Genome Medicine最新文献

{"title":"Characterizing heterogeneous cis-regulatory elements in gene regulatory programs associated with breast cancer.","authors":"Chisa Hori, Kohei Kumegawa, Sumito Saeki, Yoko Takahashi, Liying Yang, Tomoyoshi Nakadai, Kazutaka Otsuji, Chikako Takahata, Yukinori Ozaki, Natsue Uehiro, Yurie Haruyama, Tomo Osako, Toshimi Takano, Seiichi Mori, Tetsuo Noda, Satoshi Fujii, Shinji Ohno, Takayuki Ueno, Reo Maruyama","doi":"10.1186/s13073-025-01562-1","DOIUrl":"10.1186/s13073-025-01562-1","url":null,"abstract":"<p><strong>Background: </strong>Cis-regulatory elements (CREs) control oncogene expression and malignant phenotypes. The high clinicopathological heterogeneity of cancer cannot be explained by gene expression alone, being attributed to CRE heterogeneity. However, characterizing cancer-associated CREs is challenging. To address this issue, we performed a single-cell epigenomic analysis of clinical specimens.</p><p><strong>Methods: </strong>To map the multicellular ecosystem of breast cancer (BC) and identify candidate CREs (cCREs), we performed a single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) of 38 prospectively collected BC samples with various clinicopathological characteristics.</p><p><strong>Results: </strong>First, we performed single-cell chromatin accessibility profiling of a high-quality set of 22,775 cells from BC samples. Cells were annotated using marker gene accessibility and integration with existing single-cell RNA sequencing data. The chromatin accessibility patterns exhibited by cancer cells were consistent with the clinicopathological features of each tumor. We identified 224,585 cCREs across the BC ecosystem. By identifying cluster-specific differentially accessible cCREs (DA-cCREs) and constructing a putative enhancer-promoter network, we mapped the cis-regulatory landscape for cancer cells and the tumor microenvironment. The accessibility of putative enhancers targeting the same gene differed within or between tumors, highlighting intra- and inter-cis-regulatory tumor heterogeneity.</p><p><strong>Conclusions: </strong>This study provides a valuable resource for future epigenetic research on BC and highlights the diverse regulatory landscape within or among tumor(s), suggesting that cCREs regulate intra- and intertumor heterogeneity.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"145"},"PeriodicalIF":10.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12659144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145632357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cut or bind? Antigen-specific processing mechanisms define CD4⁺ T cell immunodominant epitopes for SARS-CoV-2 S and N proteins.","authors":"Miguel Álvaro-Benito, Esam T Abualrous, Holger Lingel, Stefan Meltendorf, Jakob Holzapfel, Paula de Diego Valera, Jana Sticht, Benno Kuropka, Cecilia Clementi, Frank Kuppler, Monika C Brunner-Weinzierl, Christian Freund","doi":"10.1186/s13073-025-01577-8","DOIUrl":"10.1186/s13073-025-01577-8","url":null,"abstract":"<p><strong>Background: </strong>CD4⁺ T cell responses are key to adaptive immunity, yet the mechanisms underlying peptide selection and immunodominance across MHC class II variants in humans remain poorly defined. Two non-mutually exclusive models - First Bind-then cut (FBtc) and First Cut-then bind (FCtb) - have been proposed to explain immunodominant peptide selection, but experimental evidence in humans is mostly limited to a single allotype (HLA-DRB1*01:01).</p><p><strong>Methods: </strong>To generalize processing mechanisms across DRB1 alleles we developed an integrative strategy combining in silico prediction and a reconstituted antigen processing system. The independent and combined outcome of both approaches was validated on curated SARS-CoV-2 epitope data (IEDB) for responses to the Spike and Nucleocapsid proteins across a panel of 11 DRB1 allotypes, covering over 90% of European Caucasian populations. Potential immunogenic regions identified by the combination of both methods enabled the design of minimalistic peptide pools whose performance was validated via flow cytometry and ELISpot assays in post-Covid19 and pre-pandemic donors. Mechanistic insights for the selection of immunodominant peptides were derived analyzing biophysical parameters and proteolysis of the model antigens.</p><p><strong>Results: </strong>Three prediction tools used showed limited concordance for some allotypes (< 5%), but their combined output for all allotypes considered revealed potential immunogenic hotspots in the model antigens. Complementary, the reconstituted in vitro system identified allotype-dependent and promiscuous peptide candidates. Minimal peptide pools designed from the overlap of both methods featured improved performance to identify IEDB entries and induced robust CD4⁺ T cell activation in post-COVID-19 donors. Mechanistic modeling classified most immunodominant peptides from the Spike protein as arising via FCtb while FBtc predominated for Nucleocapsid. Epitope selection pathways are therefore antigen-dependent defined by proteolytic resistance and solvent accessibility.</p><p><strong>Conclusions: </strong>We establish a scalable, genomics-informed framework for decoding CD4⁺ T cell immunodominance across diverse HLA contexts. Our findings reveal that antigen-intrinsic features govern the preferential processing pathway - FCtb for Spike and FBtc for Nucleocapsid - and validate the utility of minimal peptide pools for population-level immune-monitoring. These insights inform the design of personalized immunotherapies and broadly effective vaccines.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":" ","pages":"147"},"PeriodicalIF":10.4,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12676865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145603834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic atlas of plasma metabolome across 40 human common diseases: mapping causal metabolites to disease risk.","authors":"Yanmin Li, Yimin Cai, Qianying Ma, Xiaojun Yang, Chunyi He, Ying Xu, Ming Zhang, Zequn Lu, Can Chen, Caibo Ning, Bo Liu, Yongchang Wei, Xiangpan Li, Meng Jin, Xu Zhu, Bin Li, Ying Zhu, Chaoqun Huang, Xiaoping Miao, Jianbo Tian","doi":"10.1186/s13073-025-01578-7","DOIUrl":"10.1186/s13073-025-01578-7","url":null,"abstract":"<p><strong>Background: </strong>Metabolites are closely linked to individual health and disease conditions. Identifying genetic factors influencing metabolite levels in specific diseases can enhance our understanding of disease etiology and informing precision medicine. This study aims to characterize the genetic architecture of metabolites in specific disease states and explore their potential biological functions.</p><p><strong>Methods: </strong>We conducted a comprehensive genome-wide metabolite quantitative trait locus (metQTL) analysis of 249 plasma metabolites across 40 disease states, based on nuclear magnetic resonance (NMR) data. To predict the biological significance of metQTLs, we performed a systematic functional annotation encompassing the analysis of genomic positions, heritability assessment, histone and transcription factor (TF) enrichment, effector gene identification, and potential drug targets evaluation. Furthermore, Mendelian randomization (MR) analyses were applied to uncover causal metabolites associated with diseases, and polygenic risk score (PRS) models were constructed to assess their predictive capacity for disease outcomes.</p><p><strong>Results: </strong>Across 40 common disease types, we identified 283,563 metQTL-metabolite association pairs involving 249 metabolites and 149,984 metQTLs derived from 26,536 independent loci. Functional annotations indicated that these metQTLs influence chromatin activity and transcription factor binding, suggesting their key roles in epigenetic regulation. Mendelian randomization analysis revealed 104 reliable causal evidence between metabolites and diseases. Additionally, metQTL-derived disease PRS models demonstrated excellent performance in the risk stratification of 8 diseases, offering a framework for translating genetic resources into clinical applications. An online platform, \"metQTL-Atlas\" ( https://metqtl.whu.edu.cn/home ), has been established for convenient browsing and downloading of our comprehensive results.</p><p><strong>Conclusions: </strong>This study provides a comprehensive resource that delineates the genetic architecture of metabolites across diverse disease contexts, offering new insights into disease etiology and advancing precision medicine through enhanced risk prediction and therapeutic target discovery.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":" ","pages":"153"},"PeriodicalIF":10.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12751435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145582084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive long-read sequencing system to assess DNA methylation at differentially methylated regions and imprinting-disorder-related genes.","authors":"Tatsuki Urakawa, Atsushi Hattori, Yasuko Ogiwara, Hayate Masubuchi, Mizuho Igarashi, Sayuri Nakamura, Kaori Hara-Isono, Keisuke Ishiwata, Hiroko Ogata-Kawata, Hiromi Kamura, Yoko Kuroki, Kazuhiko Nakabayashi, Maki Fukami, Masayo Kagami","doi":"10.1186/s13073-025-01559-w","DOIUrl":"10.1186/s13073-025-01559-w","url":null,"abstract":"<p><strong>Background: </strong>Imprinted genes are expressed in a parental-origin-specific manner. The imprinted regions including imprinted genes have differentially methylated regions (DMRs) with different 5-methylcytosine (5mC) patterns for CpGs on each parental allele, and DMRs function as imprinting control centers. Aberrant expression of the imprinted genes caused by structural variants involving DMRs, single-nucleotide variants in imprinted genes, uniparental disomy, and epimutation lead to imprinting disorders (IDs). Nanopore-based targeted long-read sequencing (T-LRS) can obtain sequence reads 10-100 kb long together with information on DNA methylation in each CpG and is cost-effective compared to whole-genome LRS. T-LRS is a valuable tool for efficient genetic testing for IDs and has great potential to elucidate the regulatory mechanisms in the imprinted regions. However, there is no T-LRS system targeting all ID-related regions.</p><p><strong>Methods: </strong>We conducted T-LRS targeting 78 DMRs and 22 genes in peripheral blood leukocytes from six healthy controls and set the normal range of methylation index (MI) for each CpG within the DMRs. To clarify the properties of DMRs, we compared MIs in CpGs within DMRs between haplotypes in 78 DMRs. To evaluate the usefulness of T-LRS, we conducted T-LRS on two previously reported patients with multi-locus imprinting disturbance (MLID) having pathogenic variants in MLID-causative genes and compared the MIs in CpGs within DMRs with those measured by array-based methylation analysis.</p><p><strong>Results: </strong>The median number of reads with 5mC and unmethylated cytosine in all DMRs in the six controls was over 40. We defined the normal range of MI for all CpGs in each allele and the total, and classified 78 DMRs into three categories, namely, 33 Complete-DMRs, 25 Partial-DMRs, and 20 Non-DMRs, based on the average of six controls for the median of differences of MIs in CpGs between haplotypes. We confirmed by T-LRS pathogenic variants in MLID-causative genes in patients with MLID. The patients' methylation defect patterns in T-LRS were similar to those in array-based methylation analysis, although T-LRS showed additional aberrantly methylated DMRs.</p><p><strong>Conclusions: </strong>We established a T-LRS system targeting all ID-related regions, defined standard MI ranges in CpGs on each parental allele, and demonstrated the usefulness of T-LRS.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"144"},"PeriodicalIF":10.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12628973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145549086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical and bacterial determinants of unfavorable tuberculosis treatment outcomes: an observational study in Georgia.","authors":"Galo A Goig, Chloé Loiseau, Nino Maghradze, Kakha Mchedlishvili, Teona Avaliani, Ana Tsutsunava, Daniela Brites, Sevda Kalkan, Sonia Borrell, Rusudan Aspindzelashvili, Zaza Avaliani, Maia Kipiani, Nestani Tukvadze, Levan Jugheli, Sebastien Gagneux","doi":"10.1186/s13073-025-01555-0","DOIUrl":"10.1186/s13073-025-01555-0","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) remains a major public health concern. Improving TB control programs and treatment success requires a deeper understanding of the factors that determine disease presentation and treatment outcomes. While the importance of patient factors is well established, our understanding of the bacterial determinants of disease presentation and treatment outcomes in TB remains limited.</p><p><strong>Methods: </strong>In this study, we analyzed the Mycobacterium tuberculosis complex (MTBC) genomes and the associated clinical data from 4529 TB patients in the country of Georgia covering a period of 13 years. We used multivariable modeling together with genome-wide association studies (GWAS) to identify patient and bacterial factors that determine TB disease manifestation and clinical outcomes.</p><p><strong>Results: </strong>Multivariable modelling confirmed the role of demographic and clinical factors in determining treatment outcomes, as well as the efficacy of novel TB treatments containing bedaquiline. In addition, we found that several bacterial factors, including the MTBC lineage, the specific mutations conferring resistance to rifampicin and fluoroquinolones, as well as a high bacterial burden, were associated with unfavorable outcomes. GWAS analyses revealed no bacterial genetic mutations associated with treatment outcomes beyond the known drug resistance-conferring mutations. However, we found that mutations in the bacterial gene sufD were linked to a reduced risk of lung cavities and a lower bacterial burden within patients. By contrast, specific mutations conferring resistance to rifampicin and fitness compensatory mutations were associated with a higher bacterial burden.</p><p><strong>Conclusions: </strong>Our results show that both patient and bacterial factors determine disease presentation and clinical outcomes in TB. They also support the rationale of optimizing treatment regimens against drug-resistant TB with existing drugs based on the specific genetic features of the pathogen. Finally, our results highlight sufD as a possible therapeutic candidate.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"143"},"PeriodicalIF":10.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12619330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a consensus molecular classifier for pancreatic ductal adenocarcinoma.","authors":"Pablo Villoslada-Blanco, Lola Alonso, Sergio Sabroso-Lasa, Miguel Maquedano, Lidia Estudillo, Francisco X Real, Evangelina López de Maturana, Núria Malats","doi":"10.1186/s13073-025-01568-9","DOIUrl":"10.1186/s13073-025-01568-9","url":null,"abstract":"<p><strong>Background: </strong>Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a 5-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular classifiers have attempted to clarify this heterogeneity with moderate agreement. Recognizing the complexity introduced by this extensive array of taxonomies, this study aims to develop a consensus molecular classifier by including both tumor and stroma features.</p><p><strong>Methods: </strong>We analyzed mRNA expression data from 514 PDAC samples, applying batch correction, filtering out low-expression genes, and using variance-stabilizing transformation. Tumor and stroma profiles were classified with previously published systems, while stroma compartments were estimated through virtual microdissection. For each classifier, multiple machine learning models were trained and optimized, with the top performers used to assign subtypes. A consensus classifier was created by building subtype similarity networks and applying a Markov clustering algorithm, and robustness was evaluated through resampling. Associations between consensus classes and overall survival were examined using multivariate Cox models.</p><p><strong>Results: </strong>The results indicated that Elastic-Net emerged as the superior model. We identified two classes for tumor components (Consensus Classical and Consensus Non-classical) and stroma components (Consensus Normal-Immune and Consensus Activated-ECM). The consensus Random Forest achieved a balanced accuracy of 96.33 and 98.92%, respectively. Across cohorts, the PDAConsensus algorithm identified tumor subtypes with distinct prognostic value, with Consensus Non-classical tumors showing poorer survival than Consensus Classical tumors. Associations for stroma consensus classes were weaker and less consistent across datasets.</p><p><strong>Conclusions: </strong>We developed a robust consensus classifier for PDAC that integrates tumor and stroma features. The classifier is accessible through the R package PDACMOC (PDACMolecularOmniClassifier, https://github.com/pavillos/PDACMOC , https://doi.org/10.5281/zenodo.17161373 ) and a Shiny app ( https://pdacmoc.cnio.es/ ). This classifier offers a biologically grounded framework that integrates existing systems, allows for single-sample classification, and improves prognostic stratification. By enabling subtype-specific therapies and better patient stratification in clinical trials, it can help guide precision medicine and enhance outcomes in PDAC management.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"142"},"PeriodicalIF":10.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12619402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the size limits of Bionano optical genome mapping to resolve alternative structures of linked interspersed chromosomal duplications.","authors":"Yang Pei, Eduardo Calpena, Jill M Brown, Ron Schwessinger, Lucy Platts, Simon J McGowan, Tazeen Ashraf, Francesca Forzano, Jane A Hurst, Wendy D Jones, Ajoy Sarkar, Richard J Gibbons, Stephen R F Twigg, Andrew O M Wilkie","doi":"10.1186/s13073-025-01571-0","DOIUrl":"10.1186/s13073-025-01571-0","url":null,"abstract":"","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"141"},"PeriodicalIF":10.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12616954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145512246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PredIG: an interpretable predictor of T-cell epitope immunogenicity.","authors":"Roc Farriol-Duran, Christian Domínguez-Dalmases, Albert Cañellas-Solé, Miguel Vazquez, Eduard Porta-Pardo, Víctor Guallar","doi":"10.1186/s13073-025-01569-8","DOIUrl":"10.1186/s13073-025-01569-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cytotoxic T cells are key effectors in the immune response against pathogens and tumors. Thus, identifying those immunogenic epitopes driving T-cell activation conforms a fundamental goal for antigen-based immunotherapies. T-cell antigen discovery is challenged by immense epitope landscapes, unfeasible to screen ad hoc experimentally due to the high cost and low throughput of immunogenicity validations. Precedingly, immunoinformatic models, with orders of magnitude higher throughput such as HLA-I binding affinity tools, are used to predict the antigenic potential of T-cell epitopes. However, the resulting immunogenicity screening success rates (ISSR)-the capacity to rank truly immunogenic epitopes among top-scored candidates prioritized for experimental validation-have remained incremental and the immunological explainability underlying model predictions limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;PredIG is an interpretable predictor of T-cell epitope immunogenicity trained upon 17,448 peptide-HLA-I allele pairs (pHLAs) with reported immunogenicity in T-cell reactivity and binding assays. Upon pHLAs, PredIG integrates an in silico feature space of antigenic properties (proteasomal cleavage, TAP translocation, HLA-I binding affinity, and presentation), and physicochemical epitope descriptors, particularly focused on TCR-facing central residues. Leveraging this information, we built three antigen-specific XGBoost models to compute PredIG immunogenicity scores (PredIG-NeoAntigen, PredIG-NonCanonical, and PredIG-Pathogen). We then used Shapley Additive models (SHAP) to analyze their immunological interpretability pinpointing a balanced feature importance between antigenic and physicochemical properties. This highlighted the strong contribution of antigen processing likelihood and physicochemical characteristics, often overlooked in T-cell epitope predictions. Comparably, PredIG obtained cutting-edge ISSR performance in our pathogen and non-canonical cancer antigen held-outs versus immunogenicity, HLA-I binding, and pHLA stability predictors. In cancer neoantigens, we used PredIG to refine the success rates of HLA-I binding affinity predictions and to prioritize an additional set of immunogenic (neo)epitopes differing from top-binding candidates across the three antigen types tested.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Overall, we demonstrate how PredIG immunogenicity scores are instrumental to refine and expand the prioritization of actionable T-cell (neo)epitopes in infection and cancer, including non-canonical antigens not seen during training. Furthermore, PredIG displays an unprecedented immunological interpretability determining important immunogenicity drivers beyond HLA-I binding affinity. Ultimately, PredIG enables large throughput antigen discovery in open-source containerized environments ( https://github.com/BSC-CNS-EAPM/PredIG ) and facilitates accessibility via a streamlined webserver ( https://horus.bsc.es/p","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"140"},"PeriodicalIF":10.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12613480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145503421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome instability and crosstalk with the immune response.","authors":"Roman M Chabanon, François-Xavier Danlos, Kaissa Ouali, Sophie Postel-Vinay","doi":"10.1186/s13073-025-01509-6","DOIUrl":"10.1186/s13073-025-01509-6","url":null,"abstract":"<p><p>Genome instability, tumour-promoting inflammation, and immune escape are three distinct hallmarks of cancer. However, accumulating scientific and clinical evidence over the past decade have uncovered a multifaceted interplay of complex dynamic network of interactions between genome instability, the DNA damage response (DDR), and tumour immunogenicity. Fuelled by the clinical successes of immune checkpoint blockers (ICB), growing interest for immuno-oncology and recent cancer biology discoveries have allowed a better understanding of the underlying biology and clinical opportunities brought by this interplay-which is yet, still only in its infancy. The cooperative nature of tumour cell-intrinsic and -extrinsic mechanisms involved suggests that harnessing genomic instability in cancer does not only hamper cancer cells fitness but also stimulate the anti-tumour immune response, thereby paving the way to the development of DDR-based immunomodulatory therapeutic strategies applicable to a variety of molecular and histological cancer types. Here, we review the various aspects of this crosstalk between genome instability and tumour immunogenicity, including feedforward and feedback mechanisms affecting either side of this interplay, as well as the specific consequences of chromosomal instability. We further discuss emerging DDR-based predictive biomarkers of response to ICB therapies, and finally examine the latest clinical developments of therapeutic combinations that exploit the DDR-immunity interplay in immuno-oncology.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"139"},"PeriodicalIF":10.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12587660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145444650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell multi-omics analysis revealed the expansion of age-associated B cells in the pancreas of type 1 autoimmune pancreatitis patients.","authors":"Jiaxin Wang, Chenxiao Liu, Xianda Zhang, Tianyi Che, Yizhou Zhao, Qidi Yang, Xianzheng Qin, Yifei Chen, Xiang Ao, Xiaonan Shen, Xiangyi He, Tingting Gong, Ling Zhang, Minmin Zhang, Dong Wang, Yanhua Du, Li Wen, Youqiong Ye, Yao Zhang, Chunhua Zhou, Duowu Zou","doi":"10.1186/s13073-025-01567-w","DOIUrl":"10.1186/s13073-025-01567-w","url":null,"abstract":"<p><strong>Background: </strong>Type 1 autoimmune pancreatitis (AIP) is pancreatic manifestation of IgG4-related disease (IgG4-RD), characterized by pancreatic lymphoplasmacytic infiltration. Despite this well-known pathological feature, the immune microenvironment and the complex cellular interactions within the pancreas in AIP remain poorly understood. This study aimed to characterize the local immune features of the pancreas in AIP patients.</p><p><strong>Methods: </strong>We employed single-cell RNA sequencing (scRNA-seq), immune receptor repertoire sequencing (scTCR/BCR-seq), and spatial transcriptome sequencing on biopsy samples from lesion tissues of AIP patients. Flow cytometry, multicolour immunofluorescence, and functional assays were performed to validate the findings from bioinformatics analysis.</p><p><strong>Results: </strong>Our results revealed an increased presence of IgD^- age-associated B cells (ABCs) in the pancreas of AIP patients. These ABCs were predicted to differentiate into plasma cells that secrete IgG. Additionally, CXCL9⁺ macrophages were found to recruit IgD^- ABCs via the CXCL9-CXCR3 axis. Elevated levels of T follicular helper cells (Tfhs) were also observed, which interacted with IgD^- ABCs through IL-21 secretion. Both ABCs and Tfhs were localized at the periphery of pancreatic tertiary lymphoid structures (TLSs). Importantly, these immune abnormalities were specific to AIP and were not present in the pancreases of patients with chronic pancreatitis.</p><p><strong>Conclusions: </strong>These findings highlight significant alterations in the pancreatic immune microenvironment in AIP and propose a potential pathogenic model involving ABCs, Tfhs, and macrophages. This model provides valuable insights that could inform the development of targeted therapeutic strategies for AIP.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"138"},"PeriodicalIF":10.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12584476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145444644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}