Switching or reclassifying medicines with established safety profiles from prescription to non-prescription aims to increase timely consumer access to medicines, reduce under-treatment and enhance self-management. However, risks include suboptimal therapy and adverse effects. With a long-standing government policy supporting switching or reclassifying medicines from prescription to non-prescription, the United Kingdom is believed to lead the world in switch, but evidence for this is inconclusive. Interest in switching medicines for certain long-term conditions has arisen in the United Kingdom, United States, and Europe, but such switches have been contentious. The objective of this study was then to provide a comprehensive comparison of progress in switch for medicines across six developed countries: the United States; the United Kingdom; Australia; Japan; the Netherlands; and New Zealand.
A list of prescription-to-non-prescription medicine switches was systematically compiled. Three measures were used to compare switch activity across the countries: “progressive” switches from 2003 to 2013 (indicating incremental consumer benefit over current non-prescription medicines); “first-in-world” switches from 2003 to 2013; and switch date comparisons for selected medicines.
New Zealand was the most active in progressive switches from 2003 to 2013, with the United Kingdom and Japan not far behind. The United States, Australia and the Netherlands showed the least activity in this period. Few medicines for long-term conditions were switched, even in the United Kingdom and New Zealand where first-in-world switches were most likely. Switch of certain medicines took considerably longer in some countries than others. For example, a consumer in the United Kingdom could self-medicate with a non-sedating antihistamine 19 years earlier than a consumer in the United States.
Proactivity in medicines switching, most notably in New Zealand and the United Kingdom, questions missed opportunities to enhance consumers' self-management in countries such as the United States.
Prior expression quantitative trait locus (eQTL) studies have demonstrated heritable variation determining differences in gene expression. The majority of eQTL studies were based on cell lines and normal tissues. We performed cis-eQTL analysis using glioblastoma multiforme (GBM) data sets obtained from The Cancer Genome Atlas (TCGA) to systematically investigate germline variation’s contribution to tumor gene expression levels. We identified 985 significant cis-eQTL associations (FDR<0.05) mapped to 978 SNP loci and 159 unique genes. Approximately 57% of these eQTLs have been previously linked to the gene expression in cell lines and normal tissues; 43% of these share cis associations known to be associated with functional annotations. About 25% of these cis-eQTL associations are also common to those identified in Breast Cancer from a recent study. Further investigation of the relationship between gene expression and patient clinical information identified 13 eQTL genes whose expression level significantly correlates with GBM patient survival (p<0.05). Most of these genes are also differentially expressed in tumor samples and organ-specific controls (p<0.05). Our results demonstrated a significant relationship of germline variation with gene expression levels in GBM. The identification of eQTLs-based expression associated survival might be important to the understanding of genetic contribution to GBM cancer prognosis.
Cbls are RING ubiquitin ligases that attenuate receptor tyrosine kinase (RTK) signal transduction. Cbl ubiquitination activity is stimulated by phosphorylation of a linker helix region (LHR) tyrosine residue. To elucidate the mechanism of activation, we determined structures of human CBL, a CBL–substrate peptide complex, and a phosphoTyr371-CBL–E2–substrate peptide complex, and compared them with the known structure of a CBL–E2–substrate peptide complex. Structural and biochemical analyses show that CBL adopts an autoinhibited RING conformation where the RING’s E2-binding surface associates with CBL to reduce E2 affinity. PhosphoTyr371 activates CBL by inducing LHR conformational changes that eliminate autoinhibition, flip the RING domain and E2 into proximity of the substrate-binding site, and transform the RING domain into an enhanced E2-binding module. This activation is required for RTK ubiquitination. Our results present a mechanism for regulation of c-Cbl’s activity by autoinhibition and phosphorylation-induced activation.
c-Cbl; RING; E3; E2 binding; autoinhibition; conformational change; phosphorylation
RING ubiquitin ligases (E3s) recruit E2 thioesterified with Ub to facilitate Ub transfer to a target. Certain RING E3s dimerize to form active ligases but structural evidence on how this process promotes Ub transfer is lacking. Several members of the baculovirus inhibitor of apoptosis repeat-containing (BIRC) family of proteins function as dimeric RING E3s in the regulation of cell death. Here we report the structure of the human dimeric RING domain from BIRC7 in complex with the E2 UbcH5B covalently linked to Ub at its active site (UbcH5B~Ub). In addition to the known E2–RING contacts, the structure reveals extensive non-covalent donor Ub interactions with UbcH5B and both subunits of the RING domain dimer. Mutations that disrupt these non-covalent interactions or RING dimerization reduce UbcH5B~Ub binding affinity and ubiquitination activity. Moreover, NMR analyses demonstrate that BIRC7 binding to UbcH5B~Ub induces peak shift perturbations in the donor Ub consistent with the crystallographically-observed BIRC7–Ub interactions. Our results provide structural insights into how dimeric RING E3s recruit E2~Ub and optimize the donor Ub configuration for transfer.
Inhibitor of apoptosis; BIRC7; E3; E2 ubiquitin conjugate; RING dimerization
Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research.
Materials and methods
The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types.
The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research.
The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts.
The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.
Semantics; knowledge representation (computer); interoperability; life sciences; information model; knowledge bases; knowledge representations; data models; clinical; OMICS; genomics; cancer genomics
Genomic profiling has identified a subtype of high-risk B-progenitor acute lymphoblastic leukemia (B-ALL) with alteration of IKZF1, a gene expression profile similar to BCR-ABL1-positive ALL and poor outcome (Ph-like ALL). The genetic alterations that activate kinase signaling in Ph-like ALL are poorly understood. We performed transcriptome and whole genome sequencing on 15 cases of Ph-like ALL, and identified rearrangements involving ABL1, JAK2, PDGFRB, CRLF2 and EPOR, activating mutations of IL7R and FLT3, and deletion of SH2B3, which encodes the JAK2 negative regulator LNK. Importantly, several of these alterations induce transformation that is attenuated with tyrosine kinase inhibitors, suggesting the treatment outcome of these patients may be improved with targeted therapy.
Little is known about which attributes the patients need when they wish to maximise their capability to partner safely in healthcare. We aimed to identify these attributes from the perspective of key opinion leaders.
Delphi study involving indirect group interaction through a structured two-round survey.
International electronic survey.
11 (65%) of the 17 invited internationally recognised experts on patient safety completed the study.
50 patient attributes were rated by the Delphi panel for their ability to contribute maximally to safe health care.
The panellists agreed that 13 attributes are important for patients who want to maximise the role of safe partners. These domains relate to: autonomy, awareness, conscientiousness, knowledge, rationality, responsiveness and vigilance; for example, important attributes of autonomy include the ability to speak up, freedom to act and ability to act independently. Spanning seven domains, the attributes emphasise intellectual attributes and, to a lesser extent, moral attributes.
Whereas current safety discourses emphasise attributes of professionals, this study identified the patient attributes which key opinion leaders believe can maximise the capability of patients to partner safely in healthcare. Further research is needed that asks patients about the attributes they believe are most important.
Non-alcoholic fatty liver disease (NAFLD) is a common liver disease; the histological spectrum of which ranges from steatosis to steatohepatitis. Nonalcoholic steatohepatitis (NASH) often leads to cirrhosis and development of hepatocellular carcinoma. To better understand pathogenesis of NAFLD, we performed the pathway of distinction analysis (PoDA) on a genome-wide association study dataset of 250 non-Hispanic white female adult patients with NAFLD, who were enrolled in the NASH Clinical Research Network (CRN) Database Study, to investigate whether biologic process variation measured through genomic variation of genes within these pathways was related to the development of steatohepatitis or cirrhosis. Pathways such as Recycling of eIF2:GDP, biosynthesis of steroids, Terpenoid biosynthesis and Cholesterol biosynthesis were found to be significantly associated with NASH. SNP variants in Terpenoid synthesis, Cholesterol biosynthesis and biosynthesis of steroids were associated with lobular inflammation and cytologic ballooning while those in Terpenoid synthesis were also associated with fibrosis and cirrhosis. These were also related to the NAFLD activity score (NAS) which is derived from the histological severity of steatosis, inflammation and ballooning degeneration. Eukaryotic protein translation and recycling of eIF2:GDP related SNP variants were associated with ballooning, steatohepatitis and cirrhosis. Il2 signaling events mediated by PI3K, Mitotic metaphase/anaphase transition, and Prostanoid ligand receptors were also significantly associated with cirrhosis. Taken together, the results provide evidence for additional ways, beyond the effects of single SNPs, by which genetic factors might contribute to the susceptibility to develop a particular phenotype of NAFLD and then progress to cirrhosis. Further studies are warranted to explain potential important genetic roles of these biological processes in NAFLD.
Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancers. This is attributable to the late stage at which the majority of ovarian cancers are diagnosed, coupled with the low and variable response of advanced tumors to standard chemotherapies. To date, clinically useful predictors of treatment response remain lacking. Identifying the genetic determinants of ovarian cancer survival and treatment response is crucial to the development of prognostic biomarkers and personalized therapies that may improve outcomes for the late-stage patients who comprise the majority of cases.
To identify constitutional genetic variations contributing to ovarian cancer mortality, we systematically investigated associations between germline polymorphisms and ovarian cancer survival using data from The Cancer Genome Atlas Project (TCGA). Using stage-stratified Cox proportional hazards regression, we examined 650,000 SNP loci for association with survival. We additionally examined whether the association of significant SNPs with survival was modified by somatic alterations.
Germline polymorphisms at rs4934282 (AGAP11/C10orf116) and rs1857623 (DNAH14) were associated with stage-adjusted survival ( = 1.12e-07 and 1.80e-07, FDR = 1.2e-04 and 2.4e-04, respectively). A third SNP, rs4869 (C10orf116), was additionally identified as significant in the exome sequencing data; it is in near-perfect LD with rs4934282. The associations with survival remained significant when somatic alterations.
Discovery analysis of TCGA data reveals germline genetic variations that may play a role in ovarian cancer survival even among late-stage cases. The significant loci are located near genes previously reported as having a possible relationship to platinum and taxol response. Because the variant alleles at the significant loci are common (frequencies for rs4934282 A/C alleles = 0.54/0.46, respectively; rs1857623 A/G alleles = 0.55/0.45, respectively) and germline variants can be assayed noninvasively, our findings provide potential targets for further exploration as prognostic biomarkers and individualized therapies.
TAF7, a component of the TFIID complex that nucleates the assembly of transcription preinitiation complexes, also independently interacts with and regulates the enzymatic activities of other transcription factors, including P-TEFb, TFIIH, and CIITA, ensuring an orderly progression in transcription initiation. Since not all TAFs are required in terminally differentiated cells, we examined the essentiality of TAF7 in cells at different developmental stages in vivo. Germ line disruption of the TAF7 gene is embryonic lethal between 3.5 and 5.5 days postcoitus. Mouse embryonic fibroblasts with TAF7 deleted cease transcription globally and stop proliferating. In contrast, whereas TAF7 is essential for the differentiation and proliferation of immature thymocytes, it is not required for subsequent, proliferation-independent differentiation of lineage committed thymocytes or for their egress into the periphery. TAF7 deletion in peripheral CD4 T cells affects only a small number of transcripts. However, T cells with TAF7 deleted are not able to undergo activation and expansion in response to antigenic stimuli. These findings suggest that TAF7 is essential for proliferation but not for proliferation-independent differentiation.
As a style of information processing, intuition involves implicit perceptual and cognitive processes that can be quickly and automatically executed without conscious mental will, such that people know more than they can describe. Patient intuition can influence patient and clinician decision-making and behavior. However, physicians may not always see patient intuition as credible or important, and its management in the clinical setting is poorly understood. This paper takes a step toward suggesting conditions under which patient intuition should be taken seriously. These conditions relate to the credibility or accuracy of the intuitive beliefs held by the patient, and their significance to the patient. Credibility may be increased when the intuitions of patients (1) reflect their individualized knowledge, (2) can complement the common absence of scientific evidence in managing health problems, and (3) can quickly and effectively process key information in complex cognitive tasks. Even intuitions that lack credibility can be subjectively rational and meaningful to patients, and help to shape the decisions they and clinicians make.
intuition; decision making; patients
Summary: Bambino is a variant detector and graphical alignment viewer for next-generation sequencing data in the SAM/BAM format, which is capable of pooling data from multiple source files. The variant detector takes advantage of SAM-specific annotations, and produces detailed output suitable for genotyping and identification of somatic mutations. The assembly viewer can display reads in the context of either a user-provided or automatically generated reference sequence, retrieve genome annotation features from a UCSC genome annotation database, display histograms of non-reference allele frequencies, and predict protein-coding changes caused by SNPs.
Availability: Bambino is written in platform-independent Java and available from https://cgwb.nci.nih.gov/goldenPath/bamview/documentation/index.html, along with documentation and example data. Bambino may be launched online via Java Web Start or downloaded and run locally.
Relapsed acute lymphoblastic leukaemia (ALL) is a leading cause of death due to disease in young people, but the biologic determinants of treatment failure remain poorly understood. Recent genome-wide profiling of structural DNA alterations in ALL have identified multiple submicroscopic somatic mutations targeting key cellular pathways1,2, and have demonstrated substantial evolution in genetic alterations from diagnosis to relapse3. However, detailed analysis of sequence mutations in ALL has not been performed. To identify novel mutations in relapsed ALL, we resequenced 300 genes in matched diagnosis and relapse samples from 23 patients with ALL. This identified 52 somatic non-synonymous mutations in 32 genes, many of which were novel, including the transcriptional coactivators CREBBP and NCOR1, the transcription factors ERG, SPI1, TCF4 and TCF7L2, components of the Ras signalling pathway, histone genes, genes involved in histone modification (CREBBP and CTCF), and genes previously shown to be targets of recurring DNA copy number alteration in ALL. Analysis of an extended cohort of 71 diagnosis-relapse cases and 270 acute leukaemia cases that did not relapse found that 18.3% of relapse cases had sequence or deletion mutations of CREBBP, which encodes the transcriptional coactivator and histone acetyltransferase (HAT) CREB-binding protein (CBP)4. The mutations were either present at diagnosis or acquired at relapse, and resulted in truncated alleles or deleterious substitutions in conserved residues of the HAT domain. Functionally, the mutations impaired histone acetylation and transcriptional regulation of CREBBP targets, including glucocorticoid responsive genes. Several mutations acquired at relapse were detected in subclones at diagnosis, suggesting that the mutations may confer resistance to therapy. These results extend the landscape of genetic alterations in leukaemia, and identify mutations targeting transcriptional and epigenetic regulation as a mechanism of resistance in ALL.
Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi–SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi–SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single–SNP and SNP–set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.
We present a novel method for multi–SNP analysis of genome-wide association studies. The method is motivated by the intuition that, if a set of SNPs is associated with disease, cases and controls will exhibit more within-group similarity than across-group similarity for the SNPs in the set of interest. Our method, Pathways of Distinction Analysis (PoDA), uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit the distinction of cases from controls. By systematically applying the method to all pathways of potential interest, we can identify pathways containing SNPs for which the cases and controls are distinguished and infer those pathways' role in disease. We detail the PoDA method and describe its results in breast and liver cancer GWAS data, demonstrating its utility as a method for systems-level analysis of GWAS data.
The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.
Molecular abundance data is used to calculate two metrics - 'activity' and 'consistency' - for each pathway in a set of more than 500 canonical molecular pathways (source: Pathway Interaction Database, http://pid.nci.nih.gov). The tool then allows a detailed exploration of these metrics through integrated visualization of pathway components and structure, hierarchical clustering of pathways and samples, and statistical analyses designed to detect associations between pathway behavior and clinical features.
The PathOlogist provides a straightforward means to identify the functional processes, rather than individual molecules, that are altered in disease. The statistical power and biologic significance of this approach are made easily accessible to laboratory researchers and informatics analysts alike. Here we show as an example, how the PathOlogist can be used to establish pathway signatures that robustly differentiate breast cancer cell lines based on response to treatment.
BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.
pathway data integration; pathway database; standard exchange format; ontology; information system
Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.
High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.
Tamoxifen was approved for breast cancer risk reduction in high-risk women based on the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (P-1:BCPT), which showed 50% fewer breast cancers with tamoxifen versus placebo, supporting tamoxifen's efficacy in preventing breast cancer. Poor metabolizing CYP2D6 variants are currently the subject of intensive scrutiny regarding their impact on clinical outcomes in the adjuvant setting. Our study extends to variants in a wider spectrum of tamoxifen-metabolizing genes and applies to the prevention setting.
Our case-only study, nested within P-1:BCPT, explored associations of polymorphisms in estrogen/tamoxifen-metabolizing genes with responsiveness to preventive tamoxifen. Thirty-nine candidate polymorphisms in 17 candidate genes were genotyped in 249 P-1:BCPT cases.
CYP2D6_C1111T, individually and within a CYP2D6 haplotype, showed borderline significant association with treatment arm. Path analysis of the entire tamoxifen pathway gene network showed that the tamoxifen pathway model was consistent with the pattern of observed genotype variability within the placebo-arm dataset. However, correlation of variations in genes in the tamoxifen arm differed significantly from the predictions of the tamoxifen pathway model. Strong correlations between allelic variation in the tamoxifen pathway at CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2, in addition to CYP2D6 and its adjacent genes, were seen in the placebo-arm but not the tamoxifen-arm. In conclusion, beyond reinforcing a role for CYP2D6 in tamoxifen response, our pathway analysis strongly suggests that specific combinations of allelic variants in other genes make major contributions to the tamoxifen-resistance phenotype.
Breast cancer; tamoxifen resistance; chemoprevention; pathway analysis; breast cancer risk; genomic polymorphisms
An unhappy patient suggests poor quality care, but Glyn Elwyn and colleagues point out that using measures of satisfaction to assess health providers is not without problems
Purpose: Tamoxifen was approved for breast cancer risk reduction in high-risk women based on the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (P-1:BCPT), which showed 50% fewer breast cancers with tamoxifen versus placebo, supporting tamoxifen's efficacy in preventing breast cancer. Poor metabolizing CYP2D6 variants are currently the subject of intensive scrutiny regarding their impact on clinical outcomes in the adjuvant setting. Our study extends to variants in a wider spectrum of tamoxifen-metabolizing genes and applies to the prevention setting. Methods: Our case-only study, nested within P-1:BCPT, explored associations of polymorphisms in estrogen/tamoxifen-metabolizing genes with responsiveness to preventive tamoxifen. Thirty-nine candidate polymorphisms in 17 candidate genes were genotyped in 249 P-1:BCPT cases. Results: CVP2D6_C1111T, individually and within a CYP2D6 haplotype, showed borderline significant association with treatment arm. Path analysis of the entire tamoxifen pathway gene network showed that the tamoxifen pathway model was consistent with the pattern of observed genotype variability within the placebo-arm dataset. However, correlation of variations in genes in the tamoxifen arm differed significantly from the predictions of the tamoxifen pathway model. Strong correlations between allelic variation in the tamoxifen pathway at CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2, in addition to CYP2D6 and its adjacent genes, were seen in the placebo-arm but not the tamoxifen-arm. In conclusion, beyond reinforcing a role for CYP2D6 in tamoxifen response, our pathway analysis strongly suggests that specific combinations of allelic variants in other genes make major contributions to the tamoxifen-resistance phenotype.
Breast cancer; tamoxifen resistance; chemoprevention; pathway analysis; breast cancer risk; genomic
Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set, and ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient’s tumor. Here we present Rembrandt, Repository of Molecular BRAin Neoplasia DaTa, a cancer clinical genomics database and a web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising nearly 566 gene expression arrays, 834 copy number arrays and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that REMBRANDT represents a prototype of how high throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.
Rembrandt; personalized medicine; translational research; clinical genomics; data integration
Background. Common but seldom published are Parkinson's disease (PD) medication errors involving late, extra, or missed doses. These errors can reduce medication effectiveness and the quality of life of people with PD and their caregivers. Objective. To explore lay perspectives of factors contributing to medication timing errors for PD in hospital and community settings. Design and Methods. This qualitative research purposively sampled individuals with PD, or a proxy of their choice, throughout New Zealand during 2008-2009. Data collection involved 20 semistructured, personal interviews by telephone. A general inductive analysis of the data identified core insights consistent with the study objective. Results. Five themes help to account for possible timing adherence errors by people with PD, their caregivers or professionals. The themes are the abrupt withdrawal of PD medication; wrong, vague or misread instructions; devaluation of the lay role in managing PD medications; deficits in professional knowledge and in caring behavior around PD in formal health care settings; and lay forgetfulness. Conclusions. The results add to the limited published research on medication errors in PD and help to confirm anecdotal experience internationally. They indicate opportunities for professionals and lay people to work together to reduce errors in the timing of medication for PD in hospital and community settings.