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AI-generated MLH1 small binder improves prime editing efficiency
A compact, AI-generated suppressor of DNA mismatch repair can enhance prime editing in vivo and in vitro and can be integrated into a variety of prime editing architectures.
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Framework for the Development and Delivery of Digital Peer Support Programs: Qualitative Study on in-Person and Digital Delivery for People With Cardiovascular Disease
Background: Peer support (sharing experiences/support with others with the same condition) improves health outcomes among people with cardiovascular disease (CVD), including self-management behaviours and self-efficacy. However, current peer support interventions are diverse. Evidence is lacking on peer support attenders perceptions of benefits and the elements that are considered priorities, especially for digital interventions. Objective: The study objectives were to 1) describe perceived benefits and recommendations for CVD peer support programs from people attending in-person peer support, 2) identify priorities for digital peer support from consumers and clinicians testing a peer support app prototype, and 3) develop a framework to inform future peer support intervention development. Methods: Qualitative methodology was used across two components to address the objectives of this study. In Component 1, semi-structured focus groups were conducted with attenders of established in-person CVD peer support groups, exploring the perceived benefits of peer support and recommendations for future programs. In Component 2, semi-structured interactive workshops with consumers with CVD and semi-structured online interviews with CVD clinicians/researchers were undertaken seeking feedback and recommendations for digital peer support using an exploratory digital CVD peer support application prototype. Data were recorded digitally, transcribed verbatim, and analysed thematically. Findings from both components were iteratively synthesised to inform a digital peer support development framework. Results: In Component 1, 22 participants (age range 29-84 years, male 45%) took part in focus groups. The overarching theme was that peer support provides benefits through sharing experiences. Five themes were refined and defined; (i) peer support provides a way of coping, (ii) peers learn from each other, (iii) peers understand what each other are going through, (iv) the peer community uplifts mood and build confidence, and (v) awareness, flexibility and resources are important for engagement. In Component 2, five participants (age range 55-74 years, male 60%) attended two workshops and eight clinicians/researchers (age range 30-65 years, male 10%) were interviewed. Three themes were refined and defined: (i) autonomy is essential to promote engagement, (ii) safeguarding is important to both users and clinicians, and (iii) interfaces that are simple, easy to use and visually attractive enable use. Priorities identified from both components included greater peer support awareness and uptake, flexibility with timing and family participation, healthcare professional involvement, provision of resources, autonomous features enabling choice, checklists and clinician moderation for safeguarding, and simple to use interfaces. Conclusions: Participants in peer support programs derive benefit from sharing their experience of living with CVD which enable coping, learning, feeling understood and a sense of community. Priorities were synthesised to create a framework for digital peer support development for future peer support with recommendations to focus on six key areas: uptake, flexibility, resources, autonomy, safeguarding and interface.
Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic
Nature Biotechnology, Published online: 16 October 2025; doi:10.1038/s41587-025-02839-x
Somatic small variants in cancer genomes are identified in both short-read and long-read data.
Implementing a Digital Mental Health Intervention—the Lumi Nova App—to Support Children With Anxiety in Economically Disadvantaged Areas: Mixed Methods Study
Background: Anxiety is one of the most common mental health problems experienced by children worldwide. In the UK, many children experiencing anxiety do not receive adequate or timely help. Children living in economically-disadvantaged areas experience more mental health problems than those living in high income areas and are less able to engage in activities that can have a positive or protective impact on their mental health. The need for providing low-cost, accessible and engaging mental health interventions for children living in these areas is high. Objective: The study aimed to explore how a digital mental health therapeutic, ‘Lumi Nova: Tales of Courage’, could be used to support children living with anxiety in economically-disadvantaged areas. Methods: A mixed method study design was used to explore the implementation of Lumi Nova using a supported delivery model with mental health teams based in the North of England. Quantitative data collection on recruitment and engagement patterns were collected and analysed. Qualitative research explored children, parent and practitioner views and experiences with the Lumi Nova app. Results: 113 children were consented to use Lumi Nova and 98 (87%) accessed the intervention at least once. Qualitative semi-structured interviews found that children, their parents and practitioners viewed the Lumi Nova app positively. Quantitative analysis of the recruitment data suggested the feasibility of a future larger roll-out. Analysis of usage data demonstrated varied patterns of engagement with the intervention. The frequency and duration of usage varied across children, as did the activities completed within the game: almost half (49%) completed three in-game challenges indicating progression through the treatment pathway. Conclusions: The study demonstrated that a digital mental health intervention could be successfully deployed within economically-disadvantaged areas in the UK to support children experiencing anxiety. Expected barriers to the deployment of digital mental health interventions in economically-disadvantaged areas (e.g. lack of access to smartphones, data plans, lack of technical skills) were not reported. Digital mental health interventions have the potential to address current gaps in mental health provision for disadvantaged individuals and communities.
Automated AI based identification of autism spectrum disorder from home videos
npj Digital Medicine, Published online: 10 October 2025; doi:10.1038/s41746-025-01993-5
Automated AI based identification of autism spectrum disorder from home videos
A human pan-disease blood atlas of the circulating proteome
Science, Ahead of Print.
Stop treating code like an afterthought: record, share and value it
Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03196-0
Scientists, research institutions, funders, libraries and publishers must all improve software practices.
Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis
Nature Medicine, Published online: 30 September 2025; doi:10.1038/s41591-025-03933-y
In a prospective study enrolling 1,222 patients from 22 emergency departments, a device using a machine-learning-based signature of blood mRNAs demonstrated clinically acceptable performance to diagnose bacterial and viral infections and to predict the all-cause need for critical care interventions within 7 days, with benchmark to established biomarkers and risk scores.
Clinical Management of Circulating Tumor DNA in Breast Cancer: Detection, Prediction, and Monitoring
Breast Cancer (Dove Med Press). 2025 Sep 25;17:851-861. doi: 10.2147/BCTT.S542704. eCollection 2025.
ABSTRACT
Despite substantial progress in the diagnosis and treatment of breast cancer, current therapeutic regimens exhibit limitations, necessitating the identification of more robust biomarkers to optimize personalized strategies. Circulating tumor DNA (ctDNA), as a non-invasive liquid biopsy modality, overcomes the inherent constraints of biopsies in capturing tumor heterogeneity. Accumulating evidence from prospective cohort studies demonstrates the clinical utility of ctDNA in risk stratification, guidance of therapeutic decision-making, recurrence surveillance and other clinical applications. Furthermore, ctDNA profiling enhances real-time pharmacodynamic monitoring and accelerates drug development by identifying molecular responders. The methodical requirements and challenges inherent in implementing liquid biopsy assessments in the clinic are examined. These encompass critical pre-analytical variables, the need for highly sensitive and specific analytical techniques, standardization of assays and bioinformatics pipelines across laboratories and the complexities of interpreting results. This review synthesizes current evidence supporting ctDNA integration into breast cancer management frameworks and systematically addresses its methodological challenges and clinical limitations.
PMID:41036092 | PMC:PMC12479222 | DOI:10.2147/BCTT.S542704
Clinical Decision Support Systems Using Home Blood Pressure Readings to Manage Patients With Hypertension: Scoping Review
Background: Home blood pressure (HBP) is an important parameter that guides clinicians in managing hypertension in patients. However, in using these records to manage patients, physicians face challenges, particularly regarding access, integration, and interpretation of the records when making clinical decisions. Clinical decision support systems (CDSSs) have been proposed to address these challenges; however, current literature reveals significant heterogeneity and gaps in CDSSs used for hypertension management. Objective: This study aimed to summarize existing studies on CDSSs that use HBP readings to manage patients with hypertension. Methods: We conducted a scoping review, with searches performed in PubMed, Embase, and Scopus on April 1, 2024. The results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Studies that used CDSSs integrated with HBP monitoring among adult patients with hypertension in outpatient settings were included. Non-English studies were excluded. Outcomes assessed included the theoretical frameworks used for CDSS development, CDSS components (data capture, processing, and output), clinical outcomes, user experiences, and implementation processes. Results: Of the 5023 articles screened, 33 (0.66%) were included. Most of the studies were conducted in the United States (16/33, 49%) and were randomized controlled trials (21/33, 64%). Nearly two-thirds of the CDSSs (21/33, 64%) were computerized. Only 1 (3%) of the 33 studies reported using a theoretical framework for CDSS development. HBP recording and uploading were predominantly automatic (23/33, 70%). All computerized CDSSs (21/33, 64%) used rule-based algorithms, and most (19/21, 91%) incorporated alert triggers for results outside the reference range. More than a third of the studies (13/33, 39%) were based on hypertension guidelines. Among studies that reported outcomes, most reported improved blood pressure (25/29, 86%) and adjustment in antihypertensive medications (16/19, 84%). Patients and clinicians appreciated the convenience and remote monitoring (10/33, 30%) but reported challenges with usability and access to computerized CDSSs (2/21, 10%). Of the studies using noncomputerized CDSSs (12/33, 36%), all incorporated patient education, while nearly two-thirds of the studies using computerized CDSSs (13/21, 62%) did the same. Clinician training was reported in 5% (1/21) of the computerized CDSSs and 25% (3/12) of the noncomputerized CDSSs. Conclusions: While CDSSs hold promise for improving hypertension management, gaps remain in their development and implementation. Future efforts should focus on integrating robust frameworks; aligning with guidelines; enhancing manual data integration; and addressing usability to maximize effectiveness, adoption, and user satisfaction. Trial Registration: Open Science Framework 26zmn; https://osf.io/26zmn
Scaling DNA synthesis with a microchip-based massively parallel synthesis system
Nature Biotechnology, Published online: 01 October 2025; doi:10.1038/s41587-025-02844-0
A microchip-based DNA synthesis method enables scalable production of complex DNA constructs.
Association of HTR1F with Prognosis, Tumor Immune Microenvironment, and Drug Sensitivity in Cancer: A Multi-Omics Perspective
Biomedicines. 2025 Sep 11;13(9):2238. doi: 10.3390/biomedicines13092238.
ABSTRACT
Background:HTR1F (5-Hydroxytryptamine Receptor 1F) encodes a G protein-coupled receptor involved in serotonin signaling. Although dysregulated HTR1F expression has been implicated in certain malignancies, its biological functions and clinical significance across cancer types remain largely unexplored. Methods: We performed an integrative pan-cancer analysis of transcriptomic and pharmacogenomic datasets covering 34 cancer types (PAN-CAN cohort, N = 19,131; normal tissues, G = 60,499). Drug sensitivity and molecular docking analyses were conducted using the GSCALite database. The protein-protein interaction (PPI) network of HTR1F was constructed via the STRING database. Additionally, we evaluated the effects of HTR1F overexpression on proliferation and invasion in human lung squamous cell carcinoma (LUSC) cell lines NCI-H520 and NCI-H226. Results:HTR1F expression was significantly upregulated in 17 cancer types and was associated with poor prognosis, with LUSC showing an AUC of 0.912 for 1-year survival prediction. In LUSC, 695 genes were upregulated and 67 downregulated in response to HTR1F overexpression. HTR1F expression correlated with immune-related genes, immune checkpoints, tumor-infiltrating immune cells, tumor mutation burden (TMB), microsatellite instability (MSI), and drug responses. Genomic alterations, including amplification and deletion, were positively associated with HTR1F expression. Drug sensitivity analysis identified compounds such as sotrastaurin (-10.2 kcal/mol), austocystin D (-9.7 kcal/mol), and tivozanib (-9.3 kcal/mol) as potentially effective inhibitors based on predicted binding affinity. Functional enrichment analyses (GO, KEGG) and GSEA revealed that HTR1F is primarily involved in cell cycle regulation, DNA replication, cellular senescence, and immune-related pathways. Functional validation showed that HTR1F overexpression promotes proliferation of LUSC cells via the MAPK signaling pathway. Conclusions: Our integrative analysis highlights HTR1F as a potential biomarker associated with prognosis, immune modulation, and drug sensitivity across multiple cancer types. These findings provide a foundation for future experimental and clinical studies to explore HTR1F-targeted therapies.
PMID:41007799 | PMC:PMC12467612 | DOI:10.3390/biomedicines13092238
FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology
Nat Commun. 2025 Sep 25;16(1):8388. doi: 10.1038/s41467-025-63050-9.
ABSTRACT
Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.
PMID:40998789 | PMC:PMC12462499 | DOI:10.1038/s41467-025-63050-9
Cell-free DNA fragmentomics: a universal framework for early cancer detection and monitoring
Am J Clin Exp Immunol. 2025 Aug 15;14(4):237-240. doi: 10.62347/EBRY4326. eCollection 2025.
ABSTRACT
Cell-free DNA (cfDNA) fragmentomics has emerged as a powerful and noninvasive approach for cancer detection, characterization, and monitoring. By analyzing genome-wide fragmentation patterns - including fragment length distributions, end motifs, nucleosome footprints, and copy number variations - cfDNA fragmentomics provides high-resolution insights into tumor-specific biological signals even at low tumor burden. This technology offers advantages over conventional mutation-based assays by capturing aggregate structural and epigenomic alterations without requiring prior knowledge of driver mutations. In non-small cell lung cancer (NSCLC), cfDNA fragmentomics enables early detection, discrimination of malignant pulmonary nodules, and post-surgical monitoring of minimal residual disease. Recent studies have demonstrated that fragmentomic risk scores can accurately stratify recurrence risk and improve prognostic sensitivity beyond traditional genomic assays. In hepatocellular carcinoma (HCC), integration of fragment size selection, CNV profiling, and end-motif analysis has led to high-performing models for early diagnosis, particularly in high-risk populations. Moreover, cfDNA fragmentomics has proven effective in detecting malignant transformation in patients with neurofibromatosis-associated peripheral nerve sheath tumors, distinguishing benign from premalignant or malignant lesions with high precision. Expanding beyond these major cancers, fragmentomic approaches have demonstrated diagnostic potential in gastric, urological, hematologic, and pediatric malignancies. Notably, the DELFI-TF (DNA Evaluation of Fragments for early Interception-Tumor Fraction) framework has shown prognostic relevance by correlating pre-treatment cfDNA features with survival outcomes in colorectal and lung cancer patients, outperforming conventional imaging. All of these results highlight the translational importance of cfDNA fragmentomics as a cutting-edge precision oncology tool. Its continued integration into clinical workflows may redefine early cancer detection, facilitate subtype-specific interventions, and enable real-time, individualized treatment monitoring.
PMID:40977920 | PMC:PMC12444407 | DOI:10.62347/EBRY4326
Fluctuating DNA methylation tracks cancer evolution at clinical scale
Nature, Published online: 10 September 2025; doi:10.1038/s41586-025-09374-4
Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.
Organoids in Genetic Disorders: from Disease Modeling to Translational Applications
Stem Cell Rev Rep. 2025 Sep 11. doi: 10.1007/s12015-025-10973-x. Online ahead of print.
ABSTRACT
The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g., brain, eye, kidney, lung, and heart), highlighting their pivotal roles in identifying novel pathogenic genes, elucidating disease mechanisms, and advancing therapeutic strategies such as drug screening platforms, gene-editing therapies, and organ transplantation strategies. Furthermore, we critically address current limitations-including challenges in recapitulating complex pathologies and scaling production-while underscoring their potential for personalized medicine through multi-omics integration and bioengineering innovations. Although the scope of "genetic diseases" is broad, this synthesis focuses on disorders with well-defined inheritance patterns, such as monogenic disorders, copy number variations (CNVs), and aneuploidies. Despite covering only a subset of these conditions, this review aims to provide researchers with a comprehensive overview of the field, emphasizing how organoid-based approaches could accelerate both mechanistic discoveries and clinical translation in genetic disease research.
PMID:40931310 | DOI:10.1007/s12015-025-10973-x
Extended Insights Into Advancing Multi-Omics and Prognostic Methods for Cancer Prognosis Forecasting
Front Biosci (Landmark Ed). 2025 Aug 30;30(8):44091. doi: 10.31083/FBL44091.
ABSTRACT
Zhang et al.'s recent article utilizes comprehensive single-cell data to identify differences in tumor cell populations, highlighting the CKS1B+ malignant cell subcluster as a potential target for immunotherapy. It develops a prognostic and immunotherapeutic signature (PIS) based on this subcluster, demonstrating good performance in predicting lung adenocarcinoma (LUAD) prognosis. The study also validates the role of PSMB7 in LUAD progression. However, there are areas for improvement. There is a lack of clarity regarding the relationship between the CKS1B+ malignant cell subcluster and the PIS, particularly in terms of why PSMB7 was selected for functional studies. The sequencing data are retrospectively obtained from public databases and lack prospective clinical validation. It is suggested to collect LUAD patient tissues for RT-qPCR and RNA-seq analysis and seek external multi-center validations. Additionally, integrating emerging multi-omics methods is recommended to further validate the findings. Despite these limitations, the study represents progress in understanding LUAD and treatment strategies, and continuous evaluation and refinement of multi-omics and machine learning methods are expected for future research and clinical practice.
PMID:40917070 | DOI:10.31083/FBL44091
Building the world’s first truly global medical foundation model
Nature Medicine, Published online: 08 September 2025; doi:10.1038/s41591-025-03859-5
Building the world’s first truly global medical foundation model
GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer
bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.
ABSTRACT
Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.
PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519
GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer
bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.
ABSTRACT
Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.
PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519
