Somatic mutation in noncoding regions: The sound of silence

Noncoding RNA mutations in cancer

Cancer is driven by both germline and somatic genetic changes. Efforts have been devoted to characterizing essential genetic variations in cancer initiation and development. Most attention has been given to mutations in protein-coding genes and associated regulatory elements such as promoters and enhancers. The development of sequencing technologies and in silico and experimental methods has allowed further exploration of cancer predisposition variants and important somatic mutations in noncoding RNAs, mainly for long noncoding RNAs and microRNAs. Association studies including GWAS have revealed hereditary variations including SNPs and indels in lncRNA or miRNA genes and regulatory regions. These mutations altered RNA secondary structures, expression levels, and target recognition and then conferred cancer predisposition to carriers. Whole-exome/genome sequencing comparing cancer and normal tissues has revealed important somatic mutations in noncoding RNA genes. Mutation hotspots and somatic copy number alterations have been identified in various tumor-associated noncoding RNAs. Increasing focus and effort have been devoted to studying the noncoding region of the genome. The complex genetic network of cancer initiation is being unveiled. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

Mutations in the miR-142 gene are not common in myeloproliferative neoplasms

Recent data indicate that MIR142 is the most frequently mutated miRNA gene and one of the most frequently mutated noncoding elements in all cancers, with mutations occurring predominantly in blood cancers, especially diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma. Functional analyses show that the MIR142 alterations have profound consequences for lympho- and myelopoiesis. Furthermore, one of the targets downregulated by miR-142-5p is CD274, which encodes PD-L1 that is elevated in many cancer types, including myeloproliferative neoplasms (MPNs). To extend knowledge about the occurrence of MIR142 mutations, we sequenced the gene in a large panel of MPNs [~ 700 samples, including polycythemia vera, essential thrombocythemia, primary myelofibrosis (PMF), and chronic myeloid leukemia], neoplasm types in which such mutations have never been tested, and in panels of acute myeloid leukemia (AML), and chronic lymphocytic leukemia (CLL). We identified 3 mutations (one in a PMF sample and two others in one CLL sample), indicating that MIR142 mutations are rare in MPNs. In summary, mutations in MIR142 are rare in MPNs; however, in specific subtypes, such as PMF, their frequency may be comparable to that observed in CLL or AML.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

Genetics in the ADHD Clinic: How Can Genetic Testing Support the Current Clinical Practice?

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with a childhood prevalence of 5%. In about two-thirds of the cases, ADHD symptoms persist into adulthood and often cause significant functional impairment. Based on the results of family and twin studies, the estimated heritability of ADHD approximates 80%, suggests a significant genetic component in the etiological background of the disorder; however, the potential genetic effects on disease risk, symptom severity, and persistence are unclear. This article provides a brief review of the genome-wide and candidate gene association studies with a focus on the clinical aspects, summarizing findings of ADHD disease risk, ADHD core symptoms as dimensional traits, and other traits frequently associated with ADHD, which may contribute to the susceptibility to other comorbid psychiatric disorders. Furthermore, neuropsychological impairment and measures from neuroimaging and electrophysiological paradigms, emerging as potential biomarkers, also provide a prominent target for molecular genetic studies, since they lie in the pathway from genes to behavior; therefore, they can contribute to the understanding of the underlying neurobiological mechanisms and the interindividual heterogeneity of clinical symptoms. Beyond the aforementioned aspects, throughout the review, we also give a brief summary of the genetic results, including polygenic risk scores that can potentially predict individual response to different treatment options and may offer a possibility for personalized treatment for the therapy of ADHD in the future.

miRMut: Annotation of mutations in miRNA genes from human whole-exome or whole-genome sequencing

Here, we present the miRMut protocol to annotate mutations found in miRNA genes based on whole-exome sequencing (WES) or whole-genome sequencing (WGS) results. The pipeline assigns mutation characteristics, including miRNA gene IDs (miRBase and MirGeneDB), mutation localization within the miRNA precursor structure, potential RNA-binding motif disruption, the ascription of mutation according to Human Genome Variation Society (HGVS) nomenclature, and miRNA gene characteristics, such as miRNA gene confidence and miRNA arm balance. The pipeline includes creating tabular and graphical summaries. For complete details on the use and execution of this protocol, please refer to Urbanek-Trzeciak et al. (2020).