatibility with a supportive microenvironment. Fibroblast niche remodeling, immune protection loss, endothelial rewiring, genomic instability, epigenetic drift, and selective retention of advantageous molecular alterations further promote malignant commitment. In parallel, diffuse gastric cancer appears to follow a distinct route that may arise independently of conventional intestinal metaplasia through E-cadherin-deficient epithelial transformation and downstream chromatin reprogramming. Here, we synthesize recent evidence to propose an updated framework for early gastric carcinogenesis based on field evolution, lineage instability, ecosystem support, and pathway divergence. Rather than replacing the classical Correa cascade, this framework seeks to refine it by shifting the unit of risk assessment from histologic stage alone to biologically defined precursor states shaped by lineage instability, clonal persistence, niche permissiveness, and pathway-specific molecular constraints. This perspective shifts the emphasis of prevention from detecting smaller cancers to identifying and intercepting biologically committed precursor states before invasion occurs.

Humans↗