An IL28B Genotype-Based Clinical Prediction Model for Treatment of Chronic Hepatitis C

BACKGROUND:Genetic variation in IL28B and other factors are associated with sustained virological response (SVR) after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC). Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables. METHODS:HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR) and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC). RESULTS:Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p<.0001); the model also included HCV RNA (log10 IU/ml), AST:ALT ratio, Ishak fibrosis score and prior ribavirin treatment. For this model AUC was 78.5%, compared to 73.0% for a model restricted to the four clinical predictors and 60.0% for a model restricted to IL28B genotype (p<0.001). Subjects with a predicted probability of SVR <10% had an observed SVR rate of 3.8%; subjects with a predicted probability >10% (43.3% of subjects) had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study. CONCLUSION:A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC

Attitudes and Practices Among Internists Concerning Genetic Testing

Many questions remain concerning whether, when, and how physicians order genetic tests, and what factors are involved in their decisions. We surveyed 220 internists from two academic medical centers about their utilization of genetic testing. Rates of genetic utilizations varied widely by disease. Respondents were most likely to have ordered tests for Factor V Leiden (16.8 %), followed by Breast/Ovarian Cancer (15.0 %). In the past 6 months, 65 % had counseled patients on genetic issues, 44 % had ordered genetic tests, 38.5 % had referred patients to a genetic counselor or geneticist, and 27.5 % had received ads from commercial labs for genetic testing. Only 4.5 % had tried to hide or disguise genetic information, and <2 % have had patients report genetic discrimination. Only 53.4 % knew of a geneticist/genetic counselor to whom to refer patients. Most rated their knowledge as very/somewhat poor concerning genetics (73.7 %) and guidelines for genetic testing (87.1 %). Most felt needs for more training on when to order tests (79 %), and how to counsel patients (82 %), interpret results (77.3 %), and maintain privacy (80.6 %). Physicians were more likely to have ordered a genetic test if patients inquired about genetic testing (p  < .001), and if physicians had a geneticist/genetic counselor to whom to refer patients (p  < .002), had referred patients to a geneticist/genetic counselor in the past 6 months, had more comfort counseling patients about testing (p  < .019), counseled patients about genetics, larger practices (p  < .032), fewer African‐American patients (p  < .027), and patients who had reported genetic discrimination (p  < .044). In a multiple logistic regression, ordering a genetic test was associated with patients inquiring about testing, having referred patients to a geneticist/genetic counselor and knowing how to order tests. These data suggest that physicians recognize their knowledge deficits, and are interested in training. These findings have important implications for future medical practice, research, and education

Inclusion of diverse populations in genomic research and health services: Genomix workshop report

Clinical genetic services and genomic research are rapidly developing but, historically, those with the greatest need are the least to benefit from these advances. This encompasses low-income communities, including those from ethnic minority and indigenous backgrounds. The “Genomix” workshop at the European Society of Human Genetics (ESHG) 2016 conference offered the opportunity to consider possible solutions for these disparities from the experiences of researchers and genetic healthcare practitioners working with underserved communities in the USA, UK and Australia. Evident from the workshop and corresponding literature is that a multi-faceted approach to engaging communities is essential. This needs to be complemented by redesigning healthcare systems that improves access and raises awareness of the needs of these communities. At a more strategic level, institutions involved in funding research, commissioning and redesigning genetic health services also need to be adequately represented by underserved populations with intrinsic mechanisms to disseminate good practice and monitor participation. Further, as genomic medicine is mainstreamed, educational programmes developed for clinicians should incorporate approaches to alleviate disparities in accessing genetic services and improving study participation

Dissecting Inflammatory Complications in Critically Injured Patients by Within-Patient Gene Expression Changes: A Longitudinal Clinical Genomics Study

By studying gene expression changes over time in a cohort of trauma patients, Keyur Desai and colleagues identify genes and pathways strongly associated with longer-term complications, which could lead to improved outcome prediction in the first 80 hours after injury

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge

Paired Tumor and Normal Whole Genome Sequencing of Metastatic Olfactory Neuroblastoma

Olfactory neuroblastoma (ONB) is a rare cancer of the sinonasal tract with little molecular characterization. We performed whole genome sequencing (WGS) on paired normal and tumor DNA from a patient with metastatic-ONB to identify the somatic alterations that might be drivers of tumorigenesis and/or metastatic progression.Genomic DNA was isolated from fresh frozen tissue from a metastatic lesion and whole blood, followed by WGS at >30X depth, alignment and mapping, and mutation analyses. Sanger sequencing was used to confirm selected mutations. Sixty-two somatic short nucleotide variants (SNVs) and five deletions were identified inside coding regions, each causing a non-synonymous DNA sequence change. We selected seven SNVs and validated them by Sanger sequencing. In the metastatic ONB samples collected several months prior to WGS, all seven mutations were present. However, in the original surgical resection specimen (prior to evidence of metastatic disease), mutations in KDR, MYC, SIN3B, and NLRC4 genes were not present, suggesting that these were acquired with disease progression and/or as a result of post-treatment effects.This work provides insight into the evolution of ONB cancer cells and provides a window into the more complex factors, including tumor clonality and multiple driver mutations

Exome Sequencing and Genetic Testing for MODY

Context: Genetic testing for monogenic diabetes is important for patient care. Given the extensive genetic and clinical heterogeneity of diabetes, exome sequencing might provide additional diagnostic potential when standard Sanger sequencing-based diagnostics is inconclusive. Objective: The aim of the study was to examine the performance of exome sequencing for a molecular diagnosis of MODY in patients who have undergone conventional diagnostic sequencing of candidate genes with negative results. Research Design and Methods: We performed exome enrichment followed by high-throughput sequencing in nine patients with suspected MODY. They were Sanger sequencing-negative for mutations in the HNF1A, HNF4A, GCK, HNF1B and INS genes. We excluded common, non-coding and synonymous gene variants, and performed in-depth analysis on filtered sequence variants in a pre-defined set of 111 genes implicated in glucose metabolism. Results: On average, we obtained 45 X median coverage of the entire targeted exome and found 199 rare coding variants per individual. We identified 0–4 rare non-synonymous and nonsense variants per individual in our a priori list of 111 candidate genes. Three of the variants were considered pathogenic (in ABCC8, HNF4A and PPARG, respectively), thus exome sequencing led to a genetic diagnosis in at least three of the nine patients. Approximately 91% of known heterozygous SNPs in the target exomes were detected, but we also found low coverage in some key diabetes genes using our current exome sequencing approach. Novel variants in the genes ARAP1, GLIS3, MADD, NOTCH2 and WFS1 need further investigation to reveal their possible role in diabetes. Conclusion: Our results demonstrate that exome sequencing can improve molecular diagnostics of MODY when used as a complement to Sanger sequencing. However, improvements will be needed, especially concerning coverage, before the full potential of exome sequencing can be realized

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p

Long-Range Autocorrelations of CpG Islands in the Human Genome

In this paper, we use a statistical estimator developed in astrophysics to study the distribution and organization of features of the human genome. Using the human reference sequence we quantify the global distribution of CpG islands (CGI) in each chromosome and demonstrate that the organization of the CGI across a chromosome is non-random, exhibits surprisingly long range correlations (10 Mb) and varies significantly among chromosomes. These correlations of CGI summarize functional properties of the genome that are not captured when considering variation in any particular separate (and local) feature. The demonstration of the proposed methods to quantify the organization of CGI in the human genome forms the basis of future studies. The most illuminating of these will assess the potential impact on phenotypic variation of inter-individual variation in the organization of the functional features of the genome within and among chromosomes, and among individuals for particular chromosomes

Epigenetic Epidemiology of Common Complex Disease: Prospects for Prediction, Prevention, and Treatment

As part of the PLoS Epigenetics Collection, Caroline Relton and George Davey Smith discuss the potential of epigenetics for the treatment and prevention of common complex diseases, including cancer