The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004–2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.

βII-spectrin (SPTBN1) is an adapter protein for Smad3/Smad4 complex formation during TGF-β signal transduction. Forty percent of SPTBN1+/− mice spontaneously develop hepatocellular carcinoma (HCC), and most cases of human HCC have significant reductions in SPTBN1 expression. In this study, we investigated the possible mechanisms by which loss of SPTBN1 may contribute to tumorigenesis. Livers of SPTBN1+/− mice, compared to wild type mouse livers, display a significant increase in EpCAM+ cells and overall EpCAM expression. Inhibition of SPTBN1 in human HCC cell lines increased the expression of stem cell markers EpCAM, Claudin7 and Oct4, as well as decreased E-cadherin expression and increased expression of vimentin and c-Myc, suggesting reversion of these cells to a less differentiated state. HCC cells with decreased SPTBN1 also demonstrate increased sphere formation, xenograft tumor development and invasion. Here, we investigate possible mechanisms by which SPTBN1 may influence the stem cell traits and aggressive behavior of HCC cell lines. We found that HCC cells with decreased SPTBN1 express much less of the Wnt inhibitor Kallistatin and exhibit decreased β-catenin phosphorylation and increased β-catenin nuclear localization, indicating Wnt signaling activation. Restoration of Kallistatin expression in these cells reversed the observed Wnt activation. Analysis of publicly available expression array datasets indicates that SPTBN1 expression in human HCC tissues is positively correlated with E-cadherin and Kallistatin levels, and decreased SPTBN1 and Kallistatin gene expression is associated with decreased relapse-free survival. Our data suggest that loss of SPTBN1 activates Wnt signaling, which promotes acquisition of stem cell-like features, and ultimately contributes to malignant tumor progression.

We thank the MDACC Cytogenetics and Cell Authentication Core for technical assistance in the cytogenetic analysis.

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.

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.

BackgroundG-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information.ResultsG-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data.G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability.ConclusionG-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1010-0) contains supplementary material, which is available to authorized users.

Although innate lymphoid cells (ILCs) play fundamental roles in mucosal barrier functionality and tissue homeostasis, ILC‐related mechanisms underlying intestinal barrier function, homeostatic regulation, and graft rejection in intestinal transplantation (ITx) patients have yet to be thoroughly defined. We found protective type 3 NKp44+ILCs (ILC3s) to be significantly diminished in newly transplanted allografts, compared to allografts at 6 months, whereas proinflammatory type 1 NKp44−ILCs (ILC1s) were higher. Moreover, serial immunomonitoring revealed that in healthy allografts, protective ILC3s repopulate by 2‐4 weeks postoperatively, but in rejecting allografts they remain diminished. Intracellular cytokine staining confirmed that NKp44+ILC3 produced protective interleukin‐22 (IL‐22), whereas ILC1s produced proinflammatory interferon‐gamma (IFN‐γ) and tumor necrosis factor‐alpha (TNF‐α). Our findings about the paucity of protective ILC3s immediately following transplant and their repopulation in healthy allografts during the first month following transplant were confirmed by RNA‐sequencing analyses of serial ITx biopsies. Overall, our findings show that ILCs may play a key role in regulating ITx graft homeostasis and could serve as sentinels for early recognition of allograft rejection and be targets for future therapies.

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance.