• Aspergillus flavus
• Molecular dynamics simulation
• Q269L mutant
• Thermal stability
• Uricase

• Aspergillus flavus/enzymology
• Aspergillus flavus/genetics
• Enzyme Stability/genetics
• Urate Oxidase/genetics
• Urate Oxidase/chemistry
• Urate Oxidase/metabolism
• Thermodynamics
• Mutation
• Hydrogen-Ion Concentration
• Fungal Proteins/genetics
• Fungal Proteins/chemistry
• Fungal Proteins/metabolism
• Temperature
• Kinetics
• Molecular Dynamics Simulation

• cns cancer
• immunotherapy
• cytotoxic t cells
• immunological memory

• Glioblastoma/immunology
• Glioblastoma/therapy
• Glioblastoma/pathology
• Glioblastoma/genetics
• Humans
• Animals
• Receptors, Antigen, T-Cell/immunology
• Receptors, Antigen, T-Cell/genetics
• Receptors, Antigen, T-Cell/metabolism
• Mice
• Brain Neoplasms/immunology
• Brain Neoplasms/therapy
• Brain Neoplasms/pathology
• Cell Line, Tumor
• Neoplastic Stem Cells/immunology
• Neoplastic Stem Cells/metabolism
• Cancer Vaccines/immunology
• T-Lymphocytes/immunology
• T-Lymphocytes/transplantation
• Antigens, Neoplasm/immunology
• Receptor-Like Protein Tyrosine Phosphatases, Class 5/immunology
• Receptor-Like Protein Tyrosine Phosphatases, Class 5/genetics
• Receptor-Like Protein Tyrosine Phosphatases, Class 5/metabolism
• HLA-A2 Antigen/immunology
• Immunotherapy, Adoptive/methods
• Xenograft Model Antitumor Assays
• Female

• Swiss Cancer Foundation (Swiss Bridge Award), the Else Kröner Fresenius Foundation (2019_EKMS.49), the University Heidelberg Foundation (Hella Buühler Award), the DFG (German Research Foundation), project 404521405 (SFB1389 UNITE Glioblastoma B03), the DKFZ Hector institute (T-SIRE), the Hertie Foundation, the University of Heidelberg, ExploreTech! the DKTK Joint Funding AMI2GO, the Rolf Schwiete Foundation (2021-009), the HI-TRON strategy project PACESSETTING, the DKTK Joint Funding Program INNOVATION INVENT4GB.
• The DFG, project 404521405 (SFB1389 UNITE Glioblastoma B01) the DKTK Joint Funding AMI2GO, the Rolf Schwiete Foundation (2021-009), the HI-TRON strategy project PACESSETTING, the DKTK Joint Funding Program INNOVATION INVENT4GB.

• cancer driver variations
• colon cancer
• contingency table
• epistasis
• gene pairs
• lung cancer
• survival analysis

• PID2021-126114NB-C44 MCIN/AEI/10.13039/501100011033 and ERDF, A way of making Europe
• PI13/02778 and PI18/00847 Instituto de Salud Carlos III
• Folium Program-INTRES Project, AETIB annual plan 2019 Balearic Islands Health Research Institute
• TALENT-Plus TECH Balearic Islands Health Research Institute
• 201744NR8S Ministero Istruzione, Università e Ricerca

Personalized cancer vaccines targeting neoantigens arising from somatic missense mutations are currently being evaluated for the treatment of various cancers due to their potential to elicit a multivalent, tumor-specific immune response. Several cancers express a low number of neoantigens; in these cases, ensuring the immunotherapeutic potential of each neoantigen-derived epitope (neoepitope) is crucial. In this study, we discovered that therapeutic vaccines targeting immunodominant major histocompatibility complex (MHC) I-restricted neoepitopes require a conjoined helper epitope in order to induce a cytotoxic, neoepitope-specific CD8+ T-cell response. Furthermore, we show that the universally immunogenic helper epitope P30 can fulfill this requisite helper function. Remarkably, conjoined P30 was able to unveil immune and antitumor responses to subdominant MHC I-restricted neoepitopes that were, otherwise, poorly immunogenic. Together, these data provide key insights into effective neoantigen vaccine design and demonstrate a translatable strategy using a universal helper epitope that can improve therapeutic responses to MHC I-restricted neoepitopes.

• ab initio annotation
• functional annotation
• homology-based annotation
• structural annotation

• NRF-2019R1A2C1084308 National Research Foundation of Korea(NRF)

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

• Computational tools and databases
• Machine learning
• Mutations
• Non-synonymous single nucleotide variants
• Performance bias
• Protein function
• Protein stability

Intraductal papillary mucinous neoplasm (IPMN) of pancreas has a high risk to develop into invasive cancer or co‐occur with malignant lesion. Therefore, it is important to assess its malignant risk by less‐invasive approach. Pancreatic juice cell‐free DNA (PJD) would be an ideal material in this purpose, but genetic biomarkers for predicting malignant risk from PJD are not yet established. We here performed deep exome sequencing analysis of PJD from 39 IPMN patients with or without malignant lesion. Somatic alterations and copy number alterations (CNAs) detected in PJD were compared with the histologic grade of IPMN to evaluate their potential as a malignancy marker. Somatic mutations of KRAS, GNAS, TP53, and RNF43 were commonly detected in PJD of IPMNs, but no association with the histologic grades of IPMN was found. Instead, mutation burden was positively correlated with the histologic grade (r = 0.427, P = 0.015). We also observed frequent copy number deletions in 17p13 (TP53) and amplifications in 7q21 and 8q24 (MYC) in PJDs. The amplifications in 7q21 and 8q24 were positively correlated with the histologic grade and most prevalent in the cases of invasive carcinoma (P = 0.002 and 7/11; P = 0.011 and 6/11, respectively). We concluded that mutation burden and CNAs detected in PJD may have potential to assess the malignant progression risk of IPMNs.

• biomarkers
• cell-free nucleic acids
• exome
• pancreatic juice
• pancreatic neoplasms

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.

• analytics
• artificial intelligence
• augmented reality
• genomic data
• immersive
• machine learning
• multidimensional data
• personalised medicine
• virtual reality
• visualisation

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

• disease mechanism
• genetic disease
• network analysis
• variant interpretation

• Biomarkers, Tumor/genetics
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Breast Neoplasms/pathology
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/metabolism
• Colorectal Neoplasms/pathology
• Female
• High-Throughput Nucleotide Sequencing
• Humans
• Lung Neoplasms/genetics
• Lung Neoplasms/metabolism
• Lung Neoplasms/pathology
• Male
• Mutation
• Neoplasm Staging
• Neuroendocrine Tumors/genetics
• Neuroendocrine Tumors/metabolism
• Neuroendocrine Tumors/pathology
• Sarcoma/genetics
• Sarcoma/metabolism
• Sarcoma/pathology
• Sequence Analysis, DNA

• NIHR-RP-011-053 Department of Health
• U54 GM104940 NIGMS NIH HHS
• ULTR001417 NIH HHS
• 1 U54 GM104940 NIH HHS
• National Institute of General Medical Sciences
• National Center for Advancing Translational Sciences

Next-generation sequencing enables simultaneous analysis of hundreds of human genomes associated with a particular phenotype, for example, a disease. These genomes naturally contain a lot of sequence variation that ranges from single-nucleotide variants (SNVs) to large-scale structural rearrangements. In order to establish a functional connection between genotype and disease-associated phenotypes, one needs to distinguish disease drivers from neutral passenger variants. Functional annotation based on experimental assays is feasible only for a limited number of candidate mutations. Thus alternative computational tools are needed. A possible approach to annotating mutations functionally is to consider their spatial location relative to functionally relevant sites in three-dimensional (3D) structures of the harboring proteins. This is impeded by the lack of available protein 3D structures. Complementing experimentally resolved structures with reliable computational models is an attractive alternative. We developed a structure-based approach to characterizing comprehensive sets of non-synonymous single-nucleotide variants (nsSNVs): associated with cancer, non-cancer diseases and putatively functionally neutral. We searched experimentally resolved protein 3D structures for potential homology-modeling templates for proteins harboring corresponding mutations. We found such templates for all proteins with disease-associated nsSNVs, and 51 and 66% of proteins carrying common polymorphisms and annotated benign variants. Many mutations caused by nsSNVs can be found in protein–protein, protein–nucleic acid or protein–ligand complexes. Correction for the number of available templates per protein reveals that protein–protein interaction interfaces are not enriched in either cancer nsSNVs, or nsSNVs associated with non-cancer diseases. Whereas cancer-associated mutations are enriched in DNA-binding proteins, they are rarely located directly in DNA-interacting interfaces. In contrast, mutations associated with non-cancer diseases are in general rare in DNA-binding proteins, but enriched in DNA-interacting interfaces in these proteins. All disease-associated nsSNVs are overrepresented in ligand-binding pockets, and nsSNVs associated with non-cancer diseases are additionally enriched in protein core, where they probably affect overall protein stability.

An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.

• Computational Biology
• Databases, Nucleic Acid
• Databases, Protein
• Genetic Variation
• Genome, Human
• Genomics/statistics & numerical data
• Humans
• Polymorphism, Single Nucleotide
• Software