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This highlight summarises recent advances in agentic artificial intelligence (AI) systems for spatial omics analysis. These systems are compared along two central tensions: autonomy versus accountability, and adaptability versus reproducibility. We argue that progress will depend not on maximising automation, but on defining where autonomy is appropriate.
Hirschsprung disease (HSCR) is characterized by absence of enteric ganglia (aganglionosis) along variable lengths of the distal intestine. This disorder results from deficient colonization of fetal intestine by enteric neural crest-derived cells (ENCDCs). HSCR exhibits complex, multifactorial inheritance with penetrance and severity varying widely even within families. SOX10 is among causal genes that predispose to aganglionosis. Yet, how gene interactions influence severity of HSCR aganglionosis is not understood. Prior mapping of aganglionosis modifiers was achieved in a standard F1-intercross utilizing the Sox10Dom HSCR mouse model. Here we deploy a novel strategy of genotyping an extended pedigree pedigree of Sox10Dom mice on a mixed genetic background. GWAS in this pedigree points to novel aganglionosis modifier intervals with replication and refinement of prior modifier regions. Complementary omics analysis of the developing Enteric Nervous System (ENS) enabled identification of multiple high-priority candidate genes within these modifier intervals based on gene expression, chromatin accessibility, and presence of conserved SOX10 binding motifs. We implemented a prioritization pipeline for ranking potential modifiers that generated candidate lists including several well-known for effects on ENS development as well as multiple novel genes. Among the novel genes, Dach1 ranked as a top priority candidate gene for modifying migration of ENCDCs and thus influencing aganglionosis severity. The results identify genome intervals with intrinsic genes that are logical candidates for modifying Sox10Dom aganglionosis severity. We also note that several human orthologs to aganglionosis modifier candidate genes are within linkage disequilibrium blocks containing genetic variants associated with human gut motility disorders, which offers opportunity for gaining biological insight into human HSCR severity.
MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.
Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.
Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.
Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.
Pituitary neuroendocrine tumors are frequently invasive, with cavernous sinus invasion leading to poor treatment outcomes and high recurrence. Regional differences within these tumors remain poorly understood, hindering targeted therapy development. Here, we present the first integrative multi-omics analysis combining proteomics, metabolomics and single-cell transcriptomics to characterize tumors from the cavernous sinus and saddle regions. Our results reveal profound regional and cellular heterogeneity: cavernous sinus tumors exhibit significantly enhanced cell proliferation, driven by cancer-associated fibroblasts through the IGF1-IGF1R-MAPK1 axis. Cancer-associated fibroblasts in the cavernous sinus secrete IGF1 under regulation of the transcription factor FOXO1, which binds to receptors on tumor cells to activate proliferation. Metabolomic profiling identifies proline as a key enriched metabolite that stimulates cancer-associated fibroblasts to produce collagen fibers, reinforcing a pro-tumorigenic microenvironment. Single-cell transcriptomics further delineates a distinct subpopulation of receptor-positive malignant cells and a high abundance of cancer-associated fibroblasts in the cavernous sinus. These findings establish core mechanisms underlying the aggressive behavior of cavernous sinus-invading tumors, providing novel actionable targets for precision therapeutic strategies tailored to distinct tumor regions.
Cannabis sativa L. exhibits pronounced sexual plasticity in which both XX and XY plants can undergo floral phenotypic sex reversal in response to ethylene modulation, yet the underlying molecular mechanisms remain poorly defined. Here, we present the most extensive multi-omic analysis of ethylene-induced sex change in C. sativa to date, integrating over 130 RNA-seq libraries, ethylene pathway metabolite quantification, and whole-genome sequencing across three XX and XY genotypes. Treatments with silver thiosulfate and ethephon induced more than 80% phenotypic conversion, but transcriptomic responses diverged sharply between XX and XY plants. Profiling 47 ERGs revealed 14 high-confidence candidates, including CsACS1, CsACO5, CsERF1, and CsMTN, with sex-specific and temporal expression patterns that show dynamic ethylene mediation of plasticity. Early transcriptional activation occurred prior to the emergence of flowers, within 18 h of sex-change treatments and the photoperiod-induced transition to flowering. As opposite-sex floral tissues emerged, ethylene-related gene expression shifted accordingly within developing floral organs, with distinct sets of genes stabilizing the opposite-sex phenotype in XX and XY plants. Several candidates were located in non-recombining regions of the X chromosome or were absent from the Y chromosome, and most exhibited low nucleotide diversity, consistent with functional constraint. These results provide a high-resolution view of ethylene-responsive sexual plasticity in cannabis and show that the shared capacity for sex reversal in XX and XY plants is implemented through distinct regulatory trajectories that produce opposite-sex floral phenotypes. This work expands the mechanistic understanding of sex expression in dioecious species and identifies candidate genes relevant to the development of sex-stable cultivars.
The Global pandemic of coronavirus disease 2019 was initiated by the emergence of severe acute respiratory syndrome coronavirus 2. In addition to conventional pulmonary lesions, a range of neurological injury symptoms have been identified in clinical practice, but the aetiology of neurological disorders linked to SARS-CoV-2 infection remains poorly understood. Syrian hamsters, which are highly susceptible to SARS-CoV-2 infection, exhibit a disease phenotype similar to that observed in human COVID-19 patients. In this study, a hamster model of COVID-19 infection was used to analyze molecular changes in different tissues at various time points post infection with distinct strains using proteomic and phosphoproteomic approaches. Multi-omics analysis showed that SARS-COV-2 infection triggers sustained downregulation of the abundance and phosphorylation levels of neuronal and synapse-associated proteins in the brain, suggesting that neuronal damage persists even during the recovery period. Additionally, infections with SARS-CoV-2 may contribute to the onset of long-term symptoms of COVID-19 by impacting energy metabolism, neurotransmitter release, and synaptic transmission pathways. This study provides a comprehensive molecular profile of hamsters infected with different SARS-CoV-2 strains in different tissues, offering foundational insights into the pathogenic mechanisms of COVID-19.
BACKGROUND: Ovarian cancer (OC) remains the deadliest gynecologic malignancy, largely due to its immunosuppressive tumor microenvironment (TME) and resistance to therapy. Necroptosis, a regulated lytic cell death pathway mediated by the RIPK1-RIPK3-MLKL axis, can trigger immunogenic cell death, but its specific role in shaping the OC immune landscape and its clinical translation potential are posorly understood. METHODS: We employed multi-omics analysis (transcriptomics, genomics, clinical data) from TCGA-OV (n = 380), ICGC OV-AU, and IMvigor210 cohorts, combined with rigorous in vitro functional validation using OC cell lines (SKOV3, HEY), macrophages (THP-1 derived), and T cells (Jurkat). Computational immunology approaches (ESTIMATE, CIBERSORT, ssGSEA) quantified immune infiltration. We identified MLKL-associated immune genes, performed survival analysis (Kaplan-Meier, Cox regression), and constructed a necroptosis-immune signature (NecropImmScore) using consensus clustering and PCA of 102 prognostic genes. Drug sensitivity was predicted via pRRophetic and CellMiner. RESULTS: MLKL emerged as a protective prognostic biomarker (p = 0.018), significantly correlated with enhanced immune infiltration (ImmuneScore, StromalScore, ESTIMATEScore; p < 2.22e-16), M1 macrophage polarization (p = 0.006), activated CD4 + T cells (p = 0.003), and elevated immune checkpoint expression (PD-L1, CTLA4, LAG3, TIGIT). In vitro, MLKL overexpression in OC cells promoted M1 polarization (p < 0.05), activated Jurkat T cells (upregulated CCR4/5/7/9, CD69, CD3D/E, GZMB; p < 0.05), and induced key chemokines (CXCL9/10/11/13) critical for immune cell recruitment. Integration of MLKL-related and immune-related DEGs (n = 632) revealed enrichment in T-cell activation, chemokine signaling, and antigen presentation pathways (FDR < 0.05). Consensus clustering based on 102 survival-associated genes defined three molecular subtypes (Clusters A-C) with divergent survival (p = 0.019), necroptosis activity, and immune infiltration (Cluster C: best prognosis, highest MLKL/ImmuneScore). The derived NecropImmScore robustly stratified patients: high-score correlated with superior overall survival (TCGA: p < 0.001; ICGC: p = 0.014), inflamed TME phenotype, elevated checkpoint expression, and improved response to anti-PD-L1 in IMvigor210. Critically, high NecropImmScore predicted higher BRCA1 mutation frequency (AUC = 0.802), synergy with BRCA1 status for prognosis, higher homologous recombination deficiency (HRD) score, sensitivity to cisplatin (p = 0.014), paclitaxel (p = 0.016), gemcitabine (p = 0.017), and provided superior prognostic stratification when combined with TMB and HRD score (p < 0.001). CONCLUSION: This study establishes MLKL as a master regulator of anti-tumor immunity in OC, driving chemokine-mediated immune cell recruitment and TME reprogramming. The novel NecropImmScore is a multifaceted biomarker that effectively predicts prognosis, immunotherapy response, BRCA1 deficiency, and chemosensitivity, offering significant potential for guiding precision therapeutic strategies in OC.
BACKGROUND: Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, and the prognosis of advanced GC remains poor. Systematic identification of robust biomarkers through multi-cohort integration and computational prioritization may facilitate the discovery of novel therapeutic targets. AIM: To identify key genes associated with gastric cancer progression through integrative multi-omics analysis and to elucidate the biological functions and molecular mechanisms of the top-prioritized candidate gene. METHODS: Comprehensive bioinformatics analyses integrating The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets were performed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), Cox regression, and eight machine-learning algorithms to systematically identify and prioritize GC-associated hub genes. Among the identified candidates, CPVL was selected for further validation based on its diagnostic and prognostic performance. CPVL expression and clinical relevance were validated by independent datasets and immunohistochemistry. Lentiviral constructs were used to overexpress or silence CPVL in GC cell lines. Functional assays were performed, including CCK-8, colony formation, EdU incorporation, and flow cytometry, to assess cell proliferation and cell-cycle distribution. Western blotting and JAK2 inhibitor (AZD1480) rescue experiments were performed to elucidate the underlying mechanisms, and a nude mouse xenograft model was used to evaluate tumorigenicity in vivo. RESULTS: Multi-cohort screening identified five hub genes (CPVL, AADAC, BCAT1, CPXM1, and FBN1). Among them, CPVL exhibited the highest diagnostic accuracy (AUC = 0.895) and the strongest correlation with poor overall survival, and was therefore selected for mechanistic investigation. CPVL expression was markedly upregulated in GC tissues and cell lines. Functional assays demonstrated that CPVL promotes GC cell proliferation and accelerates G1/S-phase transition. Mechanistically, CPVL activated the JAK2/STAT3 signaling pathway, upregulating Cyclin D1 and CDK4 while downregulating p27. Treatment with the JAK2 inhibitor AZD1480 partially reversed these effects. In vivo, CPVL knockdown significantly inhibited tumor growth. CONCLUSION: Through systematic multi-cohort integration and machine-learning prioritization, CPVL was identified as a novel oncogenic driver in gastric cancer. CPVL promotes tumor growth via activation of the JAK2/STAT3 pathway and regulation of the Cyclin D1/CDK4/p27 axis, highlighting its potential as a diagnostic biomarker and therapeutic target.
BACKGROUND: Opioid receptor-ligand signalling has been implicated in tumour biology and perioperative outcomes; however, its pan-cancer molecular landscape and clinical relevance remain incompletely defined. METHODS: We performed a pan-cancer multi-omics analysis of eight predefined opioid receptor-ligand genes across 33 tumour types from The Cancer Genome Atlas. Analyses included gene expression analysis using the linear models for microarray data (limma) package, genomic alterations, DNA methylation, regulatory network inference, pathway activity estimation using gene set variation analysis, and survival modelling. Multivariable Cox regression models were adjusted for age, sex, and tumour stage. RESULTS: Opioid receptor-ligand genes exhibited heterogeneous and generally low-to-moderate expression across tumour types. Genomic and epigenetic alterations were tumour-specific and variably associated with gene expression. Selected genes showed associations with overall survival in a tumour-dependent manner; however, these associations were attenuated after adjustment for clinical covariates and were accompanied by wide confidence intervals in some cohorts. Pathway analyses suggested associations with broader biological programmes, including epithelial-mesenchymal transition and immune-related pathways. Regulatory analyses identified candidate transcription factors and miRNAs, although these findings are exploratory. CONCLUSIONS: This pan-cancer analysis provides a systematic overview of opioid receptor-ligand gene features across human cancers. The observed associations are context-dependent and should be interpreted as hypothesis-generating. Further mechanistic and prospective studies are required to determine the clinical relevance of opioid signalling in cancer and perioperative settings.
Gemcitabine remains a cornerstone treatment for pancreatic ductal adenocarcinoma (PDAC), yet the emergence of resistance constitutes a major clinical challenge with poorly understood epigenomic mechanisms. Here, we identified the pioneer transcription factor Foxa1 as a master regulator of gemcitabine resistance through multi-omics analysis. Mechanistically, Foxa1 drives widespread super-enhancer (SE) reprogramming and 3D genome remodelling in resistant cells, which coordinately activates the expression of key resistance genes, notably Rrm1 and Cdadc1. This is accompanied by increased chromatin accessibility, elevated H3K27ac enrichment at SEs, and enhanced Foxa1 binding at regulatory elements. Moreover, post-translational stabilization of Foxa1 via USP7-mediated deubiquitination sustains this epigenomic program. Genetic ablation of Foxa1 or specific SE regions near Rrm1 resensitizes resistant cells to gemcitabine. Building upon this mechanism, we demonstrate that bromodomain and extraterminal (BET) inhibitors, which disrupt SE function, potently reverse resistance. Notably, the clinical-stage BET inhibitor AZD5153, in combination with gemcitabine, achieves robust tumor suppression and overcomes resistance in cell-derived xenograft (CDX) models by dismantling the Foxa1-mediated resistant transcriptome and reinvigorating drug sensitivity. Our findings establish Foxa1-orchestrated enhancer reprogramming as a fundamental mechanism of gemcitabine resistance and unveil a promising epigenetic therapy to restore treatment efficacy in PDAC.
Cephalotaxine-type and homoerythrina-type alkaloids are structurally unique and biologically important natural products isolated from endangered species that belong to the genus Cephalotaxus. Among them, homoharringtonine (HHT [1]) is a marketed drug used to treat leukemia. However, the scalable production of HHT is significantly hindered by limited natural resources. Despite intensive investigation over half a century, the complete biosynthetic pathways of these alkaloids remain unknown. Here, we applied a comprehensive multi-omics analysis and used a set of chemically synthesized standard compounds to identify the missing enzymes required for the biosynthesis of cephalotaxinone and homoerythratine. We also uncovered a rare case of divergent oxidation catalyzed by two highly homologous cytochrome P450 enzymes, CfCYP2 and CfCYP3, in the biosynthesis of two structurally distinct alkaloids. We further identified the key residues that significantly affect the divergent oxidation outcomes and ultimately reconstituted the complete biosynthetic pathways for producing these two alkaloids in N. benthamiana.
BACKGROUND: In EGFR-mutated lung adenocarcinoma (EGFRm LUAD), EGFR mutations do not necessarily result in increased EGFR expression (EGFR-exp), which differs among patients. However, the factors influencing EGFR-exp and the impact of EGFR-exp on tumor characteristics in patients with EGFRm LUAD remain unclear. PATIENTS AND METHODS: Whole-exome and RNA sequencing were performed for patients with early- and advanced-stage EGFRm LUAD. The patients were classified into low or high EGFR-exp groups based on the median transcripts per million. We retrospectively examined the association between EGFR-exp, genomic characteristics, downstream EGFR signaling activity, tumor microenvironment (TME) status, and clinical outcomes. RESULTS: This study included 450 and 45 patients in the early- and advanced-stage cohorts, respectively. In both cohorts, the EGFR-exp low group exhibited a lower incidence of TP53 co-mutations and EGFR amplification and a higher incidence of EGFR subclonal mutations than the EGFR-exp high group. Furthermore, downstream EGFR signaling pathways, such as the MAPK signaling, were less activated in the EGFR-exp low group. However, this group showed significantly enriched adaptive immune response pathways (Q < 0.0001) and an immune-inflamed TME. Additionally, a low EGFR-exp was a significantly favorable factor for postoperative relapse (odds ratio [OR], 0.6; P = 0.04). However, in the advanced-stage cohort, a low EGFR-exp was a significant risk factor for non-responders to osimertinib (OR, 17.5; P = 0.03). CONCLUSIONS: In EGFRm LUAD, significant associations were observed between EGFR-exp levels and both EGFR signaling pathways and adaptive immune status, which in turn influence clinical outcomes. This large-scale multi-omics analysis highlights the heterogeneity among patients with EGFRm LUAD and emphasizes the need to assess EGFR-exp levels alongside mutation status for optimal treatment strategies in EGFRm LUAD.
The intratumoral microbiota is increasingly recognized as an active component of the tumor microenvironment, yet whether it directly drives tumor mutagenesis remains unclear. Here, integrated multi-omics analysis of human non-small cell lung cancer (NSCLC) identifies Mycobacterium abscessus as a microbial determinant of APOBEC3A-associated mutagenesis. Mechanistically, the bacterial effector nucleoside diphosphate kinase (NDK) directly targets the host transcription factor IRF3 and installs a non-canonical 1-phosphohistidine modification at H263, thereby amplifying type I interferon signaling and sustaining APOBEC3A expression. This inter-kingdom phosphotransfer event links intratumoral microbial colonization to an endogenous mutational process that promotes genomic diversification. Genetic inactivation of NDK, or pharmacologic elimination using an engineered NDK-PROTAC, suppresses APOBEC3A activation and attenuates microbe driven mutagenesis. Together, these findings establish a direct microbial effector mechanism that promotes APOBEC3A-associated mutagenesis and provide a therapeutic framework to intercept microbiome driven mutagenesis in NSCLC.
Acute pancreatitis (AP) is a serious inflammatory disease with significant morbidity, yet its underlying molecular mechanisms remain incompletely understood. This study reveals a novel epitranscriptomic pathway in AP pathogenesis centered on METTL1-mediated N7-methylguanosine (m7G) RNA modification. We found that METTL1 expression and global m7G levels were significantly elevated in serum from AP patients, pancreatic tissues of sodium taurocholate-induced AP mice, and in vitro models of LPS-polarized macrophages and STC-injured pancreatic acinar cells. Through integrated multi-omics analysis combining m7G methylome mapping and transcriptome profiling, we identified Musculin (MSC) as a key target whose mRNA stability is enhanced by METTL1-mediated m7G modification. Functional experiments demonstrated that MSC upregulation activates TNF signaling through phosphorylation of NF-κB, JNK, and MAPK proteins, thereby promoting macrophage M1 polarization and pancreatic acinar cell injury. The pathological significance of this pathway was confirmed in vivo, where pancreas-targeted knockdown of Mettl1 significantly attenuated AP severity. Furthermore, mechanistic studies using a catalytic-dead METTL1 mutant established that both the methyltransferase activity of METTL1 and subsequent TNF signaling activation are essential for driving inflammatory responses. Our findings delineate a previously unrecognized METTL1-m7G-MSC-TNF signaling axis that promotes AP progression, highlighting the therapeutic potential of targeting METTL1-mediated epitranscriptomic modification in inflammatory diseases.
Pediatric adrenocortical tumors are rare, clinically heterogeneous neoplasms with unpredictable outcomes and limited treatment options. Through integrated multi-omic analysis of 214 pediatric adrenocortical tumors combining DNA methylation profiling, transcriptomics, chromatin accessibility, and spatial deconvolution, we identify four distinct risk groups. A high-risk subgroup is characterized by CpG island hypermethylation, chromosomal instability, and dismal survival. These tumors exhibit transcriptional co-activation of WNT signalling and activator protein-1 transcriptional programs and display balanced admixture of zona glomerulosa and zona fasciculata/reticularis-like cells. Spatial analysis reveals zona glomerulosa cells as WNT signaling hubs driving intercellular crosstalk. Mechanistically, the histone deacetylase inhibitor entinostat reverses promoter methylation, silences activator protein-1 activity, and induces apoptotic reprogramming in tumor models. These findings establish a molecular framework for risk stratification and identify actionable therapeutic vulnerabilities, providing an essential resource for studying this molecularly uncharted pediatric malignancy.