Genome Medicine最新文献

{"title":"Correction: Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival.","authors":"Wei Tang, Ming Zhou, Tiffany H Dorsey, DaRue A Prieto, Xin W Wang, Eytan Ruppin, Timothy D Veenstra, Stefan Ambs","doi":"10.1186/s13073-025-01495-9","DOIUrl":"10.1186/s13073-025-01495-9","url":null,"abstract":"","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"69"},"PeriodicalIF":10.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484098","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":"Longitudinal analysis of genetic and environmental interplay in human metabolic profiles and the implication for metabolic health.","authors":"Jing Wang, Alberto Zenere, Xingyue Wang, Göran Bergström, Fredrik Edfors, Mathias Uhlén, Wen Zhong","doi":"10.1186/s13073-025-01492-y","DOIUrl":"10.1186/s13073-025-01492-y","url":null,"abstract":"<p><strong>Background: </strong>Understanding how genetics and environmental factors shape human metabolic profiles is crucial for advancing metabolic health. Variability in metabolic profiles, influenced by genetic makeup, lifestyle, and environmental exposures, plays a critical role in disease susceptibility and progression.</p><p><strong>Methods: </strong>We conducted a two-year longitudinal study involving 101 clinically healthy individuals aged 50 to 65, integrating genomics, metabolomics, lipidomics, proteomics, clinical measurements, and lifestyle questionnaire data from repeat sampling. We evaluated the influence of both external and internal factors, including genetic predispositions, lifestyle factors, and physiological conditions, on individual metabolic profiles. Additionally, we developed an integrative metabolite-protein network to analyze protein-metabolite associations under both genetic and environmental regulations.</p><p><strong>Results: </strong>Our findings highlighted the significant role of genetics in determining metabolic variability, identifying 22 plasma metabolites as genetically predetermined. Environmental factors such as seasonal variation, weight management, smoking, and stress also significantly influenced metabolite levels. The integrative metabolite-protein network comprised 5,649 significant protein-metabolite pairs and identified 87 causal metabolite-protein associations under genetic regulation, validated by showing a high replication rate in an independent cohort. This network revealed stable and unique protein-metabolite profiles for each individual, emphasizing metabolic individuality. Notably, our results demonstrated the importance of plasma proteins in capturing individualized metabolic variabilities. Key proteins related to individual metabolic profiles were identified and validated in the UK Biobank, showing great potential for metabolic risk assessment.</p><p><strong>Conclusions: </strong>Our study provides longitudinal insights into how genetic and environmental factors shape human metabolic profiles, revealing unique and stable individual metabolic profiles. Plasma proteins emerged as key indicators for capturing the variability in human metabolism and assessing metabolic risks. These findings offer valuable tools for personalized medicine and the development of diagnostics for metabolic diseases.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"68"},"PeriodicalIF":10.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12172340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316704","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":"PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.","authors":"Baole Wen, Sheng Shi, Yi Long, Yanan Dang, Weidong Tian","doi":"10.1186/s13073-025-01496-8","DOIUrl":"10.1186/s13073-025-01496-8","url":null,"abstract":"<p><strong>Background: </strong>Current phenotype-based diagnostic tools often struggle with accurate disease prioritization due to incomplete phenotypic data and the complexity of rare disease presentations. Additionally, they lack the ability to generate patient-centered clinical insights or recommend further symptoms for differential diagnosis.</p><p><strong>Methods: </strong>We developed PhenoDP, a deep learning-based toolkit with three modules: Summarizer, Ranker, and Recommender. The Summarizer fine-tuned a distilled large language model to create clinical summaries from a patient's Human Phenotype Ontology (HPO) terms. The Ranker prioritizes diseases by combining information content-based, phi-based, and semantic-based similarity measures. The Recommender employs contrastive learning to recommend additional HPO terms for enhanced diagnostic accuracy.</p><p><strong>Results: </strong>PhenoDP's Summarizer produces more clinically coherent and patient-centered summaries than the general-purpose language model FlanT5. The Ranker achieves state-of-the-art diagnostic performance, consistently outperforming existing phenotype-based methods across both simulated and real-world datasets. The Recommender also outperformed GPT-4o and PhenoTips in improving diagnostic accuracy when its suggested terms were incorporated into different ranking pipelines.</p><p><strong>Conclusions: </strong>PhenoDP enhances Mendelian disease diagnosis through deep learning, offering precise summarization, ranking, and symptom recommendation. Its superior performance and open-source design make it a valuable clinical tool, with potential to accelerate diagnosis and improve patient outcomes. PhenoDP is freely available at https://github.com/TianLab-Bioinfo/PhenoDP .</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"67"},"PeriodicalIF":10.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247520","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":"Associations between HLA-II variation and antibody specificity are predicted by antigen properties.","authors":"Gabriel Innocenti, Sergio Andreu-Sánchez, Nicolai V Hörstke, Hesham Elabd, Iros Barozzi, Andre Franke, Máté Manczinger, Thomas Vogl","doi":"10.1186/s13073-025-01486-w","DOIUrl":"10.1186/s13073-025-01486-w","url":null,"abstract":"<p><strong>Background: </strong>Human leukocyte antigen class II (HLA-II) genes are highly polymorphic affecting the specificity of human antibody responses, as presentation of processed antigen peptides by HLA-II on B cells is essential for T helper cell dependent affinity maturation and class switching. The combination of high-throughput immunoassays and genome-wide association studies has recently revealed strong associations between HLA-II variants and antibody responses against specific antigens. However, factors underlying these associations remain incompletely understood.</p><p><strong>Methods: </strong>Here, we have leveraged paired data sets of SNP arrays and functional antibody epitope repertoires against 344,000 peptide antigens in 1940 individuals to mine for key determinants linking genetics and antibody specificity.</p><p><strong>Results: </strong>We show that secreted proteins and antigens presented in small modules (i.e., viruses) are significantly more frequently associated with HLA-II alleles, than membrane bound or intracellular proteins. This data suggests a model in which antibody responses against separate antigen units composed of single or few proteins dominate HLA-II associations. In contrast, the presence of manifold intracellular or membrane proteins (peptides of which could be bound by different HLA-II alleles) on bacterial cells dilutes potential associations to antibody specificities.</p><p><strong>Conclusions: </strong>Hence, genetic associations to antibody specificities are shaped by antigen intrinsic properties. Given the prominent role of HLA-II alleles in infection, autoimmune diseases, allergies, and cancer, our work provides a theoretical framework to study antigen/HLA-II risk factors in these disease settings and will fuel the design of improved immunogenetics screens.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"65"},"PeriodicalIF":10.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12131526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208402","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":"Pan-cancer analysis identifies CD155 as a promising target for CAR-T cell therapy.","authors":"Xiaohong Liu, Yue Sun, Boxu Lin, Hao Xiong, Xinyue Lu, Binghe Tan, Chenglin Zhang, Mingyao Liu, Juliang Qin, Na Zhang, Bing Du","doi":"10.1186/s13073-025-01490-0","DOIUrl":"10.1186/s13073-025-01490-0","url":null,"abstract":"<p><strong>Background: </strong>Chimeric antigen receptor T (CAR-T) cell therapy has shown remarkable success in treating hematologic malignancies. However, its efficacy against solid tumors remains limited. One of the major challenges is the lack of specific tumor antigens. Therefore, the exploration and rational selection of novel tumor targets is urgently needed. In this study, we investigate the therapeutic potential of targeting CD155 in cancer by CAR-T cells.</p><p><strong>Methods: </strong>The expression of CD155 was analyzed across various cancer types using data from The Cancer Genome Atlas (TCGA) and validated by tissue microarray analysis. The impact of CD155 on T cell mediated cytotoxicity was analyzed using CD155 over-expression or knockout tumor cells. Subsequently, second-generation CAR-T cells were constructed using either the extracellular domain (ECD) of TIGIT or an anti-CD155 scFv to evaluate their anti-tumor efficacy both in vitro and in vivo.</p><p><strong>Results: </strong>We demonstrated that CD155 is specifically overexpressed across various cancer types and that its high expression is strongly associated with poor prognosis, as revealed by data from TCGA. Consistently, CD155 is significantly upregulated in clinical tumor tissues and in numerous cancer cell lines, while it is rarely expressed in normal tissues. Furthermore, CD155 expression is also significantly increased in granulocytes derived from cancer patients compared to those from healthy donors. Functionally, high CD155 expression significantly inhibits the release of cytotoxic factors from T cells, thereby functioning as an immune checkpoint that mediates tumor immune evasion. After comparison, the scFv based anti-CD155 CAR-T cells demonstrated stronger anti-tumor activity than ECD of TIGIT based CAR-T cells. Moreover, the scFv based CAR-T cells exhibited effective anti-tumor activity against multiple CD155⁺ solid and hematologic tumors both in vitro and in different xenograft mouse models.</p><p><strong>Conclusions: </strong>Our study demonstrates that CD155 is selectively expressed in cancer cells while being rarely detected in normal tissues, and may serve as a promising pan-cancer target for CAR-T therapy. Targeting CD155 with CAR-T cells provides an effective approach to treating both solid and hematologic malignancies.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"64"},"PeriodicalIF":10.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208404","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":"The molecular impact of cigarette smoking resembles aging across tissues.","authors":"Jose Miguel Ramirez, Rogério Ribeiro, Oleksandra Soldatkina, Athos Moraes, Raquel García-Pérez, Winona Oliveros, Pedro G Ferreira, Marta Melé","doi":"10.1186/s13073-025-01485-x","DOIUrl":"10.1186/s13073-025-01485-x","url":null,"abstract":"<p><strong>Background: </strong>Tobacco smoke is the main cause of preventable mortality worldwide. Smoking increases the risk of developing many diseases and has been proposed as an aging accelerator. Yet, the molecular mechanisms driving smoking-related health decline and aging acceleration in most tissues remain unexplored.</p><p><strong>Methods: </strong>Here, we use data from the Genotype-Tissue Expression Project (GTEx) to perform a characterization of the effect of cigarette smoking across human tissues. We perform a multi-tissue analysis across 46 human tissues. Our multi-omics characterization includes analysis of gene expression, alternative splicing, DNA methylation, and histological alterations. We further analyze ex-smoker samples to assess the reversibility of these molecular alterations upon smoking cessation.</p><p><strong>Results: </strong>We show that smoking impacts tissue architecture and triggers systemic inflammation. We find that in many tissues, the effects of smoking significantly overlap those of aging. Specifically, both age and smoking upregulate inflammatory genes and drive hypomethylation at enhancers (odds ratio (OR) = 2). In addition, we observe widespread smoking-driven hypermethylation at target regions of the Polycomb repressive complex (OR = 2), which is a well-known aging effect. Smoking-induced epigenetic changes overlap causal aging CpGs, suggesting that these methylation changes may directly mediate the aging acceleration observed in smokers. Finally, we find that smoking effects that are shared with aging are more persistent over time.</p><p><strong>Conclusion: </strong>Overall, our multi-tissue and multi-omic analysis of the effects of cigarette smoking provides an extensive characterization of the impact of tobacco smoke across tissues and unravels the molecular mechanisms driving smoking-induced tissue homeostasis decline and aging acceleration.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"66"},"PeriodicalIF":10.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12131351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208405","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":"Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age.","authors":"Xiaolong Guo, Mahnoor Sulaiman, Alexander Neumann, Shijie C Zheng, Charlotte A M Cecil, Andrew E Teschendorff, Bastiaan T Heijmans","doi":"10.1186/s13073-025-01489-7","DOIUrl":"10.1186/s13073-025-01489-7","url":null,"abstract":"<p><p>Variations in immune-cell fractions can confound or hamper interpretation of DNAm-based biomarkers in blood. Although cell-type deconvolution can address this challenge for cord and adult blood, currently there is no method applicable to blood from other age groups, including infants and children. Here we construct and extensively validate a DNAm reference panel, called UniLIFE, for 19 immune cell-types, applicable to blood tissue of any age. We use UniLIFE to delineate the dynamics of immune-cell fractions from birth to old age, and to infer disease associated immune cell fraction variations in newborns, infants, children and adults. In a prospective longitudinal study of type-1 diabetes in infants and children, UniLIFE identifies differentially methylated positions that precede type-1 diabetes diagnosis and that map to diabetes related signaling pathways. In summary, UniLIFE will improve the identification and interpretation of blood-based DNAm biomarkers for any age group, but specially for longitudinal studies that include infants and children. The UniLIFE panel and algorithms to estimate cell-type fractions are available from our EpiDISH Bioconductor R-package: https://bioconductor.org/packages/release/bioc/html/EpiDISH.html.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"63"},"PeriodicalIF":10.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12108007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144158039","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":"Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system.","authors":"Miriam Angeloni, Davide Rizzi, Simon Schoen, Alessandro Caputo, Francesco Merolla, Arndt Hartmann, Fulvia Ferrazzi, Filippo Fraggetta","doi":"10.1186/s13073-025-01484-y","DOIUrl":"10.1186/s13073-025-01484-y","url":null,"abstract":"<p><strong>Background: </strong>Digital pathology (DP) has revolutionized cancer diagnostics and enabled the development of deep-learning (DL) models aimed at supporting pathologists in their daily work and improving patient care. However, the clinical adoption of such models remains challenging. Here, we describe a proof-of-concept framework that, leveraging Health Level 7 (HL7) standard and open-source DP resources, allows a seamless integration of both publicly available and custom developed DL models in the clinical workflow.</p><p><strong>Methods: </strong>Development and testing of the framework were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect through HL7 messaging the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence-based decision support system (AI-DSS) containing 16 pre-trained DL models. Open-source toolboxes for DL model deployment were used to run DL model inference, and QuPath was used to provide an intuitive visualization of model predictions as colored heatmaps.</p><p><strong>Results: </strong>A default deployment mode runs continuously in the background as each new slide is digitized, choosing the correct DL model(s) on the basis of the tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slide tray. In both cases, the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate visualization style for the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly as slide description in the virtual slide tray.</p><p><strong>Conclusions: </strong>Taken together, the developed integration framework through the use of the HL7 standard and freely available DP resources offers a standardized, portable, and open-source solution that lays the groundwork for the future widespread adoption of DL models in pathology diagnostics.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"60"},"PeriodicalIF":10.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149447","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":"DNA repair and the contribution to chemotherapy resistance.","authors":"Ksenija Nesic, Phoebe Parker, Elizabeth M Swisher, John J Krais","doi":"10.1186/s13073-025-01488-8","DOIUrl":"10.1186/s13073-025-01488-8","url":null,"abstract":"<p><p>The DNA damage response comprises a set of imperfect pathways that maintain cell survival following exposure to DNA damaging agents. Cancers frequently exhibit DNA repair pathway alterations that contribute to their intrinsic genome instability. This, in part, facilitates a therapeutic window for many chemotherapeutic agents whose mechanisms of action often converge at the generation of a double-strand DNA break. The development of therapy resistance occurs through countless molecular mechanisms that promote tolerance to DNA damage, often by preventing break formation or increasing repair capacity. This review broadly discusses the DNA damaging mechanisms of action for different classes of chemotherapeutics, how avoidance and repair of double-strand breaks can promote resistance, and strategic directions for counteracting therapy resistance.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"62"},"PeriodicalIF":10.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149914","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":"The case for including proteomics in routine diagnostic practice for rare disease.","authors":"Elizabeth M McCormick","doi":"10.1186/s13073-025-01491-z","DOIUrl":"10.1186/s13073-025-01491-z","url":null,"abstract":"<p><p>Many people with rare diseases cannot access personalized therapies because they do not have a confirmed genetic diagnosis. Promising technologies including proteomics are underutilized in routine diagnostic practice. It is time to incorporate proteomics into the diagnostic workflow to shorten time to diagnosis and expand treatment options for rare disease.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"61"},"PeriodicalIF":10.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150183","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}