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1.  In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases 
BMC Immunology  2014;15(1):61.
Background
Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System.
Results
This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more “Th17 T-cell subtype” genes in RA than the other AIDs, and more “Chemokine plus Receptors” genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS.
Conclusions
Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.
Electronic supplementary material
The online version of this article (doi:10.1186/s12865-014-0061-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s12865-014-0061-0
PMCID: PMC4266212  PMID: 25486901
2.  NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures 
Translational Oncology  2014;7(5):556-569.
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26–27, 2013, entitled “Correlating Imaging Phenotypes with Genomics Signatures Research” and “Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems.” The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.
doi:10.1016/j.tranon.2014.07.007
PMCID: PMC4225695  PMID: 25389451
3.  Correction: Recurrence of Early Stage Colon Cancer Predicted by Expression Pattern of Circulating microRNAs 
PLoS ONE  2014;9(1):10.1371/annotation/86f9cc5c-58b7-4c07-8742-c3036c13e73a.
doi:10.1371/annotation/86f9cc5c-58b7-4c07-8742-c3036c13e73a
PMCID: PMC3894228
4.  Recurrence of Early Stage Colon Cancer Predicted by Expression Pattern of Circulating microRNAs 
PLoS ONE  2014;9(1):e84686.
Systemic treatment of patients with early-stage cancers attempts to eradicate occult metastatic disease to prevent recurrence and increased morbidity. However, prediction of recurrence from an analysis of the primary tumor is limited because disseminated cancer cells only represent a small subset of the primary lesion. Here we analyze the expression of circulating microRNAs (miRs) in serum obtained pre-surgically from patients with early stage colorectal cancers. Groups of five patients with and without disease recurrence were used to identify an informative panel of circulating miRs using quantitative PCR of genome-wide miR expression as well as a set of published candidate miRs. A panel of six informative miRs (miR-15a, mir-103, miR-148a, miR-320a, miR-451, miR-596) was derived from this analysis and evaluated in a separate validation set of thirty patients. Hierarchical clustering of the expression levels of these six circulating miRs and Kaplan-Meier analysis showed that the risk of disease recurrence of early stage colon cancer can be predicted by this panel of miRs that are measurable in the circulation at the time of diagnosis (P = 0.0026; Hazard Ratio 5.4; 95% CI of 1.9 to 15).
doi:10.1371/journal.pone.0084686
PMCID: PMC3882238  PMID: 24400111
5.  Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse 
Frontiers in Genetics  2013;4:236.
The use and benefit of adjuvant chemotherapy to treat stage II colorectal cancer (CRC) patients is not well understood since the majority of these patients are cured by surgery alone. Identification of biological markers of relapse is a critical challenge to effectively target treatments to the ~20% of patients destined to relapse. We have integrated molecular profiling results of several “omics” data types to determine the most reliable prognostic biomarkers for relapse in CRC using data from 40 stage I and II CRC patients. We identified 31 multi-omics features that highly correlate with relapse. The data types were integrated using multi-step analytical approach with consecutive elimination of redundant molecular features. For each data type a systems biology analysis was performed to identify pathways biological processes and disease categories most affected in relapse. The biomarkers detected in tumors urine and blood of patients indicated a strong association with immune processes including aberrant regulation of T-cell and B-cell activation that could lead to overall differences in lymphocyte recruitment for tumor infiltration and markers indicating likelihood of future relapse. The immune response was the biologically most coherent signature that emerged from our analyses among several other biological processes and corroborates other studies showing a strong immune response in patients less likely to relapse.
doi:10.3389/fgene.2013.00236
PMCID: PMC3834519  PMID: 24312117
colorectal cancer; relapse; variant analysis; integrative analysis; multi-omics; exome sequencing; systems biology; immune response
6.  Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method 
Journal of medicinal chemistry  2012;55(15):6832-6848.
The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27,000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.
doi:10.1021/jm300576q
PMCID: PMC3419493  PMID: 22780961
7.  Pediatric Palliative Care in the Age of eHealth: Opportunities for Advances in HIT to Improve Patient-Centered Communication 
American journal of preventive medicine  2011;40(5 0 2):S208-S216.
Pediatric palliative care is an organized method for delivering effective, compassionate and timely care to children with cancer and their families, but it currently faces many challenges despite advances in technology and health care delivery. A key challenge involves unnecessary suffering from debilitating symptoms, such as pain, resulting from insufficient personalized treatment. Additionally, breakdowns in communication and a paucity of usable patient-centric information impede effective care. Recent advances in informatics for consumer health through eHealth initiatives have begun to be adopted in care coordination and communication, but overall remain under-utilized. Tremendous potentials exist in effective use of health information technology (HIT) to improve areas requiring personalized care such as pain management in pediatric oncology patients.
This article aims first to identify communication challenges and needs in pediatric palliative cancer care from the perspectives of the entire group of individuals around the pediatric oncology patient, and then to describe how adoption and adaptation of these technologies can improve patient-provider communication, behavioral support, pain assessment, and education through integration into existing work flows. The goal of this research is to promote the value of using HIT standards-based technology solutions and stimulate development of interoperable, standardized technologies and delivery of context-sensitive information through user-friendly portals to facilitate communication in an existing pediatric clinical care setting.
doi:10.1016/j.amepre.2011.01.013
PMCID: PMC3703627  PMID: 21521596
health information technology; palliative care; pain management; quality of care and care coordination; health information exchange
8.  Informatics and data quality at collaborative multicenter Breast and Colon Cancer Family Registries 
Quality control and harmonization of data is a vital and challenging undertaking for any successful data coordination center and a responsibility shared between the multiple sites that produce, integrate, and utilize the data. Here we describe a coordinated effort between scientists and data managers in the Cancer Family Registries to implement a data governance infrastructure consisting of both organizational and technical solutions. The technical solution uses a rule-based validation system that facilitates error detection and correction for data centers submitting data to a central informatics database. Validation rules comprise both standard checks on allowable values and a crosscheck of related database elements for logical and scientific consistency. Evaluation over a 2-year timeframe showed a significant decrease in the number of errors in the database and a concurrent increase in data consistency and accuracy.
doi:10.1136/amiajnl-2011-000546
PMCID: PMC3392863  PMID: 22323393
Breast cancer; colon cancer; cancer registry; coordination; data governance; bioinformatics; epidemiology; ESAC; informatics
9.  Platform for Personalized Oncology: Integrative analyses reveal novel molecular signatures associated with colorectal cancer relapse 
Approximately 80% of Stage II colon cancer patients are cured by appropriate surgery. However, 20% relapse, and virtually all of these people will die due to metastatic disease. Adjuvant chemotherapy has little or no impact on relapse or survival in Stage II colon cancer, and can only add toxicity without benefit for 80% of the target population that has been cured by surgery. Despite much effort, it is difficult to identify clinical or molecular determinants of outcome in Stage II colon cancer, defeating attempts to target treatments to the 20% of individuals who are destined to relapse. We hypothesized that a multidimensional molecular analysis will identify a combination of factors that serve as prognostic biomarkers in Stage II adenocarcinoma of the colon. The Georgetown informatics team generated and analyzed multi-omics profiling datasets in stage II CRC patients with or without relapse to identify molecular signatures in CRC that may serve both as prognostic markers of recurrence, and also allow for identification of the subgroup of patients who might benefit from adjuvant chemotherapy. The datasets were loaded to GDOC® (Georgetown Database of Cancer) for further mining and analysis. The G-DOC web portal (http://gdoc.georgetown.edu) includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major “omics” types: DNA, mRNA, microRNA, and metabolites. Through technology re-use, the G-DOC infrastructure will accelerate progress for a variety of ongoing programs in need of integrative multi-omics analysis, and advance our opportunities to practice effective personalized oncology in the near future.
PMCID: PMC3814476  PMID: 24303318
10.  In Silico Discovery of Mitosis Regulation Networks Associated with Early Distant Metastases in Estrogen Receptor Positive Breast Cancers 
Cancer Informatics  2013;12:31-51.
The aim of this study was to perform comparative analysis of multiple public datasets of gene expression in order to identify common genes as potential prognostic biomarkers. Additionally, the study sought to identify biological processes and pathways that are most significantly associated with early distant metastases (<5 years) in women with estrogen receptor-positive (ER+) breast tumors. Datasets from three published studies were selected for in silico analysis of gene expression profiles of ER+ breast cancer, using time to distant metastasis as the clinical endpoint. A subset of 44 differently expressed genes (DEGs) was found common to all three studies and characterized by mitotic checkpoint genes and pathways that regulate mitotic spindle and chromosome dynamics. DEG promoter regions were enriched with NFY binding sites. Analysis of miRNA target sites identified significant enrichment of miR-192, miR-193B, and miR-16-1 targets. Aberrant mitotic regulation could drive increased genomic instability leading to a progression towards an early onset metastatic phenotype. The relative importance of mitotic instability may reflect the clinical utility of mitotic poisons in metastatic breast cancer, including poisons such as the taxanes, epothilones, and vinca alkaloids.
doi:10.4137/CIN.S10329
PMCID: PMC3579429  PMID: 23470717
estrogen receptor alpha-positive; mitotic checkpoint signaling; mitotic regulation network; microRNA targets; early distant metastasis
12.  Performance of PREMM1,2,6, MMRpredict, and MMRpro in detecting Lynch syndrome among endometrial cancer cases 
Genetics in Medicine  2012;14(7):670-680.
Purpose
Lynch syndrome accounts for 2–5% of endometrial cancer cases. Lynch syndrome prediction models have not been evaluated among endometrial cancer cases.
Methods
Area under the receiver operating curve (AUC), sensitivity and specificity of PREMM1,2,6, MMRpredict, and MMRpro scores were assessed among 563 population-based and 129 clinic-based endometrial cancer cases.
Results
A total of 14 (3%) population-based and 80 (62%) clinic-based subjects had pathogenic mutations. PREMM1,2,6, MMRpredict, and MMRpro were able to distinguish mutation carriers from noncarriers (AUC of 0.77, 0.76, and 0.77, respectively), among population-based cases. All three models had lower discrimination for the clinic-based cohort, with AUCs of 0.67, 0.64, and 0.54, respectively. Using a 5% cutoff, sensitivity and specificity were as follows: PREMM1,2,6, 93% and 5% among population-based cases and 99% and 2% among clinic-based cases; MMRpredict, 71% and 64% for the population-based cohort and 91% and 0% for the clinic-based cohort; and MMRpro, 57% and 85% among population-based cases and 95% and 10% among clinic-based cases.
Conclusion
Currently available prediction models have limited clinical utility in determining which patients with endometrial cancer should undergo genetic testing for Lynch syndrome. Immunohistochemical analysis and microsatellite instability testing may be the best currently available tools to screen for Lynch syndrome in endometrial cancer patients.
doi:10.1038/gim.2012.18
PMCID: PMC3396560  PMID: 22402756
endometrial cancer; genetic screening; genetic testing; Lynch syndrome; prediction models
13.  DDN: a caBIG® analytical tool for differential network analysis 
Bioinformatics  2011;27(7):1036-1038.
Summary: Differential dependency network (DDN) is a caBIG® (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. Developed under caBIG® 's In Silico Research Centers of Excellence (ISRCE) Program, DDN enables differential network analysis and provides an alternative way for defining network biomarkers predictive of phenotypes. DDN also serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes. Besides the standalone Java application, we have also developed a Cytoscape plug-in, CytoDDN, to integrate network analysis and visualization seamlessly.
Availability: The Java and MATLAB source code can be downloaded at the authors' web site http://www.cbil.ece.vt.edu/software.htm
Contact: yuewang@vt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr052
PMCID: PMC3065688  PMID: 21296752
14.  PUGSVM: a caBIGTM analytical tool for multiclass gene selection and predictive classification 
Bioinformatics  2010;27(5):736-738.
Summary: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases.
Availability: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.
Contact: yuewang@vt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq721
PMCID: PMC3042183  PMID: 21186245
16.  G-DOC: A Systems Medicine Platform for Personalized Oncology1 
Neoplasia (New York, N.Y.)  2011;13(9):771-783.
Currently, cancer therapy remains limited by a “one-size-fits-all” approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual's underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality “omics” data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient's tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major “omics” types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.
PMCID: PMC3182270  PMID: 21969811
17.  Rembrandt: Helping Personalized Medicine Become a Reality Through Integrative Translational Research 
Molecular cancer research : MCR  2009;7(2):157-167.
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.
doi:10.1158/1541-7786.MCR-08-0435
PMCID: PMC2645472  PMID: 19208739
Rembrandt; personalized medicine; translational research; clinical genomics; data integration
18.  Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine 
Pharmacogenetics and Genomics  2013;24(2):81-93.
Supplemental Digital Content is available in the text.
Objectives
Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.
Methods
We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.
Results
Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.
Conclusion
Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.
doi:10.1097/FPC.0000000000000015
PMCID: PMC3888473  PMID: 24401833
DMET; gemcitabine; NCI-60; pancreatic cancer; probabilistic networks

Results 1-18 (18)