Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied Biosystems™ Axiom™ array genotyping calls: Retrospective evaluation of UK Biobank array data.

The UK Biobank genotyped about 500k participants using Applied Biosystems Axiom microarrays. Participants were subsequently sequenced by the UK Biobank Exome Sequencing Consortium. Axiom genotyping was highly accurate in comparison to sequencing results, for almost 100,000 variants both directly genotyped on the UK Biobank Axiom array and via whole exome sequencing. However, in a study using the exome sequencing results of the first 50k individuals as reference (truth), it was observed that the positive predictive value (PPV) decreased along with the number of heterozygous array calls per variant. We developed a novel addition to the genotyping algorithm, Rare Heterozygous Adjusted (RHA), to significantly improve PPV in variants with minor allele frequency below 0.01%. The improvement in PPV was roughly equal when comparing to the exome sequencing of 50k individuals, or to the more recent ~200k individuals. Sensitivity was higher in the 200k data. The improved calling algorithm, along with enhanced quality control of array probesets, significantly improved the positive predictive value and the sensitivity of array data, making it suitable for the detection of ultra-rare variants.

A transcriptomic analysis of black cohosh: Actein alters cholesterol biosynthesis pathways and synergizes with simvastatin.

Previous studies indicate that the herb black cohosh (Actaea racemosa L.) and the triterpene glycoside actein inhibit the growth of human breast cancer cells and activate stress-associated responses. This study assessed the transcriptomic effects of black cohosh and actein on rat liver tissue, using Ingenuity and ToxFX analyses. Sprague-Dawley rats were treated with an extract of black cohosh enriched in triterpene glycosides (27%) for 24 h or actein for 6 and 24 h, at 35.7 mg/kg, and liver tissue collected for gene expression analysis. Ingenuity analysis indicates the top canonical pathways are, for black cohosh, RAR Activation, and, for actein, Superpathway of Cholesterol Biosynthesis, at 24 h. Actein alters the expression of cholesterol biosynthetic genes, but does not inhibit HMG-CoA reductase activity. Black cohosh and actein inhibited the growth of human breast and colon cancer cells and synergized with the statin simvastatin. Combinations of black cohosh with certain classes of statins could enhance their activity, as well as toxic, such as inflammatory liver, side effects. Transcriptomic analysis indicates black cohosh and actein warrant further study to prevent and treat cancer and lipid disorders. This study lays the basis for an approach to characterize the mode of action and toxicity of herbal medicines.

A transcriptomic analysis of turmeric: Curcumin represses the expression of cholesterol biosynthetic genes and synergizes with simvastatin.

The spice turmeric (Curcuma longa L.) has a long history of use as an anti-inflammatory agent. The active component curcumin induces a variety of diverse biological effects and forms a series of degradation and metabolic products in vivo. Our hypothesis is that the field of toxicogenomics provides tools that can be used to characterize the mode of action and toxicity of turmeric components and to predict turmeric-drug interactions. Male Sprague-Dawley rats were treated for 4 days with turmeric root containing about 3% curcumin (comparable to what people consume in the fresh or dried root) or a fraction of turmeric enriched for curcumin (∼74%) and liver tissue collected for gene expression analysis. Two doses of each agent were added to the diet, corresponding to 540 and 2700 mg/kg body weight/day of turmeric. The transcriptomic effects of turmeric on rat liver tissue were examined using 3 programs, ToxFx Analysis Suite, in the context of a large drug database, Ingenuity Pathway and NextBio analyses. ToxFx analysis indicates that turmeric containing about 3% or 74% curcumin represses the expression of cholesterol biosynthetic genes. The dose of 400 mg/kg b.w./day curcumin induced the Drug Signature associated with hepatic inflammatory infiltrate. Ingenuity analysis confirmed that all 4 turmeric treatments had a significant effect on cholesterol biosynthesis, specifically the Cholesterol biosynthesis superpathway and Cholesterol biosynthesis 1 and 2. Among the top 10 up or downregulated genes, all 4 treatments downregulated PDK4; while 3 treatments downregulated ANGPTL4 or FASN. These findings suggest curcumin may enhance the anticancer effects of certain classes of statins, which we confirmed with biological assays. Given this enhancement, lower levels of statins may be required, and even be desirable. Our findings also warn of possible safety issues, such as potential inflammatory liver effects, for patients who ingest a combination of certain classes of statins and curcumin. Transcriptomic analysis suggests that turmeric is worthwhile to study to prevent and treat cancer and lipid disorders. Our approach lays new groundwork for studies of the mode of action and safety of herbal medicines and can also be used to develop a methodology to standardize herbal medicines.

Clonal composition of human ovarian cancer based on copy number analysis reveals a reciprocal relation with oncogenic mutation status.

Intratumoral heterogeneity of cancer cells remains largely unexplored. Here we investigated the composition of ovarian cancer and its biological relevance. A whole-genome single nucleotide polymorphism array was applied to detect the clonal composition of 24 formalin-fixed, paraffin-embedded samples of human ovarian cancer. Genome-wide segmentation data consisting of the log2 ratio (log2R) and B allele frequency (BAF) were used to calculate an estimate of the clonal composition number (CC number) for each tumor. Somatic mutation profiles of cancer-related genes were also determined for the same 24 samples by next-generation sequencing. The CC number was estimated successfully for 23 of the 24 cancer samples. The mean ± SD value for the CC number was 1.7 ± 1.1 (range of 0-4). A somatic mutation in at least one gene was identified in 22 of the 24 ovarian cancer samples, with the mutations including those in the oncogenes KRAS (29.2%), PIK3CA (12.5%), BRAF (8.3%), FGFR2 (4.2%), and JAK2 (4.2%) as well as those in the tumor suppressor genes TP53 (54.2%), FBXW7 (8.3%), PTEN (4.2%), and RB1 (4.2%). Tumors with one or more oncogenic mutations had a significantly lower CC number than did those without such a mutation (1.0 ± 0.8 versus 2.3 ± 0.9, P = 0.0027), suggesting that cancers with driver oncogene mutations are less heterogeneous than those with other mutations. Our results thus reveal a reciprocal relation between oncogenic mutation status and clonal composition in ovarian cancer using the established method for the estimation of the CC number.

Clinical performance of the CytoScan Dx Assay in diagnosing developmental delay/intellectual disability.

PURPOSE: The prevalence of developmental disabilities in the United States is reported to be 13.87% across all racial, ethnic, and socioeconomic groups. Microarrays have been recommended as first-tier tests for these patients. This study reports the diagnostic yield and potential actionability of findings using a high-density chromosomal microarray (CMA). METHODS: The diagnostic yield of CytoScan Dx Assay in 960 patients was assessed with the Riggs criteria of actionability to evaluate predicted clinical utility. RESULTS: Eighty-six percent of the subjects were assessed using a microarray as part of historical routine patient care (RPC). The rate of pathogenic findings was similar between RPC (13.3%) and the CytoScan Dx Assay (13.8%). Among the 138 patients who did not receive microarray as RPC, the diagnostic yield for CytoScan Dx Assay was 23.9% as compared with 14.5%, indicating a 9.4% improvement when using higher-resolution methods. Thirty-five percent of patients with abnormal findings had predicted clinical management implications. CONCLUSIONS: This is the first study to assess the clinical performance of CytoScan Dx Assay. The assay's diagnostic yields are similar to those found in other studies of CMAs. Thirty-five percent of patients with abnormal findings are predicted to have clinical management implications that may improve health outcomes.

The liver pharmacological and xenobiotic gene response repertoire.

We have used a supervised classification approach to systematically mine a large microarray database derived from livers of compound-treated rats. Thirty-four distinct signatures (classifiers) for pharmacological and toxicological end points can be identified. Just 200 genes are sufficient to classify these end points. Signatures were enriched in xenobiotic and immune response genes and contain un-annotated genes, indicating that not all key genes in the liver xenobiotic responses have been characterized. Many signatures with equal classification capabilities but with no gene in common can be derived for the same phenotypic end point. The analysis of the union of all genes present in these signatures can reveal the underlying biology of that end point as illustrated here using liver fibrosis signatures. Our approach using the whole genome and a diverse set of compounds allows a comprehensive view of most pharmacological and toxicological questions and is applicable to other situations such as disease and development.

Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat.

The Critical Path Institute recently established the Predictive Safety Testing Consortium, a collaboration between several companies and the U.S. Food and Drug Administration, aimed at evaluating and qualifying biomarkers for a variety of toxicological endpoints. The Carcinogenicity Working Group of the Predictive Safety Testing Consortium has concentrated on sharing data to test the predictivity of two published hepatic gene expression signatures, including the signature by Fielden et al. (2007, Toxicol. Sci. 99, 90-100) for predicting nongenotoxic hepatocarcinogens, and the signature by Nie et al. (2006, Mol. Carcinog. 45, 914-933) for predicting nongenotoxic carcinogens. Although not a rigorous prospective validation exercise, the consortium approach created an opportunity to perform a meta-analysis to evaluate microarray data from short-term rat studies on over 150 compounds. Despite significant differences in study designs and microarray platforms between laboratories, the signatures proved to be relatively robust and more accurate than expected by chance. The accuracy of the Fielden et al. signature was between 63 and 69%, whereas the accuracy of the Nie et al. signature was between 55 and 64%. As expected, the predictivity was reduced relative to internal validation estimates reported under identical test conditions. Although the signatures were not deemed suitable for use in regulatory decision making, they were deemed worthwhile in the early assessment of drugs to aid decision making in drug development. These results have prompted additional efforts to rederive and evaluate a QPCR-based signature using these samples. When combined with a standardized test procedure and prospective interlaboratory validation, the accuracy and potential utility in preclinical applications can be ascertained.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

Large-scale integrated databases supporting drug discovery.

Over the past 15 years, genomics, combinatorial chemistry and high-throughput automation have transformed the setting for drug discovery, from an information-poor to a data-rich environment. The next challenge for informatics scientists is to convert the large amount of disparate data produced into useful, integrated information. Consolidation of the different types of information related to drug discovery requires a good working knowledge of database technology, the existence of accepted data standards for achieving uniformity and a complete understanding of the different data systems that are already available. Chemogenomic databases represent the first example of truly integrated systems that make 'omic' technologies directly relevant to small-molecule drug discovery. Researchers within drug discovery programs now have an opportunity to take advantage of new information domains, through the advance and adoption of integrated chemogenomic databases.