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Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min–max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65–0.86) and 0.84 (0.70–0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Pancreatic cysts, particularly intraductal papillary mucinous neoplasms (IPMNs), pose a potential risk for progressing to pancreatic cancer (PC). This study investigates the genetic architecture of benign pancreatic cysts and its potential connection to PC using genome-wide association studies (GWAS). The discovery GWAS identified significant genetic variants associated with benign cysts, specifically the rs142409042 variant near the OPCML gene. A pairwise GWAS comparing PC to benign cysts revealed the rs7190458 variant near the BCAR1 and CTRB1 genes. Further analysis with identified GWAS genes highlighted the Actin Related Protein (Arp) 2/3 complex as a potentially important molecular mechanism connecting benign cysts and PC. The Arp2/3 complex-associated genes were significantly upregulated in PC, suggesting their role in the malignant transformation of pancreatic cysts. Differential expression of these genes was observed across various cell types in PC, indicating their involvement in the tumor microenvironment. These findings suggest that the Arp2/3 complex-associated genes can serve as potential biomarkers for predicting the malignant transformation of pancreatic cysts, opening new avenues for targeted therapies and early detection strategies.

Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias. We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

Altered DNA methylation patterns in CD4(+) T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (N(patients) = 8, N(controls) = 8) and gene expression (N(patients) = 9, Ncontrols = 10) profiles of CD4(+) T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (N(patients) = 12, N(controls) = 12), but not by gene expression (N(patients) = 21, N(controls) = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (N(patients) = 35) and controls (N(controls) = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4(+) T cells.

Background Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell–cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. Methods CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. Results A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64–10.25]) and ovarian cancer in UKBB (5.53[2.08–8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97–2.96]) compared to females (1.84[1.44–2.37]). Conclusion We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman’s ρ  = 0.32, p  < 10 –16 ); and (3) the disease onset age and heritability are negatively correlated ( ρ  = −0.46, p  < 10 –16 ). Disease heritability and genetic correlations between traits depend on genetics, the environment and their interaction. Here, Jia et al. compute disease prevalence curves and disease embeddings from electronic health records and impute heritability for hundreds of diseases and genetic correlations for thousands of disease pairs.

Background Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).