Meta-Analysis of Microarray Data Using a Pathway-Based Approach Identifies a 37-Gene Expression Signature for Systemic Lupus Erythematosus in Human Peripheral Blood Mononuclear Cells

A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE. Methods: In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set. Results: Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set. Conclusions: The novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature. Please see Research Highlight: http://genomemedicine.com/content/3/5/30Office of Women's Health, US Food and Drug Administration (OWH) 07-09-0001-SATexas Institute for Drug and Diagnostic Development (TI-3D

Employing “FDAlabel” Database to Extract Pharmacogenomics Information from FDA Drug Labeling to Advance the Study of Precision Medicine

Pharmacogenomics (PGx) focuses on how genomics and genetic variants (inherited and acquired) affect drug response. A better understanding of the association between genetic markers and individual phenotypes may improve therapy by enhancing drug efficacy, safety, and advance precision medicine. The FDALabel database (https://rm2.scinet.fda.gov/druglabel/#simsearch-0) was developed from the FDA\u27s Structured Product Labeling (SPL) repository to allow users to perform full-text and customizable searches of the labeling section {e.g. Boxed Warning, Warning and Precautions, Adverse Reaction (AR) sections}. In this study, 48 known biomarkers were used to query PGx relevant contents from the FDALabel database, including Indication, Clinical Pharmacology, Clinical Studies, and Use in Specific Populations. As a result, we identified 162 drugs out of 1129 small molecule drugs with PGx biomarker information. Furthermore, statistical analysis, pattern recognition, and network visualization were applied to investigate association of drug efficacy and severe ARs with PGx biomarkers and subpopulation. The results indicated that these drugs have a higher association with certain ARs in specific patient subpopulations (e.g., a higher association between CYP2D6 poor metabolizers and ARs caused by drugs for the treatment of psychiatric disoders ), and cover a broad range of therapeutic classes (e.g., Psychiatry, Cardiology, Oncology, and Endocrinology). FDALabel database (free publicly available) provides a convenient tool to navigate and extract PGx information from FDA-approved drug. The knowledge gained from these drugs and biomarkers in this study will enhance the understanding of PGx to advance precision medicine

The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

<p>Abstract</p> <p>Background</p> <p>Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.</p> <p>Results</p> <p>Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan – the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (<it>P</it>) derived from widely used simple <it>t</it>-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent <it>P</it>-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on <it>P</it>-value ranking is an expected mathematical consequence of the high variability of the <it>t</it>-values; the more stringent the <it>P</it>-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations.</p> <p>Conclusion</p> <p>We recommend the use of FC-ranking plus a non-stringent <it>P </it>cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the <it>P</it>-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and <it>P</it>-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the <it>P </it>criterion balances sensitivity and specificity.</p

Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells.

BackgroundA number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE.MethodsIn this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set.ResultsDifferentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set.ConclusionsThe novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature

Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells

Abstract Background A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE. Methods In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set. Results Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set. Conclusions The novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature. Please see Research Highlight: http://genomemedicine.com/content/3/5/30</p

Pharmacogenomics of HIV therapy: Summary of a workshop sponsored by the National Institute of Allergy and Infectious Diseases

Approximately 1 million people in the United States and over 30 million worldwide are living with human immunodeficiency virus type 1 (HIV-1). While mortality from untreated infection approaches 100%, survival improves markedly with use of contemporary antiretroviral therapies (ART). In the United States, 25 drugs are approved for treating HIV-1, and increasing numbers are available in resource-limited countries. Safe and effective ART is a cornerstone in the global least in part to human genetic variants that affect drug metabolism, drug disposition, and off-site drug targets. Defining effects of human genetic variants on HIV treatment toxicity, efficacy, and pharmacokinetics has far-reaching implications. In 2010, the National Institute of Allergy and Infectious Diseases sponsored a workshop entitled, Pharmacogenomics – A Path Towards Personalized HIV Care. This article summarizes workshop objectives, presentations, discussions, and recommendations derived from this meeting