Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 63 appraisals
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Genetics versus environment in the pathophysiology of sagittal synostosis
PURPOSE: This systematic review aims to provide an updated overview of the relative contribution of germline genetic and environmental factors to the pathophysiology of sagittal craniosynostosis (sCS). METHODS: Using the PubMed database in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant studies addressing genetic and/or environmental determinants of sCS in paediatric patients were systematically reviewed. RESULTS: A total of 1238 records were identified, of which 97 studies met inclusion criteria after full-text review. Overall, 25,877 patients were included, with 11,086 diagnosed with sCS (42.8%). Germline genetic variants potentially associated with craniosynostosis were reported in 251 patients, accounting for 12.6% of genetically tested individuals with sCS. Identified variants were distributed across 125 genes, with recurrent findings in 25 genes. Environmental and perinatal factors were more consistently reported across large population-based and case-control studies. Frequently associated factors included male sex, advanced maternal age, maternal smoking, thyroid dysfunction, fertility treatments, foetal constraint, prematurity, and abnormal birth weight, although effect sizes varied across studies. CONCLUSION: Identifiable germline genetic causes appear to account for only a minority of sCS cases, whereas epidemiological evidence suggests that environmental and perinatal influences may also contribute to disease pathogenesis. Nevertheless, most cases lack identifiable germline mutations in key signalling pathways, and the available evidence does not support either genetic or environmental factors as individually deterministic drivers of disease development. Overall, the evidence synthesized in this review supports a multifactorial model for sCS, in which rare genetic susceptibility and non-genetic influences likely interact in the etiopathogenesis of the condition rather than reflecting a single dominant aetiology. However, heterogeneity across studies, differences in genetic testing methodologies, potential bias in exposure assessment, and the limited availability of integrative analyses restrict definitive conclusions regarding the relative contribution of these factors.
19 July 2026
Read appraisal →Nucleic acids research
FABIAN-variant 2026: improved prediction of the effects of DNA variants on transcription factor binding
Variants in promoters and enhancers can alter the binding of transcription factors (TFs), but their functional assessment remains difficult. FABIAN-variant is a web application that predicts the effects of DNA variants on TF binding by comparing position weight matrix (PWM) and transcription factor flexible model (TFFM) scores between reference and variant alleles. Here, we present FABIAN-variant 2026, a major update that expands the prediction model library from ~5000 to over 40 000 models for >1500 human TFs, sourced from nine PWM databases and including 1290 TFFMs. The application now supports the mouse genome (GRCm38 and GRCm39) with over 35 000 models for >1100 mouse TFs. An optional BPNet deep learning scorer provides neural network-based binding predictions for 240 human TFs. Known TF binding site information has been expanded from three to five sources. Predictions for over 1400 heterodimer TF complexes have been added. The web server has been rewritten in Rust and the scoring engine optimized, reducing runtime by ~70%. A RESTful JSON API and a standalone command-line version enable programmatic access and local high-throughput analysis. FABIAN-variant 2026 is available at https://fabianapp.org/variant26/. The web server is free and open to all users and there is no login requirement.
13 July 2026
Read appraisal →BioEssays : news and reviews in molecular, cellular and developmental biology
AI in Genomics: From Variant Calling to Multi-Omics Integration
Artificial intelligence (AI) strategies are revolutionizing genomics by extracting complex patterns that traditional statistical pipelines are likely to miss. This mini-review aims to provide a concise overview of how AI is transforming major genomic technologies including variant calling, gene expression analysis, single-cell transcriptomics, CRISPR-Cas9 optimization, and multi-omics integration. In genome sequencing, machine learning variant callers greatly improve the accuracy and the rate at which single nucleotide and structural variants are called. In bulk RNA-Seq, AI augmented quantification, denoising, and differential expression modules complement the highly established STAR-featureCounts-DESeq2 pipeline, revealing subtle signals in big data sets. In single cell transcriptomics, deep learning approaches enhance batch correction, automate cell type annotation, and track developmental trajectories, hence clarifying cellular heterogeneity. AI-assisted guide RNA design, outcome prediction, and nuclease engineering enable more efficient CRISPR-Cas9 editing, reducing experimental cycles, and off-target effects. Finally, integrated platforms that combine genomic, transcriptomic, epigenomic, proteomic, and metabolomic layers provide an integrative view of cellular regulation and disease mechanisms. The review also covers current limitations, sparsity of data, model bias, privacy, and the need for standardized benchmarks and offers future directions in the form of interpretable models, collaborative learning, and open science practices. Together, these developments render AI an indispensable partner to unravel genomic complexity and accelerate precision medicine applications.
3 July 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
Disparate language and model effects on AI-based translation and recognition of genetic conditions.
INTRODUCTION: Artificial intelligence (AI) is increasingly prevalent. Patients and clinicians may use AI-based tools in many different languages. OBJECTIVE: To investigate AI translation tools for descriptions of genetic conditions and how AI identification of genetic conditions is affected by translations. MATERIALS AND METHODS: We used Neural machine translation (NMT) and large language-model (LLM) translation to translate descriptions of 40 genetic conditions into 191 and 93 languages, respectively. Excluding translations retaining English medical terms verbatim, we respectively focused on 139 and 70 languages. After assessing translations, we assessed the ability of 3 proprietary and 3 open-weight general LLMs to identify conditions in the translations. We analyzed how accuracy was affected by the conditions' prevalence in the literature, and attributes of the languages (the script, language family, and prevalence of the language in training sources). We also investigated adaptive translation for select languages. RESULTS: We found significant differences in condition identification based on the translation method, condition, language, and prediction model. The accuracy of some models was more affected than others by factors like the conditions' literature prevalence, language script, family, and language prevalence. Adaptive translation for select languages did not improve translations or diagnostic accuracy with the 3 tested LLMs. However, further analysis with 1 language showed that this approach was more effective with smaller LLMs. CONCLUSIONS: AI-based translation has variable performance, which can affect the ability of AI models to recognize genetic conditions. These findings should inform safe medical AI use to support consistent performance in different languages.
2 July 2026
Read appraisal →IEEE transactions on pattern analysis and machine intelligence
50 Years of Automated Face Recognition
Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, and in many cases exceed, human performance. This paper traces the historical and technological evolution of FR, encompassing early algorithmic paradigms through to contemporary neural systems trained on extensive real and synthetically generated datasets. We examine pivotal innovations that have driven this progression, including advances in dataset construction, loss function formulation, network architecture design, and feature fusion strategies. Furthermore, we analyze the relationship between data scale, diversity, and model generalization, highlighting how dataset expansion correlates with benchmark performance gains. Recent systems have achieved near-perfect large-scale identification accuracy, with the leading algorithm in the latest NIST FRTE 1:N benchmark reporting a False Negative Identification Rate (FNIR) of 0.15 percent at False Positive Identification Rate (FPIR) of 0.001 on a gallery of over 10 million identities. Larger galleries increase false positive rates and deployments at greater scales will see higher error rates. We delineate key open problems and emerging directions, including scalable training, multi-modal fusion, synthetic data, and interpretable recognition frameworks.
1 July 2026
Read appraisal →Molecular biology reports
Update of Mexican population databases for complex kinship interpretation with twelve and seven X-STRs
BACKGROUND: X-chromosome short tandem repeats (X-STRs) are valuable for resolving complex kinship cases. Most population databases are based on the ArgusX12 kit (Qiagen), using PCR and capillary electrophoresis (CE) to identify length-based (LB) alleles, and are formatted for FamlinkX software. However, haplotype diversity is often underrepresented by population studies, compelling database updates ( https://famlink.se/fx_databases.html ). The ForenSeq DNA Signature Prep kit (Verogen) uses massive parallel sequencing (MPS) to analyze 7 X-STRs and report sequence-based (SB) alleles, for which population databases are currently unavailable. Because these 7 markers are a subset of the ArgusX12 panel, existing LB-based databases can support kinship interpretation of MPS-derived data. METHODS AND RESULTS: We therefore updated a Mexican population database for 12 X-STRs and generated a corresponding 7-marker subset with FamlinkX-compatible files. Using 500 haplotypes from the Investigator Argus X-12 kit QS (Qiagen), we estimated forensic parameters and evaluated the impact of the updated database by calculating likelihood ratios (LRs) in four representative kinship scenarios. The updated database (n = 1433) increased LRs by an average of 55% compared to the previous version (n = 933). CONCLUSIONS: These results support improved forensic interpretation in Mexican and Latin American populations lacking comprehensive national X-STR databases based on ArgusX12 and ForenSeq DNA Signature kits.
29 June 2026
Read appraisal →Familial cancer
When screentime fails: initiative to improve completion of hereditary cancer genetic testing after telemedicine counseling.
Telemedicine has broadened access to genetic counseling while maintaining high levels of patient satisfaction. However, emerging evidence suggests lower completion rates of genetic testing following virtual visits compared with in-person encounters. This study evaluated genetic testing completion rates among patients who underwent cancer risk assessment via telemedicine and assessed the effect of a structured follow-up telephone reminder on genetic testing completion. We conducted a retrospective review of a quality improvement initiative - Genetic Engagement via Navigation and Encouragement (GENE CALL) - designed to address incomplete genetic testing following telemedicine counseling. The study included all patients who underwent virtual cancer risk assessment between September 2023 and July 2024 at a single urban academic cancer genetics program. Patients who expressed interest in genetic testing but had not scheduled specimen collection were contacted by telephone as a reminder. During the call, patients were asked about ongoing interest in testing and barriers to scheduling; they were also offered assistance completing specimen collection. Among 647 patients evaluated via telemedicine, 469 (72.5%) expressed interest in genetic testing. Of these, 290 (61.8%) had scheduled or completed testing, while 179 (38.2%) had not. Among those who had not scheduled testing, 120 (67.0%) were successfully reached by telephone, and 98 (81.7%) confirmed ongoing interest in completing testing. At 3-month follow-up, 48 (40.0%) of the 120 contacted patients had completed testing, compared with 13 (22.0%) of 59 patients who were not reached (p = 0.017). Telephone follow-up is an effective strategy to improve genetic testing completion after virtual counseling.
27 June 2026
Read appraisal →BMC cardiovascular disorders
Clinical spectrum, cardiac phenotypes, and outcomes of FHL1-related cardiomyopathies: a systematic review
BACKGROUND: Mutations in the Four-and-a-Half LIM Domains 1 (FHL1) gene are increasingly recognized as a rare cause of inherited cardiomyopathies, often associated with skeletal myopathy and adverse cardiac outcomes. The phenotypic spectrum and clinical implications of FHL1 variants remain poorly defined. OBJECTIVE: To systematically review published cases of FHL1-related cardiomyopathy and characterize the clinical, genetic, and pathological features. METHODS: We conducted a systematic literature search in PubMed and EMBASE up to July 2025 using predefined criteria to identify studies reporting clinical cases of patients with FHL1 mutations and cardiac involvement. Data on genotype, phenotype, cardiac and neuromuscular features, and clinical outcomes were extracted and synthesized. RESULTS: Twenty-two studies were included, comprising 114 patients with pathogenic or likely pathogenic FHL1 mutations. Most patients were male (69%), with a median age of onset of 18 (IQR 10-26) years. Cardiac involvement consisted in left ventricular hypertrophy (56%), followed by arrhythmias (51%), and conduction abnormalities (8%). The incidence of sudden cardiac death was 7%, and heart transplantation was reported in 5% of patients. Skeletal muscle involvement was present in 75%, ranging from mild contractures to more severe myopathic phenotypes with functional impairment. Creatine kinase levels were variably elevated. Truncating variants were reported in several severe cardiac presentations in young males, while isolated cardiac disease occurred with selected variants. CONCLUSIONS: FHL1-related cardiomyopathy is a rare but important diagnosis. Genetic testing should be considered in patients with cardiac hypertrophy and neuromuscular features. Further research is needed to define prognostic markers and guide management.
19 June 2026
Read appraisal →BMJ open
Effect of a culturally adapted heart-healthy diet with phytosterols and/or krill oil on lipid-related outcomes in familial hypercholesterolaemia: protocol for a multicentre randomised controlled trial in Brazil
INTRODUCTION: An affordable heart-healthy dietary approach is essential for the management of familial hypercholesterolaemia (FH); however, the optimal dietary pattern and the role of adjunctive nutrient supplementation remain uncertain. This study aims to evaluate the effects of the Brazilian Cardioprotective Diet (DICA Br), adapted from the Portfolio Diet, with or without phytosterol and/or krill oil supplementation in individuals with probable or definite FH according to the Dutch Lipid Clinic Network (Dutch MEDPED) criteria. METHODS AND ANALYSIS: The DICA-FH study is a national, multicentre, randomised, factorial, parallel-group, superiority, placebo-controlled clinical trial with a 1:1:1:1 allocation ratio. Participants aged ≥16 years receiving age-appropriate lipid-lowering therapy will be randomised into four groups: (1) adapted cardioprotective diet (DICA-FH) plus phytosterol placebo and krill oil placebo; (2) DICA-FH plus phytosterol 2 g/day and krill oil placebo; (3) DICA-FH plus phytosterol placebo and krill oil 2 g/day or (4) DICA-FH plus phytosterol 2 g/day and krill oil 2 g/day. All participants will undergo whole-genome sequencing and receive appropriate genetic counselling. Primary outcomes will be means of low-density lipoprotein cholesterol and lipoprotein(a) levels after 120 days. Secondary outcomes will include additional lipid biomarkers, adherence to protocol and adverse events. The planned sample size is 300 participants. Follow-up is expected to conclude in July 2026. ETHICS AND DISSEMINATION: This study was registered under CAAE 65549622.2.1001.0060 and received ethical approval from the Hcor Research Ethics Committee (approval number 5.805.072) and the Brazilian National Research Ethics Commission (CONEP; approval number 6.864.951). Written informed consent will be obtained from all participants prior to enrolment. The study findings will be disseminated through peer-reviewed publications, scientific conferences and channels aimed at the general public. TRIAL REGISTRATION NUMBER: NCT06331195.
14 June 2026
Read appraisal →Placenta
Recent advances in multimodal approaches for investigating placental development and related diseases
The placenta is a vital organ that supports fetal growth and pregnancy maintenance. Its dysfunction is associated with severe complications, including preeclampsia, fetal growth restriction, and placenta accreta spectrum disorders. Traditional approaches to placental research have provided valuable insights but are limited in capturing the complexity of this dynamic organ. In recent years, multimodal approaches-integrating advanced imaging, single-cell and spatial omics, and artificial intelligence (AI)-have enabled comprehensive analyses of placental development and disease. These strategies offer improved diagnostic accuracy, deeper molecular understanding, and real-time assessment of placental function. This review summarizes recent advances in multimodal placental research, highlighting key technologies such as ultrasound and MRI, single-cell transcriptomics, spatial profiling, and AI-based prediction models. Particular emphasis is placed on contributions from Chinese research teams, who have developed novel platforms, atlases, and clinically relevant tools. We also discuss ongoing challenges, including data standardization, interpretability of AI models, and ethical considerations. Looking ahead, the integration of multimodal data with AI and wearable technologies holds promise for precision obstetrics and individualized pregnancy care. Together, these innovations are advancing both scientific understanding and clinical management of placenta-related disorders.
12 June 2026
Read appraisal →Journal of theoretical biology
Subtypes estimation in single-cell multi-omics data via S-multi-SNE
Single-cell RNA sequencing (scRNA-seq) has rapidly become an essential method in modern biology, as it allows global characterisation of transcriptomics in individual cells from different tissues, or organisms. scRNA-seq data are collected from a single individual organism and they are composed as mRNA count matrices with rows representing the cells and columns the genes. New technologies further allow multi-omics data to be obtained on the same set of single cells. In this paper, multi-SNE and S-multi-SNE, two multi-view dimensionality reduction approaches, are adapted for visualization and classification of cellular subtypes, two important challenges in the analysis of scRNA-seq data. S-multi-SNE has been adapted for cell subtype estimation of scRNA-seq data. In a series of experiments we illustrate that it is possible to identify cell subtypes by leveraging information from individuals from the same species, from different species, and when data are generated by different sequencing technologies. In the conducted analyses, we show that S-multi-SNE consistently outperformed several other machine learning techniques and, in most comparisons, surpassed existing reference-based single-cell classification algorithms. We further illustrate how two single-cell multi-omics datasets, scATAC-seq and scRNA-seq, can be integrated together through S-multi-SNE for improved cell subtype identification.
7 June 2026
Read appraisal →PLoS computational biology
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing
Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders.
1 June 2026
Read appraisal →Neurology
Vamorolone for Duchenne Muscular Dystrophy: A Cross-Trial Efficacy Comparison With Classic Corticosteroids From the FOR-DMD Trial
BACKGROUND AND OBJECTIVES: Vamorolone demonstrated similar efficacy for Duchenne muscular dystrophy (DMD) compared with prednisone in a 24-week exploratory analysis and may reduce key side effects compared with classic corticosteroids. In this study, we compare the efficacy and anthropometric effect of vamorolone 6 mg/kg/d with prednisone 0.75 mg/kg/d and deflazacort 0.9 mg/kg/d in steroid-naïve boys aged 4 to <7 years using data from 2 trials. METHODS: VISION-DMD was a phase 2b, 48-week randomized, double-blind trial assessing vamorolone 2 and 6 mg/kg/d vs placebo and prednisone 0.75 mg/kg/d to 24 weeks. Finding the Optimum Steroid Regimen for DMD (FOR-DMD) was a double-blind, parallel-group randomized trial comparing daily prednisone 0.75 mg/kg/d, daily deflazacort 0.9 mg/kg/d, and intermittent prednisone 0.75 mg/kg (10/10 days on/off). Entropy balancing generated weighted data for mixed model for repeated measures analyses that compared VISION-DMD vamorolone 6 mg/kg/d efficacy and anthropometric outcomes with FOR-DMD prednisone and deflazacort outcomes at 6 and 12 months. Inclusion criteria were boys with genetically confirmed DMD, age 4 to <7 years, ambulatory, and able to complete a time-to-stand (TTSTAND) assessment in <10 seconds at baseline. RESULTS: All interventions showed motor improvements relative to baseline at 6 and 12 months. Using the weighted cohorts and at 12 months, vamorolone 6 mg/kg/d (n = 28) had similar changes in TTSTAND velocity, the primary motor endpoint component in both trials, vs daily prednisone (n = 50) or deflazacort (n = 55; mean baseline age 5.42 years all groups), but with CIs overlapping minimal clinically important thresholds of 0.023 rises per second (TTSTAND velocity least squares mean [LSM] difference [95% CI]: vamorolone 6 mg/kg/d vs prednisone 0.75 mg/kg/d, 0.004 rises per second [-0.025 to 0.032] and vs deflazacort 0.9 mg/kg/d, 0.001 rises per second [-0.027 to 0.028]). Body mass index (BMI) Z-scores increased in all groups, greatest with vamorolone (12-month LSM difference [95% CI]: vamorolone vs prednisone 0.57 [0.24-0.90]; vamorolone vs deflazacort 0.29 [0.03-0.56]), whereas growth slowdown occurred in prednisone- and deflazacort-treated boys but not vamorolone (12-month LSM difference in height Z-scores [95% CI]: vamorolone vs prednisone 0.57 [0.24-0.90]; vamorolone vs deflazacort 0.72 [0.53-0.91]). DISCUSSION: Vamorolone demonstrated numerically similar TTSTAND velocity changes to prednisone and deflazacort at 1 year; however, interpretations of differences are limited by 95% CIs crossing minimally important difference thresholds. Further evidence of the growth-protective effect of vamorolone was observed; however, all treatments increased BMI. Vamorolone provides a linear growth-protective classic corticosteroid alternative. TRIAL REGISTRATION INFORMATION: NCT03439670; NCT01603407. CLASSIFICATION OF EVIDENCE: This Class III study did not definitively identify differences in efficacy between vamorolone and classic corticosteroids but found that vamorolone protects linear growth in boys with DMD.
30 May 2026
Read appraisal →Journal of the American College of Cardiology
Combining Genomics With Lipid and Inflammatory Biomarkers to Predict Coronary Artery Disease Risk: UK Biobank Study
BACKGROUND: Coronary artery disease (CAD) polygenic risk score (PRS), low-density-lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), and high-sensitivity C-reactive protein (hsCRP) are biomarkers that predict CAD. It is unclear whether integrating genomics with lipid and inflammatory biomarkers could complement traditional risk scores in identifying people at risk of CAD. OBJECTIVES: This study assesses the predictive value of CAD PRS, LDL-C, Lp(a), and hsCRP for incident CAD across different age and sex groups. METHODS: Participants (n = 215,695) from the UK Biobank aged 40 to 69 years with baseline CAD PRS, LDL-C, Lp(a), and hsCRP values were followed for 12 years to assess the incidence of CAD. We evaluated a multivariable-adjusted Cox model that included all 4 biomarkers, net reclassification index, C-statistics, and population attributable risk across different age and sex groups. RESULTS: Over a 12-year follow-up, 4,721 men and 2,425 women developed CAD. The HRs for incident CAD associated with each biomarker elevation were 1.79 (95% CI: 1.70-1.89) for CAD PRS, 1.60 (95% CI: 1.48-1.66) for LDL-C, 1.20 (95% CI: 1.12-1.29) for Lp(a), and 1.64 (95% CI: 1.57-1.72) for hsCRP. CAD PRS demonstrated a stronger association in men (HR per SD: 1.49; 95% CI: 1.45-1.54) than women (HR per SD: 1.37; 95% CI: 1.31-1.44; P-interaction ≤ 0.001). All biomarkers conferred greater HRs at younger ages (P < 0.0001). Individuals with all biomarkers elevated had a 4.65-fold increased risk of CAD compared with those with no elevated biomarkers. A combined 4-biomarker model had a higher C-statistic of 0.753 compared with the pooled cohort equations (C-statistic of 0.740). The C-statistic of the combined 4-biomarker model was also higher in younger individuals in both sexes and yielded a 32.0% continuous net reclassification index when compared with the pooled cohort equations. CONCLUSIONS: CAD PRS, LDL-C, hsCRP, and Lp(a) show independent age- and sex-specific associations with CAD. Measuring all 4 biomarkers may improve midlife CAD risk prediction for both male and female patients.
28 May 2026
Read appraisal →The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Integrated cell-free DNA and omics genetic scores for early detection of gestational diabetes: evidence from a nationwide multicenter study
BACKGROUND: Gestational diabetes mellitus (GDM) affects 15.6% of pregnancies globally, with Vietnam exhibiting one of the highest prevalences at 21%. Current diagnostic approaches at 24-28 weeks limit early intervention opportunities. We developed a multi-modal machine learning framework integrating cell-free DNA (cfDNA) structural features and genetic information for early GDM prediction at 10-12 weeks of gestation in Vietnamese women. METHODS: We analyzed blood samples from 1,086 pregnant women (435 GDM cases, 651 controls) collected at 9-12 weeks. Two parallel analytical pathways were employed: cfDNA profiling extracting cfDNA-specific features (fragment length, end motifs, GC content, nucleosome patterns), and whole-genome imputation generating predictions for ∼19,000 omics traits. Component scores were developed using TabPFN classifier and integrated via logistic regression into a unified master score. RESULTS: Genome-wide analysis identified five omics traits with significant GDM associations: HSD11B1, NEK7, COMMD10, KLRC4, and OCEL1. Component score optimization revealed distinct patterns-cfDNA scores peaked at 200 features (AUC = 71.53), while genetics-based scores improved with up to 2,000 omics traits (AUC = 77.21). The final master score, integrating three components (gbSC2000, gbSCBH, cfSC200), achieved AUCs of 86.82-87.19 across validation cohorts with 70% sensitivity and 89% specificity. Addition-deletion analysis confirmed that both cfDNA and genetic components provided essential, non-redundant contributions. CONCLUSIONS: This multi-modal framework demonstrates superior performance compared to single-biomarker approaches, enabling risk stratification from very low (4% GDM prevalence) to very high risk (90% prevalence). At the cutoff 0.4, the model identifies 78% of future GDM cases at 10-12 weeks while maintaining an 18% false-positive rate, potentially enabling early interventions to prevent GDM development and associated complications.
27 May 2026
Read appraisal →Clinical and experimental dermatology
An open-label phase II study of bitopertin in adults and adolescents with erythropoietic protoporphyria or X-linked protoporphyria
BACKGROUND: Erythropoietic protoporphyria (EPP) and X-linked protoporphyria (XLP) are rare genetic disorders characterized by accumulation of protoporphyrin IX (PPIX), painful phototoxic reactions and hepatobiliary disease. No disease-modifying therapies are approved for these conditions. OBJECTIVES: To evaluate the safety and efficacy of bitopertin in adults and adolescents with EPP or XLP. METHODS: BEACON (ACTRN12622000799752) was a phase II, randomized, open-label, parallel-arm study in adult and adolescent participants with EPP or XLP. Participants received an oral, once-daily administration of bitopertin 20 mg or 60 mg for 24 weeks. RESULTS: Twenty-six participants were randomized to 20 mg (n = 14) or 60 mg (n = 12) of bitopertin. The study met its primary efficacy endpoint. Treatment with bitopertin resulted in significant, dose-dependent reductions in PPIX levels compared with baseline at both the 20-mg and 60-mg dose levels (mean -31.7%, SE 7.0; P < 0.001 vs. baseline and mean -57.7%, SE 7.4; P < 0.001 vs. baseline, respectively). Similar efficacy was observed across the adult and adolescent populations. No serious adverse events were reported. Dizziness was the most common adverse event reported with bitopertin, in 9 (64%) and 8 (67%) participants completing the study in the 20-mg and 60-mg dose groups, respectively. CONCLUSIONS: By reducing whole-blood PPIX levels, bitopertin targets the underlying pathophysiology of EPP, resulting in consistent improvements in multiple measures of light tolerance and quality of life. Bitopertin was well tolerated, and most adverse events were mild or moderate in severity.
27 May 2026
Read appraisal →Medicine
Mechanisms linking primary biliary cholangitis and osteoporosis: A combined clinical and molecular analysis
Primary biliary cholangitis (PBC) is an immune-mediated cholestatic liver disease, and osteoporosis (OP) is a prevalent comorbidity that aggravates the disease burden of PBC patients. Although the co-occurrence of the 2 diseases has been widely observed, the underlying molecular mechanisms remain unclear. Herein, we evaluated the causal link between PBC and OP using Mendelian randomization (MR) and explored shared molecular mechanisms through bioinformatics and machine learning. We performed two-sample MR using genome-wide association study summary data for PBC and OP from the Integrative Epidemiology Unit database. Inverse-variance weighting was used as the primary MR method, with heterogeneity and horizontal pleiotropy tests performed to exclude potential biases. To explore shared molecular mechanisms, we analyzed transcriptomic datasets from the Gene Expression Omnibus database, identified comorbidity-associated differentially expressed genes, and applied multiple machine learning algorithms for biomarker screening and validation, combined with immune infiltration analysis. Our study showed that MR demonstrated that PBC significantly increases the risk of OP, while transcriptomic analysis identified 36 shared differentially expressed genes enriched in key biological pathways such as ribonucleic acid splicing and ubiquitin-mediated proteolysis. Furthermore, using 3 machine learning algorithms, we identified 12 PBC-specific and 4 OP-specific diagnostic genes, whose intersection revealed vacuolar protein sorting 37 homolog C (VPS37C) as a common diagnostic biomarker. In both diseases, VPS37C exhibited an area under the receiver operating characteristic curve value >0.7, demonstrating its robust predictive performance. In addition, VPS37C expression was found to be significantly correlated with the infiltration landscape of multiple immune cell types in both PBC and OP. This study identified VPS37C as a shared diagnostic gene linking PBC and OP, providing new insights into their comorbidity at both genetic and immune levels. Our findings further elucidate the molecular mechanisms underlying the comorbidity of PBC and OP, offer novel clues for understanding their pathogenesis, and highlight promising diagnostic and therapeutic targets for clinical application.
24 May 2026
Read appraisal →Postgraduate medical journal
Next-generation cancer therapies: translating experimental advances into clinical practice
Recent advancements in cancer therapy have led to the emergence of innovative approaches that offer new hope to patients. This review provides a comprehensive overview of the latest cutting-edge technologies and strategies in cancer treatment, including clinically approved therapies and experimental modalities in preclinical or early clinical development, with a focus on their potential to improve patient outcomes. We examined next-generation therapies, including personalized immunotherapies, targeted molecular treatments, gene editing approaches, and artificial intelligence-driven strategies. By evaluating the current landscape of these therapies, we highlighted their benefits, limitations, and future directions. The integration of these advanced modalities into clinical practice holds promise for enhancing their efficacy, reducing side effects, and ultimately transforming cancer care.
22 May 2026
Read appraisal →Genome medicine
DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification
BACKGROUND: Machine-learning (ML) driven molecular diagnostics based on omics data has a potential to revolutionize personalized medicine. However, implementation of ML into diagnostic protocols is hindered by methodological challenges which often lead to inflated performance assessment of models during development followed by poor performance of these models in implementation phase. Here, we aimed to develop and validate a pan-cancer classification framework based on DNA methylation data, that addresses methodological challenges of omics data powered ML. METHODS: We curated a primary dataset of DNA methylation profiles for 10756 samples, that included 54 healthy and cancer tissue types and validation dataset comprising data for 2306 samples from 28 independent studies. The classification framework was build using custom biomarkers selection strategy based on effect size metric that considers variance and class imbalance. The ML models were trained, tuned and evaluated using nested cross-validation approach. Local outlier factor algorithm was built into the inference pipelines to identify and filter samples displaying technical or biological anomalies. Additionally, for methodological validation of our framework we used methylation profiles for 3905 central nervous system (CNS) tumors. RESULTS: We found that relatively simple ML models outperformed complex algorithms such as deep neural network. A logistic regression classifier achieved a balanced accuracy (BACC) of 0.90 to classify 54 cancer and healthy tissue types using methylation levels at 1208 CpG sites. Similarly, our CNS tumor classifier also based on logistic regression algorithm reached a BACC of 0.94 across 59 CNS tumor subtypes. The anomaly filtering improved performance across all categories of samples tested. CONCLUSIONS: Our study demonstrates that DNA methylation profiling, when combined with carefully controlled ML practices allows for development of robust solutions that might substantially increase the efficacy of oncological diagnosis. Finally, we deployed our inference pipelines for public access via secure web platform - https://opp.pum.edu.pl/ .
21 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Tissue-Free Circulating Tumor DNA Assay and Patient Outcome in a Phase III Trial of FOLFOX-Based Adjuvant Chemotherapy (Alliance N0147)
PURPOSE: Detection of molecular residual disease using circulating tumor DNA (ctDNA) may enable postoperative risk stratification and guide adjuvant therapy. We evaluated the prognostic value of a tissue-free, epigenomic ctDNA assay in patients with stage III colon cancer (CC) enrolled in a phase III adjuvant chemotherapy trial. METHODS: Plasma samples were collected after surgery and before adjuvant infusional fluorouracil, leucovorin, and oxaliplatin alone or combined with cetuximab. ctDNA was analyzed using a tissue-free assay; in ctDNA-positive patients, tumor fraction (TF) was quantified and genotyping was performed with a 739-gene panel. Associations with disease-free survival (DFS), time to recurrence (TTR), and overall survival (OS) were assessed using multivariable Cox models adjusted for covariates. RESULTS: Among 2,260 evaluable patients, 461 (20.4%) were ctDNA-positive with significantly higher detection in advanced T-/N-stage, high-grade, obstruction/perforation, and BRAFV600E tumors. At a median follow-up of 6.1 years, ctDNA positivity was independently associated with shorter TTR (hazard ratio [HR], 5.96 [95% CI, 5.11 to 6.96]), DFS (HR, 5.03 [95% CI, 4.36 to 5.81]), and OS (HR, 4.45 [95% CI, 3.76 to 5.27]; all P < .0001). The 5-year DFS was 27.7% (95% CI, 23.8 to 32.2) v 77.1% (95% CI, 75.1 to 79.1) in ctDNA-positive versus ctDNA-negative patients, and adverse prognostic impact was greater in lower T/N stage, low-risk, and dMMR subsets (interaction P = .0012-.041). Among ctDNA-positive patients, TF was nearly double in those who recurred or died (P = .0002) and stratified patients for TTR, DFS, and OS (all adjusted P < .002). Genotyping identified mutations in FLT1 (OR, 8.99) and PREX2 (OR, 7.73) genes that were most strongly associated with recurrence (P < .03). CONCLUSION: Evaluation of ctDNA in resected stage III CC using a tissue-free assay provided robust and independent prognostic value. Higher ctDNA burden, dMMR, and specific mutations defined poor prognostic groups among ctDNA-positive patients.
20 May 2026
Read appraisal →Cell reports methods
A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer
Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using gene expression signatures, or integrative multi-omics frameworks, robust and interpretable biomarkers remain limited. We present SubNetDL, a deep learning framework that integrates subclonal mutation profiles and protein-protein interaction networks via network propagation. Unlike condition-specific approaches, SubNetDL leverages somatic mutations alone and is applicable across diverse cancer types and treatment modalities. Applied to 10 TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median area under the receiver operating characteristic curve [AUROC] = 0.74) and successfully generalized to two independent immunotherapy datasets (median AUROC = 0.77). Importantly, it identified candidate biomarker genes with treatment-specific relevance. SubNetDL prioritized genes that were not central in the network, highlighting its ability to capture context-specific patterns beyond traditional metrics. In conclusion, our approach offers a robust and interpretable framework for identifying predictive biomarkers and stratifying patients based on mutation profiles and network context.
20 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Diazoxide Choline Extended-release Tablets in Prader-Willi Syndrome: A Randomized, Double-blind, Withdrawal Period Study
CONTEXT: The hallmark condition of Prader-Willi syndrome, a rare, genetic neurobehavioral/metabolic disorder, is life-threatening hyperphagia. OBJECTIVE: We assessed the efficacy and safety of diazoxide choline extended-release (DCCR) tablets for the treatment of hyperphagia in adults and children four years of age and older with Prader-Willi syndrome. METHODS: We conducted a 16-week, randomized withdrawal study in children and adults with Prader-Willi syndrome and hyperphagia. Participants who previously completed randomized (13-week DCCR or placebo) and open-label (2.5-4.5 years DCCR) studies were randomized 1:1 to receive once-daily DCCR or placebo. The primary endpoint was Hyperphagia Questionnaire for Clinical Trials (HQ-CT) total score change from baseline to 16 weeks. Secondary endpoints included Clinical Global Impression of Severity (CGI-S) and Improvement (CGI-I); exploratory endpoints included weight and body mass index (BMI) z-score. RESULTS: Seventy-seven participants were randomized (DCCR:38; placebo:39). Statistically significant increases in HQ-CT from baseline to week 16 were observed with placebo vs DCCR [least square (LS) mean (standard error) change 7.6 (1.09)] with placebo and 2.6 (1.12) with DCCR; P = .0022. CGI scores favored DCCR but were not significantly changed. Consistent with the hyperphagia response, the placebo cohort gained more weight and increased their BMI z-score more than the DCCR cohort [LS mean weight difference (95% confidence interval) -1.6 kg (-3.1, -0.1)]; LS mean z-score difference was -0.09 (-0.17, -0.01). Adverse events were similar with both treatments, with no serious adverse events in the DCCR arm. CONCLUSION: Continued DCCR treatment was superior to placebo for hyperphagia. DCCR appears to offer meaningful therapeutic benefits for people with Prader-Willi syndrome.
19 May 2026
Read appraisal →Physiological measurement
Machine learning-based information flow analysis of ECG signals for long QT syndrome
Objective. Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.Approach. Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.Results. The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.Conclusions.Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.
19 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Invasive lobular and ductal breast cancers: a systematic review and metanalysis in association with BRCA1/2 mutation status
BACKGROUND: Invasive lobular breast carcinoma (ILC) differs from invasive ductal breast carcinoma (IDC) with respect to genetic alterations and risk factors. We aimed to quantify differences in the prevalence of germline BRCA1/2 mutations between these two patients' populations. MATERIALS AND METHODS: We conducted a systematic literature search to extract data on the association between BRCA1/2 mutation status and breast cancer (BC) histological subtype (ILC and IDC). Summary odds ratios (SOR) and 95% CI were calculated using random effects univariate and bivariate models and between-study heterogeneity investigated through meta-regression and subgroup analyses. RESULTS: Twelve studies, published between 1998 and 2025, were considered, including 8004 BC women. BRCA1 mutations were significantly less frequent in patients with ILC than IDC (SOR=0.32, 95%CI (0.16-0.65)) whereas no association was found overall for BRCA2 (SOR=1.28, 95%CI (0.95-1.72)). The difference between the association with BRCA1 and BRCA2 was statistically significant (p-value<0.001). There was no indication of publication bias in BRCA1 (Egger's and Begg's test p-value=0.23, 0.12). The between study heterogeneity was 29% in BRCA1 and 0% in BRCA2. Excluding papers with high risk of bias, BRCA2 mutations were more frequent in patients with ILC than in IDC (SOR=2.56, 95%CI (1.15-5.7)). This association was primarily driven by the bivariate random-effects model, which accounts for the correlation between BRCA1 and BRCA2 estimates. CONCLUSIONS: In studies with lower risk of bias, germline BRCA2 mutations were more frequent in patients with ILC than IDC. Conversely, BRCA1 mutations are more frequently observed in IDC, particularly among younger patients and those with a positive family history of BC.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Explainable machine learning framework for the molecular classification of triple negative breast cancer
BACKGROUND AND OBJECTIVE: The difference in molecular characteristics of Triple negative breast cancer (TNBC) aids in distinguishing between its four prominent subtypes- basal-like 1, basal-like 2, mesenchymal, and luminal androgen receptor. This study presents the first integrative framework that combines explainable AI with machine learning approaches to classify TNBC subtypes. Unlike conventional models, our approach offers interpretability while enabling biomarker prioritization by identifying key hub genes that drive subtype-specific predictions. METHODS: In the experiment 783 cases (BL1 (160), BL2 (75), M (151), LAR (106), non-TNBC (291) reported in Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC) data portal were used for the analysis. The proposed framework comprises modules for the identification of gene signatures for the four-subtype followed by the classification model based on eight different machine learning algorithms. Random Forest classifier was found to be best model with 96 % testing accuracy, which was elected for Explainable framework using Shapley Additive Explanations. RESULTS: Explainable biomarker module could provide a set of 47 biomarkers which is relevant in distinguishing the four types on triple negative breast cancer. The biomarkers could have the potential to be considered for TNBC prognosis in clinical setting. CONCLUSION: Key findings highlight the hub genes CDC20, CDCA2, PIMREG, KIF2C, and CENPW, implicating pathways such as ubiquitin-proteasome signaling and microtubule dynamics. These insights pave the way for biomarker-driven therapies and precision medicine in triple negative breast cancer.
16 May 2026
Read appraisal →PloS one
Analyzing the performance of deep learning splice prediction algorithms
SpliceAI is the leading tool for predicting splice-altering variants, but restrictive licensing limits clinical adoption. While open-source implementations have been published with author-reported comparisons, independent benchmarking across diverse datasets is needed to establish equivalence. We compared the original SpliceAI with two open-source implementations (OpenSpliceAI and CI-SpliceAI) and a legacy ensemble baseline across six datasets: a curated set of 1,316 validated variants, 213 variants with splice-assay data, 99,601 variants from the SPiP splicing prediction study, 242 manually curated deep intronic pathogenic variants, and two ClinVar-derived datasets comprising 53,600 intronic variants and 58,064 variants spanning all genomic contexts. The deep learning models were also evaluated against an ensemble of four legacy splice-prediction tools. Across all datasets, the deep learning algorithms outperformed the legacy ensemble. All three deep learning algorithms showed similar performance on the larger datasets dominated by canonical splice site variants (balanced accuracies 0.889-0.977). On the deep intronic benchmark, the original SpliceAI achieved the highest balanced accuracy (0.940), outperforming both CI-SpliceAI (0.890) and OpenSpliceAI (0.841). Critically, optimal thresholds for deep intronic variants were an order of magnitude lower than standard recommendations, indicating that default thresholds would miss the majority of pathogenic deep intronic variants. A correlation analysis showed that CI-SpliceAI maintained balanced concordance across event types, whereas OpenSpliceAI showed stronger correlation for loss events than gain events. Both implementations showed high positional agreement with SpliceAI, with exact splice-site match rates exceeding 90% across event types. Together, these results demonstrate that both open-source reimplementations of SpliceAI successfully reproduce the predictive behavior of the original algorithm across multiple evaluation contexts, while consistently outperforming traditional splice prediction methods. However, performance diverges on deeply intronic variants, and standard score thresholds are poorly calibrated for this variant class regardless of algorithm choice.
15 May 2026
Read appraisal →European journal of pediatrics
Machine learning-based integration of respiratory oscillometry and spirometry for predicting clinical outcomes in spinal muscular atrophy
UNLABELLED: Assessing respiratory function in spinal muscular atrophy (SMA) is challenging due to the effort-dependent nature of traditional spirometry. While respiratory oscillometry offers a passive alternative, its clinical utility in predicting clinical status remains underexplored. This study evaluates the efficacy of integrating spirometric and respiratory oscillometric indices through machine learning (ML) to predict ambulatory status and the requirement for bilevel positive airway pressure (BiPAP) in SMA patients. We retrospectively analyzed forty-five patients with a genetically confirmed diagnosis of SMA. To address data imbalance, we utilized the SMOTETomek technique and validated Random Forest models through leave-one-out cross-validation. SHAP (SHapley Additive exPlanations) analysis was used to interpret the contribution of individual physiological markers. In univariate logistic regression, forced vital capacity (FVC), peak expiratory flow (PEF), and forced expiratory flow at 75% of vital capacity (FEF75) were significant predictors for walking support. FEF25, FEF50, FEF75, and FEF 25-75 were associated with the requirement for BiPAP use. None of the respiratory oscillometric parameters reached statistical significance in regression models. However, ML models achieved high predictive accuracy, with SMOTETomek improving BiPAP requirement recall from 0.00 to 1.00. SHAP analysis revealed that respiratory oscillometry gained significant predictive weight that traditional models overlooked. R20Hz emerged as a key predictor for ambulatory status, while R5-20 Hz was identified as a decisive feature for predicting the BiPAP use. CONCLUSION: The integration of effort-independent respiratory oscillometry with traditional spirometry via ML has the potential to enhance risk stratification in SMA. WHAT IS KNOWN: • SMA requires lung function monitoring, but traditional spirometry is challenging due to the need for forceful expiratory maneuvers. • Respiratory oscillometry provides an effort-independent assessment of respiratory mechanics through tidal breathing. WHAT IS NEW: • Machine learning successfully integrates effort-independent respiratory oscillometry with spirometry to significantly enhance clinical predictions in SMA. • It identifies R20Hz and R5-20 Hz as critical physiological markers for predicting ambulatory status and the requirement for respiratory support, respectively.
13 May 2026
Read appraisal →Journal of chemical information and modeling
Examining Genetic Variants Associated with FOXP1 Syndrome through Molecular Dynamics of Its DNA-Binding Domain and Self-Organizing Maps
Genetic mutations in the transcription factor FOXP1 (forkhead box protein P1) cause an autosomal dominant neurodevelopmental disorder called FOXP1 syndrome. To understand the structural impact of pathogenic variants associated with FOXP1 syndrome, we investigated the conformational changes resulting from six distinct missense variants in FOXP1 by combining molecular dynamics simulations, molecular docking, and machine learning via self-organizing maps. Our results reveal different conformational landscapes mapped by the FOXP1 variants and reduced interactions with the DNA for mutations residing in helix H3 of the DNA-binding domain. These analyses offer a framework for assessing the structural impact of missense variants implicated in the FOXP1 syndrome, highlighting the importance of structural inferences in interpreting genetic variants.
13 May 2026
Read appraisal →The New England journal of medicine
Base Editing of HBG1 and HBG2 Promoters for Sickle Cell Disease
BACKGROUND: Sickle cell disease is characterized by chronic hemolytic anemia and recurrent severe vaso-occlusive crises. Ristoglogene autogetemcel (risto-cel) includes autologous CD34+ hematopoietic stem and progenitor cells that have been base-edited to target the HBG1 and HBG2 promoters and inhibit BCL11A binding without altering BCL11A expression, yielding a switch in hemoglobin production from sickle hemoglobin (HbS) to antisickling fetal hemoglobin (HbF). METHODS: In this phase 1-2 study, we enrolled patients 12 to 35 years of age with sickle cell disease who had had at least four severe vaso-occlusive crises in the 2 years before enrollment. After myeloablative conditioning with pharmacokinetically guided administration of busulfan, patients received a single infusion of risto-cel (at a dose of ≥3.0×106 viable CD34+ cells per kilogram of body weight). The primary efficacy end point was freedom from severe vaso-occlusive crises for 12 consecutive months, starting later than 60 days after the last red-cell transfusion. This interim analysis was unplanned; here, we describe safety, editing, engraftment, and hemoglobin production and the number of severe vaso-occlusive crises starting later than 60 days after the last red-cell transfusion. RESULTS: A total of 31 patients received risto-cel and were followed for a mean of 6.6 months (range, 0.3 to 20.4). A median of one cycle (range, one to five) was required for stem-cell collection. Neutrophil engraftment occurred at a median of 17.5 days, and platelet engraftment at a median of 19 days. One patient died from idiopathic pneumonia syndrome. All 31 patients had at least one adverse event, 27 (87%) had an adverse event of grade 3 or higher, and 12 (39%) had a serious adverse event. At 6 months, the mean fraction of on-target edited alleles in peripheral blood was 67.4%, the mean HbF as a fraction of total hemoglobin was more than 60%, and the HbS as a fraction of total hemoglobin was less than 40% (among 13 patients); these levels were maintained throughout follow-up. No investigator-reported severe vaso-occlusive crises occurred later than 60 days after the last red-cell transfusion. CONCLUSIONS: Treatment with risto-cel was followed by rapid engraftment and durable expression of HbF and reduction in HbS. These data support further investigation of risto-cel to treat sickle cell disease. (Funded by Beam Therapeutics; BEACON ClinicalTrials.gov number, NCT05456880.).
12 May 2026
Read appraisal →Analytical chemistry
FiLM-Enhanced Biologically Informed Neural Networks for Multiclass Omics Analysis and Biomarker Discovery
High-throughput proteomics enables detailed molecular phenotyping but poses challenges for predictive modeling and interpretation due to high dimensionality, sparsity, and nonlinear interactions. Biologically informed neural networks (BINNs) address these challenges by embedding pathway knowledge into network architectures, providing interpretable models of complex omics data. We present BINN-FiLM, which extends conventional BINNs by integrating feature-wise linear modulation, enabling task-specific scaling and shifting of pathway-level activations while preserving a fixed Reactome-based hierarchy. Unlike BINNs that share all pathway parameters across classes, BINN-FiLM captures class-dependent biological rewiring, making it particularly effective for multiclass classification tasks. Multiclass problems are reformulated as multitask binary learning, allowing pathway activations to adapt to disease-specific contexts. We evaluated BINN-FiLM on three multiclass proteomic data sets, and BINN-FiLM consistently outperformed conventional BINNs and standard machine learning models. FiLM modulation parameters revealed task-specific pathway activity, and SHAP-based interpretation identified key proteins and pathways driving disease discrimination.
11 May 2026
Read appraisal →Journal of medical virology
A Triage Model for HR-HPV Positive Women Based on Methylation of Human and HPV Genes
The aim of this study is to develop a machine learning triage model for HR-HPV positive women based on methylation patterns of human and HPV genes to predict the risk of CIN2 +, and to compare its performance with traditional detection techniques. A total of 512 eligible women participated in the study, with exfoliated cervical cell specimens collected for methylation analysis of both human and HPV genes. Highly methylated gene fragments related to cervical cancer were identified through high density, high association, high MHL (Methylation Haplotype Load) screening and Logistic regression. The samples were divided into a training set and a validation set at a ratio of 7:3, and a machine learning triage model was constructed using CIN2+ as the outcome. Among 512 HR-HPV positive women, 174 (34.0%) had CIN2 +. To construct the random forest model, 10 high methylation human gene fragments were identified from a pool of 45 genes, along with HPV 16, 18, and 52 L2 genes. In the validation set, the random forest model identified 45 samples as positive (38 were true positives) and 112 samples as negative (96 were true negatives). The model achieved an AUC of 0.9, and DeLong test confirmed its performance was significantly superior (p < 0.001) to HPV 16/18 genotyping and cytology testing. The random forest model constructed based on methylation patterns of human and HPV genes demonstrated strong predictive efficacy for CIN2+ in HR-HPV positive women, and may optimize the triage method in cervical cancer screening.
5 May 2026
Read appraisal →Nano letters
Seeing and Feeling DNA Methylation: Single-Molecule Biophysics Meets Machine Learning
DNA methylation at 5-methylcytosine (5mC) is crucial for embryonic development and cellular function, while aberrant patterns strongly drive disease onset and progression. Its reversible nature offers substantial therapeutic potential, emphasizing the need for precise, context-specific genome wide 5mC mapping. Conventional techniques such as bisulfite sequencing and ensemble biosensor assays are hindered by DNA degradation, amplification bias, high cost, and inability to resolve single-molecule structural and mechanical effects of methylation. This review examines advances in single-molecule biophysical methods (nanopore sensing, smFRET, optical/magnetic tweezers, and AFM) that provide direct, label-free/minimally invasive 5mC detection, along with quantitative insights into DNA conformation, mechanics, and protein-DNA interactions. These techniques complement traditional methylome mapping by linking genomic localization to molecular mechanisms. Emerging machine-learning approaches are revolutionizing analysis, particularly in nanopore sensing, while promising applications in smFRET, tweezers, and AFM address throughput and reproducibility challenges. Their convergence promises scalable, high-resolution epigenetic profiling, advancing precision epigenomics toward clinical application.
4 May 2026
Read appraisal →Current allergy and asthma reports
Interception of Regulatory RNAs on TGF-β Signaling in the Pathogenesis of Idiopathic Pulmonary Fibrosis: A Systematic Review
PURPOSE OF REVIEW: Idiopathic Pulmonary Fibrosis is a type of interstitial lung disease characterized by lung scarring due to excessive extracellular matrix (ECM) deposition. The transforming growth factor-beta (TGF-β) signaling pathway is the master regulator of fibrosis, driving myofibroblast activation and differentiation, epithelial-mesenchymal transition (EMT), and inhibition of ECM degradation. While individual classes of non-coding RNAs (ncRNAs) have been studied in IPF, a comprehensive understanding of how microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (LncRNAs) collectively regulate canonical TGF-β signaling remains lacking. Addressing this gap, this systematic review was conducted which presents current evidence on the integrated roles of ncRNAs in modulating TGF-β signaling in IPF pathogenesis. RECENT FINDINGS: A comprehensive search of PubMed and Web of Science databases (2015-2025), identified 45 eligible studies. miRNAs (19 antifibrotic, 11 profibrotic) directly targeted the core components of the TGF-β signaling pathway, whereas circRNAs (5 antifibrotic, 5 profibrotic) and LncRNAs (6 profibrotic) acted as competing endogenous RNAs and sponged their target miRNAs. Notably, all identified LncRNAs were profibrotic, amplifying the fibrotic signaling pathway. We found that ncRNAs critically fine-tune TGF-β signaling at multiple regulatory nodes, including ligand activation, receptor expression, Smad protein phosphorylation, nuclear translocation, and inhibitory feedback mechanism. The ncRNA-mediated regulatory mechanisms provide valuable insight into IPF pathogenesis and may help identify potential biomarkers and therapeutic targets. Future efforts should prioritize validating in vitro and animal model data in human samples, integrating regulatory network analysis, in vivo functional validation of RNA-based therapeutics, and exploring therapeutic delivery systems to develop an effective ncRNA-based antifibrotic therapy.
3 May 2026
Read appraisal →Bone research
A phase 2 trial of burosumab for treatment of fibroblast growth factor-23-mediated hypophosphatemia in children and adults with fibrous dysplasia
Fibrous dysplasia (FD) is a rare disorder associated with fractures and deformities. FD lesions produce excess phosphaturic hormone fibroblast growth factor 23 (FGF23), leading to hyperphosphaturia in most patients, and hypophosphatemia in those with high FD burden. Skeletal complications are associated with both low-normophosphatemia and frank hypophosphatemia. Burosumab is approved for other forms of FGF23 excess, but there is little evidence to inform use in FD. A phase 2 study investigated the safety and efficacy of burosumab in patients with FD. The primary endpoint was the proportion of participants achieving phosphate levels within the mid to upper part of the normal range (age and sex-adjusted Z-score -1 to +2). 12 participants (7 children, 5 adults) received burosumab for 48 weeks. Median phosphate Z-score increased from -2.88 (1.65) to 0.22 (1.37), meeting the target in 100% of participants. Alkaline phosphatase levels were elevated at baseline in 8 participants [median 846 U/L (464)] and declined by 49% at week 48, representing a median decline of -364 (244.5) U/L. PROMIS questionnaires showed trends toward improvements in all domains in children; adult scores showed no identifiable trends. Two children experienced transformational mobility gains, including advancement from full-time wheelchair use to independent ambulation. Lesion biopsies showed no changes in cellularity or composition, and 18F-NaF PET/CT scans showed no changes in tracer uptake, suggesting burosumab did not adversely impact lesional activity. Adverse events were mild, and none resulted in treatment withdrawal. Burosumab treatment in patients with FD was well-tolerated, restored phosphate homeostasis, and reduced alkaline phosphatase levels. Burosumab has the potential to lead to functional improvements and ambulation gains in severely affected patients and is a valuable tool to reduce the impact of FD-related disability.
2 May 2026
Read appraisal →European journal of pediatrics
Efficacy and safety of vosoritide in children with achondroplasia: a systematic review and meta-analysis
UNLABELLED: Achondroplasia is caused by a gain-of-function mutation in the FGFR3 gene. Vosoritide activates the NPR-B receptor to inhibit the overactive FGFR3 signaling pathway. We aim to pool the efficacy and safety outcomes of vosoritide in children with genetically confirmed achondroplasia who are receiving the approved dose of 15 μg/kg/day. We searched five databases up to February 10, 2026. A systematic review and single-arm meta-analysis were conducted in accordance with the PRISMA guidelines. The primary outcomes were annualized growth velocity (AGV), height gain, and change in height Z-score. Three quality assessment tools were used to assess different study designs of the included studies. All analyses were conducted using OnlineMeta V1.1. Thirteen studies on vosoritide treatment in children with achondroplasia were included, comprising randomized controlled trials, cohort studies, case reports, and case series. A meta-analysis showed that vosoritide was associated with an AGV of 5.72 cm/year (95% CI: 5.51-5.94) at 12 months. The mean height Z-score improvement at 12 months after sensitivity analysis was 0.28 (95% CI: 0.16-0.4), with no significant difference between sexes. Overall, the most common adverse events were injection site reactions (51%) and gastrointestinal symptoms (50%). CONCLUSION: One-year treatment with vosoritide is associated with increased growth velocity, height gain, and a modest improvement in height Z-score, accompanied by a high incidence of mild to moderate adverse events. Larger, longer-term studies are necessary to confirm the treatment's safety and efficacy. WHAT IS KNOWN: • Achondroplasia is a skeletal dysplasia caused by a mutation in the FGFR3 gene, which leads to inhibit endochondral ossification and bone growth. • Vosoritide is a recombinant C-type natriuretic peptide analogue designed to counteract overactive FGFR3 signaling and was approved by the FDA and EMA. WHAT IS NEW: • One-year treatment with vosoritide in achondroplasia children is associated with increased growth velocity and improvement in height Z-score with reported minor adverse events.
26 Apr 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Effect of Vosoritide Therapy on IGF-I and Endogenous C-Type Natriuretic Peptide in Hypochondroplasia
CONTEXT: Insulin-like growth factor-1 (IGF-1) and C-type natriuretic peptide (CNP) promote endochondral bone growth. We previously reported that vosoritide, a CNP analogue, increases annualized growth velocity (AGV) and height SD in prepubertal children with hypochondroplasia (HCH). OBJECTIVE: We hypothesized that IGF-1 and CNP will be inversely associated at baseline and will respond differentially during a single-arm, unblinded, standardized 12-month phase 2 clinical trial of vosoritide in prepubertal patients aged 3 to 11 years with HCH and height less than -2.25 SD. Participants were followed for a baseline 6-month observation period and then received a dose of 15 μg/kg/day of vosoritide subcutaneous daily injections for 12 months. Anthropometrics, IGF-1, and NTproCNP were measured every 6 months. Longitudinal changes in IGF-1 and NTproCNP as well as the correlations between these 2 analytes and changes in height SD and AGV were analyzed. RESULTS: Baseline IGF-1 values are reduced in children with HCH and increased throughout the study, but IGF-1 SD did not change significantly. NTproCNP was raised at baseline and declined throughout the study, with NTproCNP SD significantly lower at months 6 and 12 on treatment. Change in height SD or AGV were not correlated to IGF-1 or NTproCNP SD at 12 months. The change in IGF-1 concentrations at 12 months was positively correlated with the change in NTproCNP plasma concentrations (ρ = 0.57; P = .024). CONCLUSION: In children with HCH, IGF-1 levels are reduced and NTproCNP levels are elevated at baseline. A correlation between changes in these levels during treatment suggests a possible interaction between IGF-1 and CNP signaling.
24 Apr 2026
Read appraisal →Human mutation
HSPB6: A Potential Prognostic Biomarker, Inhibiting the Epithelial-Mesenchymal Transition (EMT) Process Through the PI3K/Akt Signaling Pathway Based on the Machine Learning and Experimental Validation
Bladder cancer (BC) is a prevalent malignant tumor worldwide, posing a significant public health burden and challenge to human society. Current therapeutic modalities for BC include surgical treatment, radiotherapy, chemotherapy, targeted therapy, and immunosuppressive therapy. However, almost all patients experience disease progression and ultimately succumb to BC. Our study demonstrated that elevated expression of Heat Shock Protein Beta-6 (HSPB6) correlated with higher clinical grades and stages, establishing it as an independent prognostic risk factor for BC. Enrichment analysis indicated that HSPB6 is associated with the extracellular matrix in BC. Experimental validation revealed that HSPB6 overexpression inhibits the proliferation of BC cell line T24. This effect may be achieved by inhibiting the PI3K/Akt signaling pathway, which in turn leads to inhibition of epithelial-mesenchymal transition (EMT). Furthermore, we developed a prognostic risk model that incorporated DDR2, DPYSL3, MFAP5, PDGFRB, and SPOCD1, allowing accurate prediction of patient outcomes based on immunological status. In conclusion, this study highlights that HSPB6 overexpression can restrain the proliferation of BC cells and inhibit EMT, underscoring its potential as a diagnostic marker and therapeutic target in BC.
23 Apr 2026
Read appraisal →Journal of medical genetics
Clinically significant DNA variation from the GENCOV and HostSeq COVID-19 genome sequencing studies
BACKGROUND: Vast amounts of genome sequencing data generated from large-scale research studies like HostSeq provide an opportunity to summarise the spectrum of pathogenic variation in a subset of the Canadian population. Sharing variant-level data with public databases, like ClinVar, is crucial for advancing our understanding of genomic variants related to Mendelian diseases. However, such entries are often incomplete or contain discrepancies which render interpretation and classifications less useful. METHODS: The GENCOV and HostSeq cohorts were annotated and summarised using custom workflows to identify variants with a pathogenic and likely pathogenic (P/LP) classification in ClinVar. These variants were further filtered using custom gene panels, gnomAD frequency, variant type, gene-disease relationship and the number of reputable ClinVar laboratory submissions. Manually assessed variants from the GENCOV study were compared with ClinVar classifications to identify discrepancies. RESULTS: A total of 1956 unique variants were manually classified as P/LP through the GENCOV study. Of these, 65% (1276) were also identified in ClinVar, and among those, 69% (889) had concordant P/LP classifications. The manual assessment of unique exonic variants in GENCOV yielded a higher number of putative P/LP variants (1595), including truncating and missense variants compared with the 127 unique exonic P/LP variants in HostSeq. CONCLUSION: These results highlight that a large proportion of P/LP variation is either absent or has conflicting evidence for pathogenicity in ClinVar, emphasising the importance of periodic reassessment, discrepancy resolution and updates to ensure completeness and accuracy of public databases.
23 Apr 2026
Read appraisal →PloS one
Identification and clinical implications of immune-related hub genes in psoriasis
BACKGROUND: Psoriasis, a chronic inflammatory skin disease affecting 2-3% of the global population, is driven by dysregulated immune responses. Despite advancements in biologic therapies, treatment challenges persist due to high recurrence rates. This study aimed to identify immune-related hub genes and elucidate their clinical implications in psoriasis pathogenesis and therapy. METHODS: Multiple microarray datasets from psoriasis patients (GSE30999, GSE106992, GSE14905, GSE78097, and GSE117468) were obtained to identify immune-key genes by differential gene analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, immune-related hub genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Protein-Protein Interaction (PPI) networks, with further validation through Gene Set Enrichment Analysis (GSEA) and Receiver Operating Characteristic (ROC) curves to assess exploratory within-sample discrimination. Pearson correlation analysis evaluated the relationship between hub genes, skin lesion severity, and treatment outcomes. The study also conducted immune infiltration by using the Cell-type Identification by Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and identified potential therapeutic targets by the Drug-Gene Interaction Database (DGIdb). RESULTS: Thirty-one immune-related key genes were identified, and six hub genes (CLEC7A, CXCL1, IRF1, S100A12, S100A8, S100A9) were validated as central players in immune signaling pathways. These genes exhibited within-sample discrimination (AUC > 0.9) and correlated with disease severity and biological therapy efficacy. Immune infiltration analysis revealed increased activated memory CD4+ T cells and M1 macrophages in lesional skin, which was strongly associated with hub gene expression. Additionally, drug-gene interaction analysis identified potential therapeutic agents targeting these genes. CONCLUSION: This study identified six immune-related hub genes that were closely linked to the severity of psoriasis, the effectiveness of biological treatments, and infiltrated activated memory CD4+ T cells and M1 macrophages. Our findings elucidate a novel immune-related hub gene network in psoriasis and provide potential targets for the development and application of biologics.
23 Apr 2026
Read appraisal →Journal of hematology & oncology
Uncoupling toxicity from efficacy in donor selection for allogeneic hematopoietic cell transplantation: a retrospective cohort study using double machine learning
Haploidentical hematopoietic cell transplantation (HCT) offers potent graft-versus-tumor effects but is historically limited by higher non-relapse mortality (NRM) compared to matched unrelated (MUD) or related donors (MRD). To algorithmically decouple the competing risks of NRM and relapse, we analyzed 1,713 adult patients (MD Anderson discovery cohort) and 6,829 patients (registry replication cohorts) using a double machine-learning framework of Causal Survival Forests to estimate individualized treatment effects, while Random Survival Forests defined absolute prognostic risk. We found biological uncoupling of relapse and NRM: relapse was driven by disease biology, while NRM was governed by physiological reserve. Specifically, cardiovascular or cerebrovascular comorbidities acted as selective amplifiers of haploidentical toxicity-a "vascular penalty" that doubled mortality risk in the bootstrapped model (sub-distribution hazard ratio 2.09, 95% confidence interval 1.26-3.35). We developed a 3-factor nomogram (recipient age, vascular comorbidity, B-leader matching status) to identify an "Optimized Haploidentical" phenotype. Exploratory multivariable modeling suggested MUD consistently maximized predicted disease-free survival across all subgroups. Among haploidentical versus MRD comparisons, B-leader mismatch appeared to favor MRD across ages, while B-leader matched haploidentical donors in older patients (≥ 60 years) modeled near-equivalent DFS to MRD, representing a hypothesis-generating "zone of equivalence" requiring prospective validation.
22 Apr 2026
Read appraisal →International journal of epidemiology
Structured tools for assessing quality and risk of bias in Mendelian randomization studies: an updated systematic review
BACKGROUND: The growing use of Mendelian randomization (MR) has heightened the need for rigorous quality and bias assessment tools. A previous systematic review included studies published up to July 2021 identified 14 structured instruments for conducting, evaluating, and reporting MR studies. However, methodological developments have accelerated in the years since. METHODS: We updated the previous systematic review to include tools published between July 2021 and January 2025, applying the same search strategy and eligibility criteria. Two reviewers independently screened articles, extracted data, and mapped tool content to bias domains. RESULTS: We identified 15 additional articles, bringing the total to 29 tools. Of these 29, 19 provided structured evaluation tools. Twelve of the 19 evaluation tools were newly added in the present review, which addressed broader methodological domains beyond core instrumental variable assumptions, including genetic instrument selection, population stratification, sensitivity analyses, and dataset considerations. However, substantial variation in bias domains, structure, and scoring methods across tools persists. Key gaps remain in the assessment of linkage disequilibrium, missing data, and dynastic effects. CONCLUSIONS: While the number of structured tools has increased in recent years, the lack of standardization across tools makes it difficult to compare across systematic reviews of MR studies. Developing more complete and standardized evaluation frameworks and properly testing these tools in practice are important next steps to improve the overall quality of MR research.
20 Apr 2026
Read appraisal →Chemical Society reviews
Machine learning-driven molecular engineering of nucleic acids
Molecular engineering has played a pivotal role in biomedical fields, driving significant advancements in gene therapy, disease diagnosis, and biosensing. However, nucleic acid molecular engineering faces various challenges including vast design spaces, complex structure-function relationships, lengthy application validation cycles, and inefficient optimization processes. Machine learning (ML), with its superior pattern recognition, multidimensional data integration, and automated optimization capabilities, offers a unique opportunity to construct predictive models of sequence-structure-function relationships, thereby enabling a paradigm shift from empirically driven to data-driven approaches. This review systematically surveys recent progress in ML applications across three major domains: nucleic acid structure construction, performance modulation, and application expansion. It also explores core challenges such as data quality, model interpretability, and experimental validation efficiency, along with potential resolution strategies. These insights are poised to propel nucleic acid molecular engineering from static structure prediction toward dynamic behavior simulation, and from single-molecule design to complex system engineering, guiding future directions in hybrid ML-quantum models and expanded applications to non-canonical nucleic acids for transformative innovation in biomedicine, environmental monitoring, and information technology.
20 Apr 2026
Read appraisal →PloS one
Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study
Cardiovascular disease is a leading cause of mortality and rising healthcare costs worldwide. Fortunately, the disease is preventable, and addressing risk factors can significantly reduce its effects. Over the past decade, risk prediction models have advanced significantly, with polygenic risk scoring analysis, which is often used in combination with clinical health information for prediction. However, most previous cardiovascular disease prediction studies based on polygenic risk scores have focused on a single specific disease or event, such as cardiac events. Given the complex nature of the cardiovascular disease, which involves a combination of genetic and environmental factors, a comprehensive analysis of the disease prediction results is essential. In this study, we investigate the genetic and environmental factors contributing to cardiovascular disease by utilizing data from the Framingham Heart Study, a leading cardiovascular cohort. We compared the prediction performance of different methods across various scenarios and assessed performance using various evaluation metrics to identify the best-fitting model for six cardiovascular related diseases. We also analyzed the feature importance of genetic and clinical variables, noting that different variables had varying effects on each disease. Our findings demonstrated the performance of prediction algorithms in forecasting cardiovascular disease by utilizing genetic and clinical factors, as well as highlighting the importance of each feature in the disease prediction. While models relying solely on polygenic risk score showed relatively low prediction performance for some diseases, integrating genetic information with clinical data improved prediction performance in most cases. For certain diseases, particularly those known to be heritable, polygenic risk scores demonstrated predictive ability, suggesting that they may serve as standalone predictive tools. We believe our study reveals the value of combining polygenic risk scores with clinical variables and expect that our thorough analysis can inform study designs tailored to specific diseases and research objectives.
20 Apr 2026
Read appraisal →JMIR formative research
Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach
BACKGROUND: Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has shown potential in early cancer detection, but its integration with machine learning across racially diverse populations remains limited. OBJECTIVE: This study aimed to develop a prostate cancer stage classifier for the majority White cohort using DNA methylation data and a multilayer perceptron (MLP) model in order to classify prostate cancer stages into early (stages I-II) and late (stages III-IV) stages and assess its performance when applied to other racial groups to highlight the need for race-specific models. METHODS: Methylation and phenotype data from the TCGA-PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma) dataset were processed using differentially methylated position (DMP) analysis to identify CpG sites correlated with cancer stages. These features were further refined through recursive feature elimination (RFE) and used to train MLP models. Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the model and identify key DNA methylation features contributing to model predictions. RESULTS: The best-performing model achieved 95% accuracy and up to 99% area under the curve on the majority race (White) training data using 90 selected features. However, performance declined sharply in racial minority groups, revealing the effects of sample imbalance and race-specific methylation patterns. Feature importance examination indicated strong patterns within certain CpG sites driving model predictions. CONCLUSIONS: We propose a race-aware MLP model for prostate cancer stage classification using DNA methylation data, which has been optimized through DMP and RFE-based feature selection. SHAP and LIME confirmed the predictive relevance of selected CpG sites, supporting model transparency. The results highlight high performance within the White cohort but reveal poor generalization to racial minority groups, emphasizing the importance of race-specific modeling strategies.
17 Apr 2026
Read appraisal →Cancer research
Mutant and Wild-Type RAS Cross-talk and Stoichiometric Deficiencies Are Determinants of Sensitivity to Targeted Therapies in KRASG12R Pancreatic Ductal Adenocarcinoma
UNLABELLED: Therapies targeting the RAF-MEK-ERK pathway are generally considered to have limited efficacy in KRAS-mutant cancers. However, specific KRAS mutants exhibit distinct behaviors. Notably, KRASG12R pancreatic ductal adenocarcinoma (PDAC) tumors have shown sensitivity to MEK inhibitors (MEKi) in combination with autophagy inhibitors, but a better understanding of the underlying mechanisms is needed to optimize this treatment strategy. Using a systems-level approach, we uncovered a mechanistic explanation for this phenomenon. Due to distinct biophysical properties, KRASG12R had an impaired ability to activate wild-type HRAS and NRAS (WT-RAS) compared with other KRAS mutants, such as KRASG12D. This reduced activation stemmed from the weaker interaction between KRASG12R and guanine exchange factors (SOS), as well as the tumor suppressor neurofibromin (NF1), crucial in regulating WT-RAS activity. The impaired ability to activate WT-RAS led to weaker holistic MAPK signaling in KRASG12R-driven tumors, which conferred increased sensitivity to MEKi. To substantiate the preclinical findings, the utility of MEKi in combination with the autophagy inhibitor hydroxychloroquine was analyzed in patients with KRASG12R-mutated metastatic PDAC. Five of the eight (62.5%) patients treated in first- or second-line settings had a progression-free survival exceeding 6 months. Three patients had impressive disease control: two had stable disease of 11 and 22.7 months, and one achieved a partial response with an 83% decrease in tumor size that lasted for 8.9 months. Overall, this work highlights how systems-based approaches in precision medicine can uncover mechanistic insights to guide the identification of patients with PDAC most likely to benefit from tailored therapeutic strategies. SIGNIFICANCE: The unique sensitivity of KRASG12R-mutant cancers to MEK inhibitors offers a critical advancement in understanding MAPK signaling and paving the way for precision-targeted therapies in previously untreatable contexts. See related commentary by Tiriac and Engle, p. 1817 See related article by Burge et al., p. 1854 See related article by Burge et al., p. 1868.
17 Apr 2026
Read appraisal →Computers in biology and medicine
Deep learning with limited data: a transfer learning approach for transcriptomic survival prediction
BACKGROUND: Deep neural networks (DNNs) are promising for analyzing high-dimensional transcriptomic data in cancer research but are limited by data scarcity and heterogeneity. Transfer learning (TL), which leverages large datasets to improve performance on smaller ones, has been widely applied in classification and clustering, yet its use in survival prediction remains underexplored. METHODS: We evaluated transfer learning for disease-free survival prediction using RNA-seq data from 7509 patients across 27 tumor types from The Cancer Genome Atlas. A pan-cancer DNN was optimized via random search across 750 configurations. Tumor-specific baseline models were trained independently, while transfer learning models were built with a leave-one-out pre-training strategy followed by fine-tuning on the target tumor. Performance was assessed with the concordance index. Biological relevance was evaluated through a LIME-like interpretability approach and gene-set enrichment analysis. RESULTS: Transfer learning outperformed tumor-specific baselines in 24 of 27 tumor types, with the greatest improvements in smaller cohorts. Fine-tuning consistently enhanced predictions by adapting pan-cancer features to tumor-specific data. Enrichment analysis confirmed that the models captured both general oncogenic mechanisms and tumor-specific pathways. CONCLUSIONS: Transfer learning improves survival prediction from transcriptomic data, especially in small cohorts, by exploiting molecular patterns shared across cancers. These findings support transfer learning as a robust strategy for survival modeling and a step toward foundation models in molecular oncology.
16 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Resistance to neoadjuvant talazoparib in triple-negative breast cancer by BRN2-induced ATR/STAT3 pathways or SHLD2 subclone expansion
Intrinsic and acquired resistance to poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) remains a major barrier in treating homologous recombination (HR) repair-deficient tumors, including those with germline or somatic BRCA1/2 mutations. Although PARPi are FDA approved for adjuvant treatment of locally advanced or metastatic breast cancer in patients with germline BRCA1/2 mutations, emerging data support their use as monotherapy in the neoadjuvant setting. Promising safety profiles of newer-generation PARPi further support this potential. However, resistance mechanisms specific to the neoadjuvant setting are poorly understood. To address this gap, we leveraged resources from a phase II neoadjuvant clinical trial (NCT03499353), analyzing tumors from patients with germline BRCA1/2 mutant breast tumors before and after six months of talazoparib monotherapy. Whole-transcriptome analyses were performed on these samples. Additionally, we established orthotopic patient-derived xenograft models from a subset of the patient tumors and conducted whole-exome and whole-transcriptome analysis. This integrative approach revealed both known and previously unknown PARPi resistance mechanisms. In one case, overexpression of BRN2, encoding a transcription factor that plays a critical role in neurogenesis, led to activation of ATR/RAD51 and STAT3 pathways, restoring HR repair. BRN2-driven resistance could be reversed with ATR and STAT3 inhibitors, resensitizing cells to talazoparib. In another, an HR repair proficient tumor subclone lacking Shieldin 2 expression expanded during treatment and accounted for intrinsic resistance. Our findings highlight the need to determine intrinsic and anticipate acquired resistance pathways in treatment-naïve tumors and support combining PARPi with targeted agents to improve outcomes in the neoadjuvant setting.
12 Apr 2026
Read appraisal →HGG advances
Gene-specific pathogenicity predictor for chromatin remodeling BAF complex-associated neurodevelopmental disorders
Advancements in whole-genome sequencing have increased the number of variants of uncertain significance (VUS) identified in human genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BRG1/BRM-associated factor (BAF) complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and area under the precision-recall curve than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.
12 Apr 2026
Read appraisal →Biomedical physics & engineering express
Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches
Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threatening arrhythmias. This study aims to achieve automatic discrimination among the LQT1, LQT2, and LQT3 genotypes to enable targeted treatment and prevention strategies. Utilizing ECG data from the Telemetric and Holter ECG Warehouse's LQTS database, our methodology involves an automated extraction process of short ECG signals, geometric parameterization techniques, and classification using a two-stage cascade of binary support vector machine classifiers. The input features for the classifiers are derived from Lead I ECG signals sampled at 200 Hz, highlighting the potential application in developing single-lead ECG monitoring devices and applications, such as widely used smartwatches, for which short recording duration, low sampling frequency, and arm-to-arm lead measurements are fundamental prerequisites for practical use. The proposed classifier achieved 71% weighted accuracy on out-of-sample data (LQT1: 65% recall, 58% precision; LQT2: 79% recall, 82% precision; and LQT3: 71% recall, 77% precision). Our findings demonstrate the feasibility of noninvasive genotype differentiation for LQTS based on the morphological analysis of ECG signals, providing an advancement in the field of personalized cardiology and the development of portable diagnostic tools.
11 Apr 2026
Read appraisal →Scientific reports
SPG601-associated modulation of resting-state EEG and improvement in executive function in a fragile X syndrome randomized controlled crossover study
Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and autism spectrum disorder. Despite extensive research, no targeted treatments exist for the core symptoms of FXS. SPG601 represents the first BK channel activator to enter clinical testing for FXS, designed to restore synaptic function by correcting specific ion channel dysfunction downstream of fragile X messenger ribonucleoprotein protein (FMRP) loss. We conducted a randomized, double-blind, placebo-controlled, 2-period balanced crossover study in 10 adult men with genetically confirmed full-mutation fragile X syndrome. Participants received single doses of SPG601 800 mg and placebo separated by a 1-week washout period. Given the first in FXS nature of the study, the safety and tolerability of SPG601 were evaluated as a primary outcome. Additional endpoints included resting-state EEG power spectral density analysis across predefined frequency bands and auditory-evoked gamma oscillations combined with cognitive assessments using validated instruments and clinical global impressions scales. Among EEG measurements, excessive high frequency gamma band activity has been most extensively validated as a biomarker across species in FXS and has demonstrated correlation with severity of the human FXS phenotype and therefore served as the primary neurophysiologic endpoint. SPG601 was well tolerated with a favorable safety profile. SPG601 demonstrated significant modulation of resting-state EEG power spectral density compared to placebo. Significant treatment effects were observed for gamma power (F = 5.20, p = 0.023), alpha2 power (F = 17.43, p < 0.001), and theta power (F = 7.06, p = 0.008). SPG601 also significantly modulated aperiodic EEG slope (F = 5.28, p = 0.022), indicating alterations in the broadband 1/f component reflecting excitation-inhibition balance. Cognitive improvement was observed in the NIH Toolbox Flanker Inhibitory Control and Attention Test across multiple scoring metrics (p = 0.027). No significant effects were observed on auditory-evoked gamma measures. This study provides the first clinical evidence that SPG601 produces significant neurophysiological changes in FXS, accompanied by a cognitive enhancement in executive function.
10 Apr 2026
Read appraisal →Psychological medicine
Joint effects of childhood adversity and genetic risk for psychosis on psychopathology in the UK Biobank
BACKGROUND: The individual effects of genetic factors and adverse childhood experiences (ACEs) on risk of psychosis, including schizophrenia (SCZ) and bipolar disorder (BIP), have been widely acknowledged, but their interaction effects on individual psychopathological symptoms remain unclear. METHODS: Based on data from 163,704 individuals in the UK Biobank, we investigated the joint effects of polygenic risk scores (PRSs) of SCZ and BIP and ACEs on psychopathology. ACEs status and 55 psychopathological symptoms from seven domains were measured retrospectively using an online mental health questionnaire in 2016. Recent genome-wide association studies for SCZ and BIP were combined with genotype data to generate PRSs. Logistic regression analyses were then conducted to explore univariate and joint main effects of PRSs and ACEs on psychopathological symptoms, as well as their additive and multiplicative interaction effects. RESULTS: The interaction mechanisms for PRSs and ACEs varied across symptom domains: additive interactions were observed on the depression (RERIBIP-ACEs = 0.20-0.25), anxiety (RERISCZ-ACEs = 0.20; RERIBIP-ACEs = 0.22-0.26), help-seeking (RERISCZ-ACEs = 0.24; RERIBIP-ACEs = 0.23), and cognition domains (RERISCZ-ACEs = -0.23 to -0.17), whereas multiplicative interactions were only detected on the psychotic (betaSCZ-ACEs = -0.543; betaBIP-ACEs = -0.181), mania (betaBIP-ACEs = -0.195), self-harm or suicide (betaSCZ-ACEs = -0.118), and cognitive domains (betaSCZ-ACEs = -0.204 to -0.157). CONCLUSIONS: The interplay mechanisms for genetic liability to SCZ and BIP and ACEs vary across symptom domains. This study reveals heterogeneity in gene-ACEs interaction mechanisms underlying psychosis and may provide personalized guidance for psychological care after ACEs.
8 Apr 2026
Read appraisal →Genetics
Mapping whole-organism genetic comorbidities across model Species using unified ontologies
Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a computational approach to analyze whole-organism comorbidities associated with genes underlying non-obstructive azoospermia (NOA), the most severe form of male infertility. We curated 204 mouse genes with experimentally validated spermatogenic failure and mapped their orthologs and phenotype annotations across humans, M. musculus, D. rerio, D. melanogaster, and C. elegans using a unified cross-species phenotype structure. This framework integrates a newly developed reproductive phenotype ontology with standardized whole-body phenotype categories to enable direct cross-species comparisons, using thousands of genotype-phenotype associations stored in model organism databases. Our analysis shows that most NOA genes have conserved orthologs across models, and that perturbation of these genes is frequently associated with non-reproductive phenotypes. In particular, integumentary defects and neoplastic phenotypes recur across species, supporting a shared genetic basis for epidemiological links between male infertility and systemic disease. Gene-level comorbidity patterns are partially predictable from single-cell RNA-seq, whole-body gene expression, and Gene Ontology annotations, suggesting that fundamental biological constraints shape the systemic consequences of reproductive gene dysfunction. Clustering genes by comorbidity profiles further distinguished genes with isolated reproductive effects from those with broad organismal consequences. This work demonstrates the power of ontology-based cross-species analysis for identifying pleiotropic effects of Mendelian disease mutations, and provides a resource, CoMorbidity DataBase Mapper (CoMo DBM), for joint analysis of genotype-phenotype associations in model organism databases.
6 Apr 2026
Read appraisal →American journal of human genetics
The Electronic Medical Records and Genomics study: Design and analytic framework for assessing the impact of genome-informed risk assessments.
The Electronic Medical Records and Genomics (eMERGE) Network developed and implemented a genome-informed risk assessment (GIRA) to communicate genomic (polygenic risk scores [PRSs], integrated risk scores [IRSs], and monogenic results), clinical, and family history-based risk for 11 chronic diseases and provide recommended healthcare recommendations. GIRA reports have now been returned to 23,840 participants and their providers in a large prospective cohort study. We present here the study design and analysis framework for assessing the attributable impact of GIRA return. Pre-specified outcomes include (1) provider/participant adoption of recommended healthcare actions, (2) new diagnosis of disease, (3) treatment initiation/intensification, and (4) clinical outcomes (surrogate markers or clinical events). We assess outcomes in high risk vs. not-high-risk participants, adjusting for covariates. We evaluate the effect of PRS/IRS at pre-established high-risk thresholds using regression discontinuity (RD), a quasi-experimental method that mimics randomization near a cutoff, enabling estimation of causal effects and controlling for unobserved confounders. Monogenic and family history-based risk stratification are analyzed using logistic regression. With 23,840 participants and 12 months of follow-up, the study is powered to detect differences of 2%-11% with 80% power (α = 0.05 in the adoption outcome). Longer follow-up will be required to enable assessment of new disease diagnosis, treatment changes, and clinical outcomes. Through innovative RD analyses and defined outcomes and comparison groups, this study will provide new insights into the real-world clinical impact of genomic risk assessment, address critical evidence gaps, advance understanding of genomic medicine outcomes, and inform future research.
5 Apr 2026
Read appraisal →The Lancet. Oncology
Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study
BACKGROUND: Genomic assays such as Oncotype DX have transformed adjuvant treatment selection for hormone receptor-positive, HER2-negative, early breast cancer but remain inaccessible to many patients because of high cost and logistical barriers. We aimed to develop and validate an artificial intelligence (AI) model that estimates Oncotype DX 21-gene recurrence scores directly from routine histopathology slides and clinicopathological variables. METHODS: In this multicentre, model development and validation study, a multimodal deep-learning model was trained on digital whole-slide images and clinical features using a foundation model pre-trained on 171 189 histopathology slides for predicting Oncotype DX recurrence score. We included slides from patients with hormone receptor-positive, HER2-negative, invasive breast cancers and without scanning artifacts and with at least 100 tissue tiles (1·6 mm2). The model was fine-tuned and validated on the TAILORx randomised trial (8284 patients after quality control). Prognostic and predictive performance was assessed in the TAILORx-test set and externally validated in six independent cohorts (Carmel, Haemek, and Sheba medical centres [Israel], the University of Chicago Medical Center [USA], the Australian Breast Cancer Tissue Bank [Australia], and the Cancer Genome Atlas Breast Invasive Carcinoma project [USA]). FINDINGS: In the TAILORx-test set (n=2407), the AI model classified 1097 (45·6%) patients as low risk, 1021 (42·4%) as intermediate risk, and 289 (12·0%) as high risk. For identifying high genomic-risk disease (recurrence score ≥26), the area under the curve (AUC) was 0·898 (95% CI 0·879-0·913). AI-based risk stratification was prognostic for recurrence-free interval (hazard ratio 2·61 [95% CI 1·68-4·04]), distant recurrence-free interval (2·88 [1·73-4·79]), and disease-free survival (1·32 [0·92-1·89]). Chemotherapy benefit was evident in premenopausal patients classified by AI as being at high risk (0·63 [0·46-0·86]) but absent in postmenopausal patients classified by AI as being at low risk (0·94 [0·78-1·12]). 151 (31·3%) clinically high-risk postmenopausal women (by MINDACT criteria) were reclassified as low AI risk with no chemotherapy benefit. Analysis on external cohorts (5497 patients) showed that the model is transferable to new data with high generalisability (recurrence score ≥26 AUC ranging from 0·858 to 0·903). INTERPRETATION: These findings show that AI applied to routine histopathology can serve as a practical and scalable tool for guiding chemotherapy decisions in hormone receptor-positive, HER2-negative, early breast cancer. This approach has the potential to reduce unnecessary chemotherapy and broaden access to precision oncology, particularly in resource-limited settings where genomic testing remains unavailable or unaffordable. FUNDING: Israel Innovation Authority (Kamin), Zimin Institute for Artificial Intelligence Solutions in Healthcare, Israel Precision Medicine Partnership program, and Israel Cancer Research Fund.
4 Apr 2026
Read appraisal →Nature reviews. Clinical oncology
MicroRNAs in oncology: a translational perspective in the era of AI
Over the past three decades, knowledge of microRNA (miRNA) biology has advanced from the initial discovery of their regulatory functions to the finding of abnormal activity in leukaemias, and then to a comprehensive understanding of the roles of miRNAs in both normal physiology and most diseases, with cancer being extensively studied. miRNA dysregulation contributes to tumorigenesis, with certain miRNAs acting as either tumour suppressors or oncogenic factors in a context-dependent manner. A subset of miRNAs have shown promise as tumour biomarkers and therapeutic targets in preclinical studies, with several miRNA-based diagnostic tools and treatments progressing to clinical trials. Artificial intelligence (AI) and machine learning techniques began to be introduced into cancer research and oncology a decade ago and are now on the verge of revolutionizing biomarker identification and clinical trials. In this Review, we highlight important roles of miRNAs in cancer biology and their potential as diagnostic tools and therapeutic targets. In particular, we discuss emerging challenges and opportunities presented by AI-driven data analysis and combinatorial strategies, and how advances in these areas have addressed previous doubts on the clinical translation of miRNA-based biomarkers and therapeutics.
4 Apr 2026
Read appraisal →Critical reviews in oncology/hematology
Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology
Breast cancer (BC) is a highly heterogeneous malignancy and remains a major cause of cancer-related mortality among women worldwide. Advances in multi-omics profiling spanning genomics, transcriptomics, epigenomics, proteomics, and metabolomics have enabled finer subtype stratification and more comprehensive characterisation of tumour biology, thereby accelerating the discovery of diagnostic and prognostic biomarkers and actionable therapeutic targets. Nonetheless, translating multi-layer molecular signals into clinically robust decision support remains challenging because of the high dimensionality and heterogeneity of omics data, cross-cohort and cross-platform variability, and the fragmentation inherent to single-modality analyses. This review summarises how multi-omics studies have refined BC subtype definitions and advanced biomarker and target identification, and then synthesises recent progress in artificial intelligence, particularly deep learning, for integrating multi-omics with imaging, pathology, and clinical variables to improve diagnosis, risk stratification, prognosis prediction, and treatment response assessment. We critically examine representative multimodal integration frameworks and emerging deep learning architectures that learn both shared and modality-specific representations, which in many settings enable more accurate patient-level prediction than unimodal baselines. We further delineate key barriers to clinical translation, including cross-centre heterogeneity and inconsistent endpoint definitions, structural missingness of modalities in real-world workflows, inadequate cross-platform normalisation, limited interpretability and auditability, and a lack of prospective validation. Finally, we propose realistic next steps, including standardised and auditable preprocessing pipelines, missingness-aware fusion strategies, explainable and uncertainty-aware modelling, privacy-preserving multi-centre learning, and prospective, workflow-based evaluation. Collectively, these perspectives provide a roadmap for advancing multimodal AI-multi-omics integration toward reliable clinical deployment in BC management.
4 Apr 2026
Read appraisal →Clinical genetics
Clinical Feasibility of Long-Read WGS for DNA Methylation Signature Analysis
DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.
4 Apr 2026
Read appraisal →N Engl J Med
Adenoviral Inciting Antigen and Somatic Hypermutation in VITT
BACKGROUND: Vaccine-induced immune thrombocytopenia and thrombosis (VITT) is a rare prothrombotic complication that occurs after adenoviral vector-based vaccination against coronavirus disease 2019; in rare cases, it can also occur after natural adenovirus infection. VITT is mediated by platelet-activating antibodies against the highly cationic protein platelet factor 4 (PF4). The underlying inciting antigen trigger and immunopathogenesis remain unknown. METHODS: We used antibody proteomics to determine the amino acid sequences of anti-PF4 antibodies from 21 patients with VITT and sequenced the genes encoding the immunoglobulin light-chain hypervariable region from 100 patients with VITT. To identify an adenoviral trigger, we used the antigen-binding fingerprints of anti-PF4 and anti-adenovirus protein antibodies to identify a shared serum clonotype and subsequently used adenovirus protein peptides and recombinant anti-PF4 VITT antibodies to map the mimicking linear epitope. RESULTS: Genomic and proteomic profiling of VITT antibodies revealed a shared immunoglobulin light-chain allele, IGLV3-21*02 or *03, harboring a critical somatic hypermutation, K31E. Only antibodies purified against adenoviral core protein VII (pVII) contained anti-PF4 species matching the VITT fingerprint; antibodies against intact virions or other adenoviral proteins did not. Cross-reactive IgGs were mapped to a basic linear epitope on pVII. A pathogenic anti-PF4 VITT antibody, back-mutated to germline (K31), lost its prothrombotic activity in vitro and in vivo and preferentially bound pVII, a finding that directly supported the role of the hypermutation in the antigenic shift from adenovirus pVII to PF4. CONCLUSIONS: The results of our study indicate that VITT occurs when, in persons with immunoglobulin light-chain allele IGLV3-21*02 or *03, a specific somatic hypermutation develops that affects antibodies that recognize a specific epitope on the adenoviral core protein pVII, which results in misdirection of antibody targeting toward PF4. (Funded by Deutsche Forschungsgemeinschaft and others; German Clinical Trials Register number, DRKS00025738; EU Post-Authorization Study Register number, EUPAS45098.).
1 Feb 2026
Read appraisal →Int J Surg
Multi-omics dissection of high TWAS-active endothelial pathogenesis in pulmonary arterial hypertension: bridging single-cell heterogeneity, machine learning-driven biomarkers, and developmental reprogramming
BACKGROUND: Pulmonary Arterial Hypertension (PAH) is a leading cause of cardiovascular-related mortality worldwide. The emergence of single-cell RNA sequencing (scRNA-seq) has enhanced the ability to dissect cellular heterogeneity in PAH at a granular level. Transcriptome-wide association studies (TWAS) leverage expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) data to identify novel susceptibility genes whose genetically predicted expression correlates with disease risk. However, no study has systematically integrated TWAS with scRNA-seq to unravel the pathogenesis of PAH at single-cell resolution. METHODS: Using TWAS analysis, we identified a set of candidate genes genetically associated with PAH. We then evaluated the differential activity of these genes across PAH cell types at single-cell resolution using AUCell, Ucell, ssGSEA (Single Sample Gene Set Enrichment Analysis), and AddModuleScore algorithms. A subset of endothelial cells exhibiting elevated TWAS activity was identified via quartile-based stratification and designated as the high TWAS activity state (HTS) group. Multi-dimensional analyses, including observed-to-expected ratio (RO/E), CellChat, CytoTRACE (CytoTRACE is based on the robust observation that transcriptional diversity decreases during cell differentiation), and scMetabolism, were employed to characterize the functional and communicative properties of HTS cells. Machine learning algorithms were integrated to identify signature genes of the HTS subpopulation, and a benchmarked random forest model was trained to predict HTS status. We performed immunohistochemistry and quantitative reverse transcription- polymerase chain reaction (qRT-PCR) validation of the signature genes (KLF2, RASIP1 and DEPP1) in PAH and control lung tissues to support their expression patterns. RESULTS: We demonstrated that HTS endothelial cells are strongly associated with PAH pathogenesis, exhibiting significant tissue tropism, enhanced roles in intercellular communication, and a progenitor-like function in endothelial differentiation. Machine learning-based feature selection revealed three robust signature genes: KLF2, RASIP1, and DEPP1. These genes demonstrated exceptional predictive power for identifying HTS cells, suggesting their potential as drivers of endothelial dysfunction in PAH. The random forest model, benchmarked against multiple algorithms, achieved high accuracy in predicting PAH progression using these genes. Immunohistochemical analysis of pulmonary artery and qRT-PCR result of lung tissues addressed the elevated expression of KLF2, RASIP1 and DEPP1 in arterial wall post-PAH. CONCLUSION: This study elucidates endothelial cell heterogeneity in PAH and establishes the central role of HTS cells in disease progression, cellular crosstalk, and developmental reprogramming. Our findings bridge the gap between GWAS and scRNA-seq methodologies and provide a transformative framework for understanding PAH mechanisms.
1 Feb 2026
Read appraisal →Cancer Cell
Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2- breast cancer: The LINUXtrial.
We report the results of LINUX (NCT05594095), a multicenter, randomized, controlled phase II platform trial aiming to identify effective precision treatments for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer after resistance to cyclin-dependent kinase 4/6 inhibitor. A total of 105 patients were categorized into four similarity network fusion (SNF) subtypes by artificial intelligence-assisted classification and randomly assigned to receive subtyping-based precision therapy (N = 70) or treatment of physician's choice (N = 35). Results demonstrate superior primary endpoint of objective response rates in the subtyping-based groups compared to controls: 10% versus 0% for SNF1, 65% versus 30% for SNF2, 40% versus 30% for SNF3, and 70% versus 20% for SNF4. Grade 3-4 treatment-related adverse events occurred in 37% of both groups. These findings highlight the clinical benefits of subtyping-based precision therapies, particularly for SNF2 and SNF4 subtypes, warranting further validation in phase III trials.
1 Feb 2026
Read appraisal →J Clin Oncol
Genomics Define Malignant Transformation in Myeloma Precursor Conditions
Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.
1 Jan 2026
Read appraisal →Curr Opin Pediatr
Artificial intelligence supported facial feature analysis in medical genetics
PURPOSE OF REVIEW: This review highlights recent developments in the use of machine learning supported facial feature analysis in the context of genetic and syndromic conditions. The review focusses on 2D image-based tools. The 2D images are easily obtained using handheld devices such as mobile phones and are more relevant to general practice than 3D image based systems. RECENT FINDINGS: Different algorithms are used widely in pediatric clinics, medical genetics clinics, for gene variant analysis and in research. Integrated systems combining next-generation phenotyping with next-generation genotyping support a shortened diagnostic odyssey for patients. SUMMARY: Future integration of phenotyping using data available in electronic medical records with genotyping data will likely result in earlier identification of possible genetic conditions.
1 Dec 2025
Read appraisal →Nat Med
Feasibility and clinical utility of expanded genomic newborn screening in the Early Check program
Although genomic sequencing presents groundbreaking newborn screening (NBS) opportunities, critical feasibility and utility questions remain. Here we present initial results from the Early Check program-an observational study assessing the feasibility and clinical utility of genomic NBS in North Carolina. Recruitment was statewide through mailed letters with electronic consent. Genome sequencing with analysis of 169 high actionability genes (plus 29 optional lower actionability genes) was performed using residual NBS dried blood spots. In 8 months, 1,979 newborns were screened, with 50 (2.5%) screen positives. Negative results were returned electronically, positive results by genetic counselors. Twenty-eight results (55%) were true positives, all received anticipatory guidance, surveillance and management recommendations, and referral to specialists as appropriate. We report technical feasibility and preliminary clinical utility finding, along with interpretation and follow-up challenges that hinder public health implementation. We propose standardized terminology to facilitate cross-study comparisons and accurate characterization of genomic NBS outcomes.
1 Nov 2025
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