Correction: Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study.

[This corrects the article DOI: 10.1371/journal.pone.0153461.].

A case study for cloud based high throughput analysis of NGS data using the globus genomics system.

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the "Globus Genomics" system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.

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.

Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse.

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.

In silico discovery of mitosis regulation networks associated with early distant metastases in estrogen receptor positive breast cancers.

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.

G-DOC: a systems medicine platform for personalized oncology.

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.