Genome Medicine最新文献

{"title":"SCAN-ACT: adoptive T cell therapy target discovery through single-cell transcriptomics.","authors":"Stefano Testa, Aastha Pal, Ajay Subramanian, Sushama Varma, Jack Pengfei Tang, Danielle Graham, Sara Arfan, Minggui Pan, Nam Q Bui, Kristen N Ganjoo, Sarah Dry, Paul Huang, Matt van de Rijn, Wei Jiang, Anusha Kalbasi, Everett J Moding","doi":"10.1186/s13073-025-01514-9","DOIUrl":"10.1186/s13073-025-01514-9","url":null,"abstract":"<p><strong>Background: </strong>The FDA approval of T cell receptor-engineered T cells (TCR-T) for synovial sarcoma demonstrates the potential for adoptive T cell therapies (ACTs) in solid tumors. However, the paucity of tumor-associated targets without expression in normal tissues remains a major bottleneck, especially in rare cancer subtypes.</p><p><strong>Methods: </strong>We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells. For surface membrane targets, SCAN-ACT proposes monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells. For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens. Selected targets were validated experimentally by protein expression and for peptide-MHC binding.</p><p><strong>Results: </strong>We applied the SCAN-ACT pipeline to soft tissue sarcoma (STS), analyzing 986,749 single cells to identify and prioritize 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells. Proposed targets and target pairs reflected the mesenchymal, neuronal, and hematopoietic ontogeny of STS. We further validated SCAN-ACT in glioblastoma revealing its versatility.</p><p><strong>Conclusions: </strong>This work provides a robust data repository along with a web-based and user-friendly set of analysis tools to accelerate ACT development for solid tumors ( https://scanact.stanford.edu/ ).</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"89"},"PeriodicalIF":10.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855086","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 quest for a complete understanding of the human genome.","authors":"Ahmad Abou Tayoun, Hamad Ali, Younes Mokrab","doi":"10.1186/s13073-025-01523-8","DOIUrl":"10.1186/s13073-025-01523-8","url":null,"abstract":"<p><p>An integrated roadmap toward clinical interpretation of the complete human genome is in dire need. We discuss approaches to meet this goal, including integrating data from diverse, well-phenotyped populations with enhanced long-read genome assemblies, variant calling as well as improved predictive models and scalable functional assays.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"88"},"PeriodicalIF":10.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12333148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798833","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":"Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.","authors":"Hanna M Hieromnimon, James Dolezal, Kristina Doytcheva, Frederick M Howard, Sara Kochanny, Zhenyu Zhang, Robert L Grossman, Kevin Tanager, Cindy Wang, Jakob Nikolas Kather, Evgeny Izumchenko, Nicole A Cipriani, Elana J Fertig, Alexander T Pearson, Samantha J Riesenfeld","doi":"10.1186/s13073-025-01502-z","DOIUrl":"10.1186/s13073-025-01502-z","url":null,"abstract":"<p><strong>Background: </strong>Precision oncology depends on identifying the biological vulnerabilities of a tumor. Molecular assays, like transcriptomics, provide an information-rich view of the tumor that can be leveraged to inform therapeutic selection. However, the costs of such assays can be prohibitive for clinical translation at scale. Histology-based imaging remains a predominant means of diagnosis that is widely accessible. To more broadly leverage limited molecular datasets, models have been trained to use histology to infer the expression of individual genes or pathways, with varying levels of accuracy and explainability.</p><p><strong>Methods: </strong>Our approach detects expression of transcriptional programs from tumor histology and interprets the image features supporting program detection. Specifically, we used RNA-seq data from squamous cell carcinoma (SCC) patients to infer cohesive expression patterns of multiple genes. Then, we used deep learning techniques to train a computational model to predict the activity levels of the transcriptional programs directly from histology images. We exploited that predictive capability to generate synthetic digital models of the cellular histology of each transcriptional program, using generative adversarial networks to isolate image features supporting specific transcriptional predictions and pathologist review to interpret the images.</p><p><strong>Results: </strong>Applying our histologically integrated latent space analysis to SCCs revealed sets of genes associated with both pathologist-interpretable image features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis, going beyond predictions of individual molecular features.</p><p><strong>Conclusions: </strong>Our results demonstrate an approach for discovering clinically interpretable histological features that indicate molecular, potentially treatment-informing, biological processes. These features are detectable in widely available histology slides, allowing a standard microscope to deliver complex, patient-specific molecular information.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"87"},"PeriodicalIF":10.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798832","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":"Contribution of rare coding variants to microcephaly in individuals with neurodevelopmental disorders.","authors":"Jihoon G Yoon, Hyunsoo Jang, Seungbok Lee, Se Song Jang, Soojin Park, Jaeso Cho, Minji Kim, Jiye Han, Hyounji Yun, Man Jin Kim, Soo Yeon Kim, Woo Joong Kim, Anna Cho, Jin Sook Lee, Murim Choi, Alberto Fernandez-Jaen, Sebastian Silva, Reinaldo Uribe-San-Martín, Christian Cantillano, Noriko Miyake, Byung Chan Lim, Jung Min Ko, Ki Joong Kim, Ki-Jun Yoon, Jong-Hee Chae","doi":"10.1186/s13073-025-01513-w","DOIUrl":"10.1186/s13073-025-01513-w","url":null,"abstract":"<p><strong>Background: </strong>Microcephaly, characterized by an abnormally small head size, frequently co-occurs with neurodevelopmental disorders (NDDs). While the genetic basis of NDDs has been widely investigated, the contribution of rare coding variants to microcephaly remains poorly understood.</p><p><strong>Methods: </strong>We investigated the relationships between head circumference and rare coding variants in 418 individuals with microcephaly, analyzing data from 1050 exomes (312 trios and 106 proband-only samples). Participants were classified into primary microcephaly (PM) and secondary microcephaly (SM) groups, and their clinical and genetic characteristics were systematically assessed. The functional impact of high-priority candidate genes, RTF1 and ASAP2, was further validated using neural progenitor cells (NPCs) and human forebrain organoid models.</p><p><strong>Results: </strong>Exome sequencing revealed 142 causative and 12 candidate genes associated with microcephaly. Pathway analyses indicated that PM genes are linked to early phases of brain development, whereas SM genes are more associated with later stages of neuronal maturation. In addition, the PM group had a significantly higher proportion of autosomal recessive disorders and exhibited more severe microcephaly than the SM group. Notably, females displayed greater microcephaly severity than males, primarily attributable to differences in the origin of the allele and inheritance patterns on the X chromosome. Functional experiments using CRISPR-Cas9 knockout in NPCs and brain organoids demonstrated reduced NPC proliferation, supporting the essential role of RTF1 and ASAP2 in brain development.</p><p><strong>Conclusions: </strong>This study sheds light on the complex genetic architecture of microcephaly, emphasizing the impact of rare coding variants on brain development and delineating distinct clinical and molecular profiles underlying PM and SM.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"86"},"PeriodicalIF":10.4,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794214","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":"Blocking CXCR4⁺ CD4⁺ T cells reprograms T_reg-mediated immunosuppression via modulating the Rho-GTPase/NF-κB signaling axis.","authors":"Canhui Cao, Miaochun Xu, Ting Peng, Xiaojie Liu, Shitong Lin, Yashi Xu, Tian Chu, Shiyi Liu, Ping Wu, Bai Hu, Wencheng Ding, Li Li, Ding Ma, Peng Wu","doi":"10.1186/s13073-025-01515-8","DOIUrl":"10.1186/s13073-025-01515-8","url":null,"abstract":"<p><strong>Background: </strong>While clinical trials have shown that CXCR4 antagonists can enhance the efficacy of cancer immunotherapy, the molecular mechanisms by which CXCR4 modulates the tumor microenvironment remain poorly understood. We recently identified CXCR4 as a regulator of exhausted CD8⁺ T cell phenotypes in cancer. Here, we investigate its role in orchestrating regulatory T (T_reg) cell-mediated immunosuppression within tumors.</p><p><strong>Methods: </strong>We conducted meta-analyses of single-cell RNA-seq datasets from pan-cancer tissues to characterize CXCR4 expression patterns in CD4⁺ T cells. Using CXCR4 antagonists and conditional knockout mice (Cxcr4^flox/flox, Lck^Cre), we inhibited T_reg phenotypes in vivo. Through single-cell transcriptomics and single-cell ATAC-seq of the cervical cancer mouse model, phosphoproteomics, and ChIP-seq analyses, we elucidated how CXCR4 blockade in CD4⁺ T cells suppresses activated T_reg phenotypes by modulating the Rho-GTPase/NF-κB signaling axis. We further integrated RNA-seq data, clinical trial datasets (NCT02826486 and NCT04516616), and human organoid models to validate the therapeutic potential of CXCR4 inhibition in enhancing antitumor immunotherapy.</p><p><strong>Results: </strong>Single-cell transcriptomics of CD4⁺ T cells across multiple cancers revealed CXCR4 expression was associated with T_reg cell developmental trajectories. Pharmacological and genetic inhibition of CXCR4 inhibited T_reg phenotypes in cervical cancer and breast cancer. Mechanistically, phosphoproteomics and ChIP-seq analyses unveiled that blocking CXCR4⁺ CD4⁺ T cells reduced activated T_reg phenotypes by modulating the Rho-GTPase/NF-κB signaling axis. Single-cell transcriptomic and multi-omic analyses demonstrated that blocking CXCR4⁺ CD4⁺ T cells promoted immunotherapy via reprogramming T_reg-mediated immunosuppression. Furthermore, clinical trial data and human cervical cancer organoids confirmed that blocking CXCR4 enhances antitumor immunotherapy by reducing T_reg phenotypes.</p><p><strong>Conclusions: </strong>Our study highlights the crucial role of CXCR4 in deriving T_reg-mediated immunosuppression via regulating the Rho-GTPase/NF-κB signaling axis, informing the potential of combining CXCR4 blockades with T cell-targeted immunotherapies.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"85"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784131","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":"varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.","authors":"Lusiné Nazaretyan, Philipp Rentzsch, Martin Kircher","doi":"10.1186/s13073-025-01517-6","DOIUrl":"10.1186/s13073-025-01517-6","url":null,"abstract":"<p><strong>Background: </strong>Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unbiased datasets for variant prioritization and effect predictions are rare as most of the available databases do not represent a broad ensemble of variant effects and are often biased towards the protein-coding genome, or even towards few well-studied genes.</p><p><strong>Methods: </strong>To overcome these issues, we propose several alternative training sets derived from subsets of human standing variation. Specifically, we use variants identified from whole-genome sequences of 71,156 individuals contained in gnomAD v3.0 and approximate the benign set with frequent standing variation and the deleterious set with rare or singleton variation. We apply the Combined Annotation Dependent Depletion framework (CADD) and train several alternative models using CADD v1.6.</p><p><strong>Results: </strong>Using the NCBI ClinVar validation set, we demonstrate that the alternative models have state-of-the-art accuracy, globally on par with deleteriousness scores of CADD v1.6 and v1.7, but also outperforming them in certain genomic regions. Being larger than conventional training datasets, including the evolutionary-derived training dataset of about 30 million variants in CADD, standing variation datasets cover a broader range of genomic regions and rare instances of the applied annotations. For example, they cover more recent evolutionary changes common in gene regulatory regions, which are more challenging to assess with conventional tools.</p><p><strong>Conclusions: </strong>Standing variation allows us to directly train state-of-the-art models for genome-wide variant prioritization or to augment evolutionary-derived variants in training. The proposed datasets have several advantages, like being substantially larger and potentially less biased. Datasets derived from standing variation represent natural allelic changes in the human genome and do not require extensive simulations and adaptations to annotations of evolutionary-derived sequence alterations used for CADD training. We provide datasets as well as trained models to the community for further development and application.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"84"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784132","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":"Developing a novel aging assessment model to uncover heterogeneity in organ aging and screening of aging-related drugs.","authors":"Yingqi Xu, Maohao Li, Congxue Hu, Yawen Luo, Xing Gao, Xinyu Li, Xia Li, Yunpeng Zhang","doi":"10.1186/s13073-025-01501-0","DOIUrl":"10.1186/s13073-025-01501-0","url":null,"abstract":"<p><strong>Background: </strong>The decline in organ function due to aging significantly impacts the health and quality of life of the elderly. Assessing and delaying aging has become a major societal concern. Previous studies have largely focused on differences between young and old individuals, often overlooking the complexity and gradual nature of aging.</p><p><strong>Methods: </strong>In this study, we constructed a comprehensive multi-organ aging atlas in mice and systematically analyzed the aging trajectories of 16 organs to elucidate their functional specificity and identify organ-specific aging trend genes. Cross-organ association analysis was employed to identify global aging regulatory genes, leading to the development of a multi-organ aging assessment model, hereafter referred to as the 2A model. The model's validity was confirmed using single-cell RNA sequencing data from aging mouse lungs, cross-species gene expression profiles, and pharmacogenomic data. Furthermore, a random walk algorithm and a weighted integration approach combining gene set enrichment analysis were implemented to systematically screen potential drugs for mitigating multi-organ aging.</p><p><strong>Results: </strong>The 2A model effectively assessed aging states in both human and mouse tissues and demonstrated predictive capability for senescent cell clearance rates. Compared to the sc-ImmuAging and SCALE clocks, the 2A model exhibited superior predictive accuracy at the single-cell level. Organ-specific analyses identified the lungs and kidneys as particularly susceptible to aging, with immune dysfunction and programmed cell death emerging as key contributors. Notably, single-cell data confirmed that plasma cell accumulation and naive-like cell reduction showed linear changes during organ aging. Aging trend genes identified in each organ were significantly enriched in aging-related functional pathways, enabling precise assessment of the aging process and determination of organ-specific aging milestones. Additionally, drug screening identified Fostamatinib, Ranolazine, and Metformin as potential modulators of multi-organ aging, with mechanisms involving key pathways such as longevity regulation and circadian rhythm.</p><p><strong>Conclusions: </strong>The 2A model represents a significant advancement in aging assessment by integrating multi-dimensional validation strategies, enhancing its accuracy and applicability. The identification of organ-specific aging pathways and candidate pharmacological interventions provides a theoretical foundation and translational framework for precision anti-aging therapies.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"83"},"PeriodicalIF":10.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707323","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":"Epigenomic preconditioning of peripheral monocytes determines their transcriptional response to the tumor microenvironment.","authors":"Máté Kiss, Laszlo Halasz, Eva Hadadi, Wilhelm K Berger, Petros Tzerpos, Szilard Poliska, Daliya Kancheva, Aurélie Gabriel, Romina Mora Barthelmess, Ayla Debraekeleer, Jan Brughmans, Yvon Elkrim, Liesbet Martens, Yvan Saeys, Bence Daniel, Zsolt Czimmerer, Damya Laoui, Laszlo Nagy, Jo A Van Ginderachter","doi":"10.1186/s13073-025-01511-y","DOIUrl":"10.1186/s13073-025-01511-y","url":null,"abstract":"<p><strong>Background: </strong>Monocytes are recruited to tumors and undergo transcriptional reprogramming resulting in tumor-promoting functions. Epigenomic features, such as post-translational modification of histones and chromatin accessibility, are key determinants of transcription factor binding and thereby play an important role in controlling transcriptional responses to the tissue environment. It remains unknown whether systemic tumor-associated signals could alter the epigenomic landscape of peripheral monocytes before they reach the tumor, thus shaping their subsequent response to the tumor microenvironment.</p><p><strong>Methods: </strong>We used a combination of genome-wide assays for chromatin accessibility and multiple histone modifications (H3K4me1, H3K4me3, H3K27ac) in a mouse tumor model to investigate changes in the epigenomic landscape of peripheral monocytes. We then integrated these epigenomic data with transcriptomic data to link altered regulatory elements to gene expression changes in monocytes occurring in the periphery or during tumor infiltration.</p><p><strong>Results: </strong>We found that tumor-induced systemic inflammation was associated with transcriptional and epigenomic preconditioning of peripheral monocytes. The distal tumor caused extensive remodeling of both H3K4me3⁺ promoters and H3K4me1⁺ enhancers. Specifically, this involved the repression of interferon-responsive regulatory elements as well as the establishment of enhancers harboring binding motifs for transcription factor families downstream of pro-inflammatory signaling, such as C/EBP, AP-1, and STAT. Reprogrammed enhancers in peripheral monocytes were linked to sustained gene expression changes that persisted after tumor infiltration. In addition, key pro-tumor genes upregulated in tumor-infiltrating monocytes showed epigenetic priming already in the circulation.</p><p><strong>Conclusions: </strong>These results suggest that cancer-associated remodeling of the epigenomic landscape in peripheral monocytes can shape the gene expression programs they acquire in the tumor, highlighting the role of the epigenome in redirecting monocyte function to support cancer progression.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"82"},"PeriodicalIF":10.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12285133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698306","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":"Impact of genetic risk and lifestyles on cardiovascular disease-free and total life expectancy: a cohort study.","authors":"Dong Sun, Qiufen Sun, Yinqi Ding, Canqing Yu, Dianjianyi Sun, Yuanjie Pang, Pei Pei, Ling Yang, Iona Y Millwood, Robin G Walters, Huaidong Du, Jun Zhang, Dan Schmidt, Junshi Chen, Zhengming Chen, Liming Li, Jun Lv","doi":"10.1186/s13073-025-01487-9","DOIUrl":"10.1186/s13073-025-01487-9","url":null,"abstract":"<p><strong>Background: </strong>Understanding the role of genetic risk and lifestyles on life expectancy (LE) without cardiovascular disease (CVD) and total LE may help optimize healthy aging strategies after taking genetic background into account.</p><p><strong>Methods: </strong>The China Kadoorie Biobank recruited participants from five urban and five rural areas across China during 2004-2008 and followed them up till December 31, 2018. A polygenic risk score (PRS) comprising 3.5 million genetic variants for overall CVD was constructed by combining multiple PRSs for CVD and CVD-related risk factors in 96,400 participants. Genetic risk was categorized into low, intermediate, and high according to the PRS, and lifestyles were categorized as favorable, intermediate, and unfavorable according to the number of unfavorable lifestyles. Using multistate life tables, we estimated CVD-free and total LE at age 40 for different genetic and lifestyle risk groups.</p><p><strong>Results: </strong>Genetic risk was more strongly associated with CVD onset than post-CVD mortality. As a result, the increase in LE without CVD associated with low genetic risk (4.9 years (95% CI 4.3-5.5) for women and 4.4 years (3.6-5.1) for men) was greater than the increase in total LE (2.9 years (1.8-3.8) for women and 2.6 years (1.5-3.5) for men) when compared to high genetic risk. In contrast, the association strengths of lifestyles with CVD onset and mortality after CVD were similar. Correspondingly, compared to those with unfavorable lifestyles, participants with favorable lifestyles had longer total LE and LE without CVD of 3.0 (1.5-4.3) and 4.0 (3.0-4.9) years in women and 5.7 (4.1-7.1) and 5.8 (4.7-6.9) years in men, respectively. Participants with high genetic risk benefited more from favorable lifestyles than those with low and intermediate genetic risk, gaining 5.9 (2.3-9.3) and 5.3 (3.0-7.6) years in women and 6.1 (0.8-10.6) and 6.2 (2.3-9.8) years in men for total and CVD-free LE, respectively.</p><p><strong>Conclusions: </strong>Improving lifestyles is critical for reducing CVD-related healthcare burden and promoting healthy aging, especially for individuals with high genetic risk.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"81"},"PeriodicalIF":10.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12285114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690008","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":"Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.","authors":"Oshrit Shtossel, Adi Eshel, Shalev Fried, Mika Geva, Ivetta Danylesko, Ronit Yerushalmi, Noga Shem-Tov, Joshua A Fein, Marco Fabbrini, Avichai Shimoni, Sondra Turjeman, Yoram Louzoun, Arnon Nagler, Omry Koren, Roni Shouval","doi":"10.1186/s13073-025-01507-8","DOIUrl":"10.1186/s13073-025-01507-8","url":null,"abstract":"<p><strong>Background: </strong>Allogeneic hematopoietic stem cell transplantation (HSCT) is potentially curative for hematologic malignancies but is frequently complicated by relapse and immune-mediated complications, such as graft-versus-host disease (GVHD). Emerging evidence suggests a role for the intestinal and oral microbiome in modulating HSCT outcomes, yet predictive models incorporating microbiome data remain limited.</p><p><strong>Methods: </strong>We applied the RATIO (suRvival Analysis lefT barrIer lOss) model to longitudinal stool and saliva microbiome data from 204 adult HSCT recipients to predict the timing of seven outcomes: overall survival (OS), non-relapse mortality (NRM), relapse, acute GVHD (grades II-IV and III-IV), chronic GVHD, and oral chronic GVHD. A total of 514 stool and 1291 saliva samples were collected over 70 weeks post-HSCT. Model performance was evaluated using the concordance index (CI) and Spearman correlation coefficient (SCC), with SHAP (SHapley Additive exPlanations) analysis used for model interpretability.</p><p><strong>Results: </strong>Oral and stool microbial dysbiosis peaked within the first 2 weeks post-HSCT, followed by partial recovery. Using the RATIO model, we found that microbiome features from early time points (weeks 1-2) were most predictive of short-term complications such as acute GVHD, while later samples (weeks 36-70) were more informative for long-term outcomes, including overall survival. RATIO outperformed traditional survival models (Cox and Random Survival Forest) across most outcomes (median CI > 0.65), with stool microbiota showing greater predictive power than saliva. SHAP analysis identified specific stool genera, including Collinsella and Eggerthella, associated with shorter time to various complications. External validation using a pediatric GVHD cohort confirmed the model's generalizability and reproducibility. External validation using a pediatric HSCT cohort (n = 90) confirmed the reproducibility and generalizability of these microbiome-based predictions.</p><p><strong>Conclusions: </strong>Microbiome profiling of stool and saliva samples offers robust, time-sensitive prediction of post-HSCT complications. The RATIO model enables interpretable, time-to-event prediction across multiple outcomes and may inform microbiome-guided interventions to improve transplant success.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"80"},"PeriodicalIF":10.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659072","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}