Human variation databases

More than 100 000 human genetic variations have been described in various genes that are associated with a wide variety of diseases. Such data provides invaluable information for both clinical medicine and basic science. A number of locus-specific databases have been developed to exploit this huge amount of data. However, the scope, format and content of these databases differ strongly and as no standard for variation databases has yet been adopted, the way data is presented varies enormously. This review aims to give an overview of current resources for human variation data in public and commercial resources

VariVis: a visualisation toolkit for variation databases

<p>Abstract</p> <p>Background</p> <p>With the completion of the Human Genome Project and recent advancements in mutation detection technologies, the volume of data available on genetic variations has risen considerably. These data are stored in online variation databases and provide important clues to the cause of diseases and potential side effects or resistance to drugs. However, the data presentation techniques employed by most of these databases make them difficult to use and understand.</p> <p>Results</p> <p>Here we present a visualisation toolkit that can be employed by online variation databases to generate graphical models of gene sequence with corresponding variations and their consequences. The VariVis software package can run on any web server capable of executing Perl CGI scripts and can interface with numerous Database Management Systems and "flat-file" data files. VariVis produces two easily understandable graphical depictions of any gene sequence and matches these with variant data. While developed with the goal of improving the utility of human variation databases, the VariVis package can be used in any variation database to enhance utilisation of, and access to, critical information.</p

Characterization of gene mutations and copy number changes in acute myeloid leukemia using a rapid target enrichment protocol

Prognostic stratification is critical for making therapeutic decisions and maximizing survival of patients with acute myeloid leukemia. Advances in the genomics of acute myeloid leukemia have identified several recurrent gene mutations whose prognostic impact is being deciphered. We used HaloPlex target enrichment and Illumina-based next generation sequencing to study 24 recurrently mutated genes in 42 samples of acute myeloid leukemia with a normal karyotype. Read depth varied between and within genes for the same sample, but was predictable and highly consistent across samples. Consequently, we were able to detect copy number changes, such as an interstitial deletion of BCOR, three MLL partial tandem duplications, and a novel KRAS amplification. With regards to coding mutations, we identified likely oncogenic variants in 41 of 42 samples. NPM1 mutations were the most frequent, followed by FLT3, DNMT3A and TET2. NPM1 and FLT3 indels were reported with good efficiency. We also showed that DNMT3A mutations can persist post-chemotherapy and in 2 cases studied at diagnosis and relapse, we were able to delineate the dynamics of tumor evolution and give insights into order of acquisition of variants. HaloPlex is a quick and reliable target enrichment method that can aid diagnosis and prognostic stratification of acute myeloid leukemia patients.This project was funded by the Wellcome Trust. NB is a fellow of the European Hematology Association and was supported by the Academy of Medical Sciences. EP is a European Hematology Association Advanced Research Fellow. GV is a Wellcome Trust Senior Fellow in Clinical Science. IV is funded by Spanish Ministerio de Economía y Competitividad subprograma Ramón y Cajal

Mutational analysis of disease relapse in patients allografted for acute myeloid leukemia

Disease relapse is the major cause of treatment failure after allogeneic stem cell transplantation (allo-SCT) in acute myeloid leukemia (AML). To identify AML-associated genes prognostic of AML relapse post–allo-SCT, we resequenced 35 genes in 113 adults at diagnosis, 49 of whom relapsed. Two hundred sixty-two mutations were detected in 102/113 (90%) patients. An increased risk of relapse was observed in patients with mutations in WT1 (P = .018), DNMT3A (P = .045), FLT3 ITD (P = .071), and TP53 (P = .06), whereas mutations in IDH1 were associated with a reduced risk of disease relapse (P = .018). In 29 patients, we additionally compared mutational profiles in bone marrow at diagnosis and relapse to study changes in clonal structure at relapse. In 13/29 patients, mutational profiles altered at relapse. In 9 patients, mutations present at relapse were not detected at diagnosis. In 15 patients, additional available pre–allo-SCT samples demonstrated that mutations identified posttransplant but not at diagnosis were detectable immediately prior to transplant in 2 of 15 patients. Taken together, these observations, if confirmed in larger studies, have the potential to inform the design of novel strategies to reduce posttransplant relapse highlighting the potential importance of post–allo-SCT interventions with a broad antitumor specificity in contrast to targeted therapies based on mutational profile at diagnosis

Heterozygous missense variants of SPTBN2 are a frequent cause of congenital cerebellar ataxia

Heterozygous missense variants in the SPTBN2 gene, encoding the non-erythrocytic beta spectrin 2 subunit (beta-III spectrin), have been identified in autosomal dominant spinocerebellar ataxia type 5 (SCA5), a rare adult-onset neurodegenerative disorder characterized by progressive cerebellar ataxia, whereas homozygous loss of function variants in SPTBN2 have been associated with early onset cerebellar ataxia and global developmental delay (SCAR14). Recently, heterozygous SPTBN2 missense variants have been identified in a few patients with an early-onset ataxic phenotype. We report five patients with non-progressive congenital ataxia and psychomotor delay, 4/5 harboring novel heterozygous missense variants in SPTBN2 and one patient with compound heterozygous SPTBN2 variants. With an overall prevalence of 5% in our cohort of unrelated patients screened by targeted next generation sequencing (NGS) for congenital or early-onset cerebellar ataxia, this study indicates that both dominant and recessive mutations of SPTBN2 together with CACNA1A and ITPR1, are a frequent cause of early-onset/congenital non-progressive ataxia and that their screening should be implemented in this subgroup of disorders

Mutational analysis in podocin-associated hereditary nephrotic syndrome in Polish patients: founder effect in the Kashubian population

Hereditary nephrotic syndrome is caused by mutations in a number of different genes, the most common being NPHS2. The aim of the study was to identify the spectrum of NPHS2 mutations in Polish patients with the disease. A total of 141 children with steroid-resistant nephrotic syndrome (SRNS) were enrolled in the study. Mutational analysis included the entire coding sequence and intron boundaries of the NPHS2 gene. Restriction fragment length polymorphism (RFLP) and TaqMan genotyping assay were applied to detect selected NPHS2 sequence variants in 575 population-matched controls. Twenty patients (14 %) had homozygous or compound heterozygous NPHS2 mutations, the most frequent being c.1032delT found in 11 children and p.R138Q found in four patients. Carriers of the c.1032delT allele were exclusively found in the Pomeranian (Kashubian) region, suggesting a founder effect origin. The 14 % NPHS2 gene mutation detection rate is similar to that observed in other populations. The heterogeneity of mutations detected in the studied group confirms the requirement of genetic testing the entire NPHS2 coding sequence in Polish patients, with the exception of Kashubs, who should be initially screened for the c.1032delT deletion

Linking genes to diseases: it's all in the data

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches

A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma.

Genomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies

Patterns of genomic change in residual disease after neoadjuvant chemotherapy for estrogen receptor-positive and HER2-negative breast cancer

Background: Treatment of patients with residual disease after neoadjuvant chemotherapy for breast cancer is an unmet clinical need. We hypothesised that tumour subclones showing expansion in residual disease after chemotherapy would contain mutations conferring drug resistance. Methods: We studied oestrogen receptor and/or progesterone receptor-positive, HER2-negative tumours from 42 patients in the EORTC 10994/BIG 00-01 trial who failed to achieve a pathological complete response. Genes commonly mutated in breast cancer were sequenced in pre and post-treatment samples. Results: Oncogenic driver mutations were commonest in PIK3CA (38% of tumours), GATA3 (29%), CDH1 (17%), TP53 (17%) and CBFB (12%); and amplification was commonest for CCND1 (26% of tumours) and FGFR1 (26%). The variant allele fraction frequently changed after treatment, indicating that subclones had expanded and contracted, but there were changes in both directions for all of the commonly mutated genes. Conclusions: We found no evidence that expansion of clones containing recurrent oncogenic driver mutations is responsible for resistance to neoadjuvant chemotherapy. The persistence of classic oncogenic mutations in pathways for which targeted therapies are now available highlights their importance as drug targets in patients who have failed chemotherapy but provides no support for a direct role of driver oncogenes in resistance to chemotherapy. ClinicalTrials.gov: EORTC 10994/BIG 1-00 Trial registration number NCT00017095.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe