Sci Rep. 2025 May 22;15(1):17735. doi: 10.1038/s41598-025-02444-7.

ABSTRACT

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson's correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model's input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein's contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs' actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs.

PMID:40404689 | DOI:10.1038/s41598-025-02444-7

Nat Commun. 2025 May 23;16(1):4786. doi: 10.1038/s41467-025-60064-1.

ABSTRACT

High-throughput massively parallel reporter assays (MPRAs) and phenotype-rich in vivo transgenic mouse assays are two potentially complementary ways to study the impact of noncoding variants associated with psychiatric diseases. Here, we investigate the utility of combining these assays. Specifically, we carry out an MPRA in induced human neurons on over 50,000 sequences derived from fetal neuronal ATAC-seq datasets and enhancers validated in mouse assays. We also test the impact of over 20,000 variants, including synthetic mutations and 167 common variants associated with psychiatric disorders. We find a strong and specific correlation between MPRA and mouse neuronal enhancer activity. Four out of five tested variants with significant MPRA effects affected neuronal enhancer activity in mouse embryos. Mouse assays also reveal pleiotropic variant effects that could not be observed in MPRA. Our work provides a catalog of functional neuronal enhancers and variant effects and highlights the effectiveness of combining MPRAs and mouse transgenic assays.

PMID:40404660 | DOI:10.1038/s41467-025-60064-1

Nat Commun. 2025 May 23;16(1):4785. doi: 10.1038/s41467-025-60154-0.

ABSTRACT

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

PMID:40404656 | DOI:10.1038/s41467-025-60154-0

Nat Commun. 2025 May 22;16(1):4771. doi: 10.1038/s41467-025-59779-y.

ABSTRACT

Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.

PMID:40404650 | DOI:10.1038/s41467-025-59779-y

Sci Data. 2025 May 22;12(1):842. doi: 10.1038/s41597-025-05114-5.

ABSTRACT

The endangered mountain gorilla, Gorilla beringei beringei, faces numerous threats to its survival, highlighting the urgent need for genomic resources to aid conservation efforts. Here, we present a near telomere-to-telomere, haplotype-phased reference genome assembly for a male mountain gorilla generated using PacBio HiFi (26.77× ave. coverage) and Oxford Nanopore Technologies (52.87× ave. coverage) data. The resulting non-scaffolded assembly exhibits exceptional contiguity, with contig N50 of ~95 Mbp for the combined pseudohaplotype (3,540,458,497 bp), 56.5 Mbp (3.1 Gbp) and 51.0 Mbp (3.2 Gbp) for each haplotype, an average QV of 65.15 (error rate = 3.1 × 10-7), and a BUSCO score of 98.4%. These represent substantial improvements over most other available primate genomes. This first high-quality reference genome of the mountain gorilla provides an invaluable resource for future studies on gorilla evolution, adaptation, and conservation, ultimately contributing to the long-term survival of this iconic species.

PMID:40404646 | DOI:10.1038/s41597-025-05114-5

Nat Commun. 2025 May 22;16(1):4759. doi: 10.1038/s41467-025-59839-3.

ABSTRACT

In spatial'omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry imaging (MSI)-based spatial 'omics must compromise between data acquisition speed and biochemical profiling depth. Here, we introduce fast, label-free quantum cascade laser mid-infrared imaging microscopy (QCL-MIR imaging) to guide MSI to high-interest tissue regions as small as kidney glomeruli, cultured multicellular spheroid cores or single motor neurons. Focusing on smaller tissue areas enables extensive spatial lipid identifications by on-tissue tandem-MS employing imaging parallel reaction monitoring-Parallel Accumulation-Serial Fragmentation (iprm-PASEF). QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. Taken together, widely applicable and fast QCL-MIR imaging-based guidance of MSI ensures that more time is available for exploration and validation of new biology by default on-tissue tandem-MS analysis.

PMID:40404613 | DOI:10.1038/s41467-025-59839-3

Food Chem. 2025 May 21:144629. doi: 10.1016/j.foodchem.2025.144629. Online ahead of print.

NO ABSTRACT

PMID:40404507 | DOI:10.1016/j.foodchem.2025.144629

Eur Respir J. 2025 May 22;65(5):2500092. doi: 10.1183/13993003.00092-2025. Print 2025 May.

NO ABSTRACT

PMID:40404195 | DOI:10.1183/13993003.00092-2025

New Phytol. 2025 May 22. doi: 10.1111/nph.70182. Online ahead of print.

ABSTRACT

Evolutionarily conserved switch-defective/sucrose nonfermentable (SWI/SNF) ATP-dependent chromatin remodelling complexes (CRCs) alter nucleosome positioning and chromatin states, affecting gene expression to regulate important processes such as proper development and hormonal signalling pathways. We employed transcript profiling, chromatin immunoprecipitation (ChIP), mass spectrometry, yeast two-hybrid and bimolecular fluorescence complementation protein-protein interaction studies, along with hormone and metabolite profiling and phenotype assessments, to distinguish the SWP73A and SWP73B subunit functions in Arabidopsis. We identified a novel subclass of SWI/SNF CRCs defined by the presence of the SWP73A subunit. Therefore, we propose a refined classification of SWI/SNF CRCs in Arabidopsis, introducing BRM-associated SWI/SNF (BAS)-A (containing SWP73A) and BAS-B (containing SWP73B) subclasses. The SWP73A- and SWP73B-carrying SWI/SNF CRCs exhibit differential properties, demonstrated by distinct chromatin binding patterns and divergent effects on hormone biosynthesis and metabolism. We additionally found that SWP73A plays a specific role in the regulation of auxin signalling, root development, metabolism and germination that cannot be fully compensated by SWP73B. We recognised that some atypical subclasses of SWI/SNF CRCs may be likely formed in mutant lines with inactivated SWP73 subunits. Our study reveals that the duplication of the SWP73 subunit genes contributes to unique and shared functions of SWI/SNF CRC subclasses in the regulation of various processes in Arabidopsis.

PMID:40404167 | DOI:10.1111/nph.70182

Cell Genom. 2025 May 19:100879. doi: 10.1016/j.xgen.2025.100879. Online ahead of print.

ABSTRACT

Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics" to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex.

PMID:40403730 | DOI:10.1016/j.xgen.2025.100879

Mol Cell. 2025 May 14:S1097-2765(25)00405-8. doi: 10.1016/j.molcel.2025.04.026. Online ahead of print.

ABSTRACT

Diffuse midline glioma (DMG) is a fatal childhood brain tumor characterized primarily by mutant histone H3 (H3K27M). H3K27M causes a global reduction in Polycomb repressive complex 2 (PRC2)-mediated H3K27 trimethylation (H3K27me3). Paradoxically, PRC2 is essential in DMG cells, although the downstream molecular mechanisms are poorly understood. Here, we have discovered a specific form of canonical PRC1 (cPRC1) containing CBX4 and PCGF4 that drives oncogenic gene repression downstream of H3K27me3 in DMG cells. Via a novel functional region, CBX4 preferentially associates with PCGF4-containing cPRC1. The characteristic H3K27me3 landscape in DMG rewires the distribution of cPRC1 complexes, with CBX4/PCGF4-cPRC1 accumulating at H3K27me3-enriched CpG islands. Despite comprising <5% of cPRC1 in DMG cells, the unique repressive functions of CBX4/PCGF4-cPRC1 are essential for DMG growth. Our findings link the altered distribution of H3K27me3 to imbalanced cPRC1 function, which drives oncogenic gene repression in DMG, highlighting potential therapeutic opportunities for this incurable childhood brain cancer.

PMID:40403727 | DOI:10.1016/j.molcel.2025.04.026

Neuron. 2025 May 21;113(10):1525-1547.e15. doi: 10.1016/j.neuron.2025.05.002.

ABSTRACT

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate specific neuronal subtypes are still limited. Here, we performed systematic analysis of single-cell genomic data to identify enhancer candidates for each of the telencephalic interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic interneurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP), and manipulate (opto-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy.

PMID:40403705 | DOI:10.1016/j.neuron.2025.05.002

Plant Physiol Biochem. 2025 May 17;225:110043. doi: 10.1016/j.plaphy.2025.110043. Online ahead of print.

ABSTRACT

A growing number of weed biotypes showing resistance to acetolactate synthase (ALS)-inhibitors have been reported in several species, notably including Sinapis arvensis L. Two putative resistant (R) populations of S. arvensis from Tunisia were subjected to greenhouse and laboratory investigations to validate resistance to ALS-inhibitors and to determinate the mechanisms involved. The results indicated that both populations were resistant to four distinct ALS-inhibiting herbicides, tribenuron-methyl (TM), florasulam, flucarbazone and imazamox (IMZ), thereby confirming cross-resistance between them. The dose of (TM) required to achieve a 50 % reduction in plant growth (ED50) and 50 % mortality (LC50) in R populations of S. arvensis was found to be at least 60 times greater than the recommended field dose (18.7 g ai ha-1) applied in cereal crops in Tunisia, indicating a significantly elevated resistance factor. Synergist experiments using malathion as a cytochrome P450 (Cyt-P450) inhibitor demonstrated a reduction in resistance to imazamox (IMZ) in both resistant (R) biotypes, indicating that Cyt-P450 plays a partial role in the resistance mechanism. In addition, ALS gene analysis identified three key point mutations, Pro197Ala, Asp376Glu and Trp574Leu, in both R populations. The docking analysis demonstrated that Asp376Glu mutation in S. arvensis could confer cross-resistance to IMZ and TM herbicides. CAPS and dCAPS methods were developed for detecting the Trp574Leu and Asp376Glu mutations, respectively, in S arvensis and it was shown that work efficiently. Fortunately, the study also confirmed that 2,4-D still effectively controlled S. arvensis populations. This study provides valuable insights into the mechanisms underlying herbicide resistance in S.arvensis populations from Tunisia, demonstrating that both target-site resistance (TSR) and non-target-site resistance (NTSR) contribute to the species' broad-spectrum resistance against four dissimilar ALS-inhibitors.

PMID:40403623 | DOI:10.1016/j.plaphy.2025.110043

Biomaterials. 2025 May 19;323:123422. doi: 10.1016/j.biomaterials.2025.123422. Online ahead of print.

ABSTRACT

Spinal cord injury (SCI) remains a formidable clinical challenge with limited therapeutic options. Recent advances in nanotechnology have introduced paradigm-shifting strategies that transcend the limitations of traditional treatments by offering precision, controllability, and multifunctionality in modulating the hostile post-injury microenvironment. This review systematically summarizes nanotechnology-based therapeutic approaches for SCI, including cell-based nanotherapeutics, nanogels/hydrogels, nano-engineered materials, and combinatorial strategies. We emphasize the synergistic design of multifunctional nanoplatforms that integrate neuroprotection, immune modulation, antioxidative capacity, and axonal regeneration within a single system. Special attention is given to microenvironment-responsive smart materials capable of dynamic therapeutic delivery in response to pathological cues. We critically analyze the challenges of clinical translation, such as the need for standardized safety evaluation and personalized therapeutic dosing, and explore emerging solutions including AI-driven nanocarrier design and organoid-based validation. By integrating interdisciplinary innovations, nanotherapies represent an irreplaceable therapeutic paradigm with the potential to achieve spatiotemporal precision and sustained regenerative support for SCI repair.

PMID:40403446 | DOI:10.1016/j.biomaterials.2025.123422

Comput Biol Chem. 2025 May 15;119:108505. doi: 10.1016/j.compbiolchem.2025.108505. Online ahead of print.

ABSTRACT

It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign yeast form to an invasive hyphal form which leads to systemic candidiasis with high mortality rate. However, the crucial players contributing to the increased susceptibility of immune-deficient individuals to Candida infection remain obscure. To uncover the molecular differences between these conditions, blood-associated proteins from the NDEx database and differentially expressed genes from GEO datasets of immunocompetent and immune-deficient individuals infected with C. albicans were analysed. We focused on deregulated proteins exhibiting inverse expression patterns i.e. upregulated in one group and downregulated in the other and identified 539 proteins. Mapping them onto protein-protein interaction network reconstructed with blood- associated proteins, revealed that they exhibit in 45 hubs, 31 network nodes forming 29 intermodular complexes, and 69 clustered into 11 immunologically relevant MCODE modules. Amongst them 13 key host molecules emerging as key player based on their network topological properties. Furthermore, a machine learning model was developed with a precision of 85 %, recall of 92 %, F1-score of 89 %, and accuracy of 81 % which substantiates the robust association of 11 out of 13 proteins with fungal co-infections in immune-deficient individuals. These findings underscore key host proteins maintaining immune balance in healthy individuals while their disruption in immune-deficient conditions may weaken defense mechanisms and promote fungal infections. Identification of crucial proteins promoting T-reg cells proliferation and M2 macrophage polarization in immune-deficient conditions offers promising therapeutic targets following experimental validation.

PMID:40403354 | DOI:10.1016/j.compbiolchem.2025.108505

Plant Cell. 2025 May 22:koaf124. doi: 10.1093/plcell/koaf124. Online ahead of print.

ABSTRACT

Plant roots dynamically respond to nitrogen availability by executing a signaling and transcriptional cascade resulting in altered plant growth that is optimized for nutrient uptake. The NIN-LIKE PROTEIN 7 (NLP7) transcription factor senses nitrogen and, along with its paralog NLP6, partially coordinates transcriptional responses. While the post-translational regulation of NLP6 and NLP7 is well established, their upstream transcriptional regulation remains understudied in Arabidopsis (Arabidopsis thaliana) and other plant species. Here, we dissected a known sub-circuit upstream of NLP6 and NLP7 in Arabidopsis, which was predicted to contain multiple multi-node feedforward loops suggestive of an optimized design principle of nitrogen transcriptional regulation. This sub-circuit comprises AUXIN RESPONSE FACTOR 18 (ARF18), ARF9, DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN 26 (DREB26), Arabidopsis NAC-DOMAIN CONTAINING PROTEIN 32 (ANAC032), NLP6 and NLP7 and their regulation of NITRITE REDUCTASE 1 (NIR1). Conservation and divergence of this circuit and its influence on nitrogen-dependent root system architecture were similarly assessed in tomato (Solanum lycopersicum). The specific binding sites of these factors within their respective promoters and their putative cis-regulatory architectures were identified. The direct or indirect nature of these interactions was validated in planta. The resulting models were genetically validated in varying concentrations of available nitrate by measuring the transcriptional output of the network revealing rewiring of nitrogen regulation across distinct plant lineages.

PMID:40403157 | DOI:10.1093/plcell/koaf124

Circ Res. 2025 May 23;136(11):1335-1362. doi: 10.1161/CIRCRESAHA.125.325492. Epub 2025 May 22.

ABSTRACT

The liver and heart are closely interconnected organs, and their bidirectional interaction plays a central role in cardiometabolic disease. In this review, we summarize current evidence linking liver dysfunction-particularly metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, and cirrhosis-with an increased risk of heart failure and other cardiovascular diseases. We discuss how these liver conditions contribute to cardiac remodeling, systemic inflammation, and hemodynamic stress and how cardiac dysfunction in turn impairs liver perfusion and promotes hepatic injury. Particular attention is given to the molecular mediators of liver-heart communication, including hepatokines and cardiokines, as well as the emerging role of advanced research methodologies, including omics integration, proximity labeling, and organ-on-chip platforms, that are redefining our understanding of interorgan cross talk. By integrating mechanistic insights with translational tools, this review aims to support the development of multiorgan therapeutic strategies for cardiometabolic disease.

PMID:40403112 | DOI:10.1161/CIRCRESAHA.125.325492

Database (Oxford). 2025 Mar 21;2025:baaf020. doi: 10.1093/database/baaf020.

ABSTRACT

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

PMID:40402802 | DOI:10.1093/database/baaf020

Database (Oxford). 2025 Jan 22;2025:baae133. doi: 10.1093/database/baae133.

ABSTRACT

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of "apply patch" and "diff" commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders-e.g. ontology edits can be specified by commands like "add synonym 'arm' to 'forelimb'" or "move 'Parkinson disease' under 'neurodegenerative disease'." We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs. Database URL: https://github.com/INCATools/kgcl.

PMID:40402778 | DOI:10.1093/database/baae133

Blood Adv. 2025 May 22:bloodadvances.2025016002. doi: 10.1182/bloodadvances.2025016002. Online ahead of print.

ABSTRACT

Primary renal manifestations of diffuse large B-cell lymphoma (prDLBCL) represent an exceptionally rare variant of the most common type of non-Hodgkin lymphoma (NHL). Insights into prDLBCL pathogenesis have been limited to small case series and methodologically limited approaches. To address this gap, we conducted the largest comprehensive molecular study of prDLBCL to date, analyzing 30 cases using whole exome sequencing, RNA sequencing, and somatic copy number alteration profiling. The mechanisms driving lymphomagenesis within an organ lacking an intrinsic lymphatic niche and its proclivity for dissemination to immune-privileged sites, including testes and the central nervous system, remain poorly understood. Our findings reveal significant molecular similarities to primary large B-cell lymphomas of immune-privileged sites (IP-LBCL), including a high frequency of immune-escape mechanisms, particularly through deleterious MHC class I and II aberrations and loss of CDKN2A. Despite significant mutational heterogeneity with a broad distribution among molecular clusters, transcriptional deregulation of interferon signaling and MYC target pathways emerged as key hallmarks of prDLBCL pathogenesis. Our comprehensive analysis of prDLBCL biology significantly advances the molecular understanding of this rare variant. These insights not only highlight shared pathogenetic pathways with IP-LBCL but also uncover unique features of prDLBCL, offering potential biomarkers for diagnostic refinement and therapeutic targeting. These findings have profound implications for the future development of diagnostic algorithms and risk-adapted therapeutic approaches, potentially improving the clinical management of this rare and challenging lymphoma subtype.

PMID:40402672 | DOI:10.1182/bloodadvances.2025016002