BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking.

Here, we present an update of Breast Cancer Now Tissue Bank bioinformatics, a rich platform for the sharing, mining, integration and analysis of breast cancer data. Its modalities provide researchers with access to a centralised information gateway from which they can access a network of bioinformatic resources to query findings from publicly available, in-house and experimental data generated using samples supplied from the Breast Cancer Now Tissue Bank. This in silico environment aims to help researchers use breast cancer data to their full potential, irrespective of any bioinformatics barriers. For this new release, a complete overhaul of the IT and bioinformatic infrastructure underlying the portal has been conducted and a host of novel analytical modules established. We developed and adopted an automated data selection and prioritisation system, expanded the data content and included tissue and cell line data generated from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia, designed a host of novel analytical modalities and enhanced the query building process. Furthermore, the results are presented in an interactive format, providing researchers with greater control over the information on which they want to focus. Breast Cancer Now Tissue Bank bioinformatics can be accessed at http://bioinformatics.breastcancertissuebank.org/.Breast Cancer Campaign [TB2016BIF]; Pancreatic Cancer Research Fund (PCRFTB) [Tissue Bank grant, to J.M and A.Z.D.U.]. Funding for open access charge: Breast Cancer Campaign [TB2016BIF]

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic -omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.Pancreatic Cancer Research Fund [Tissue Bank grant]; Cancer Research UK [Grant A12008]; Breast Cancer Campaign [Tissue Bank Bioinformatics grant TB2016BIF]

A global insight into a cancer transcriptional space using pancreatic data: importance, findings and flaws

Despite the increasing wealth of available data, the structure of cancer transcriptional space remains largely unknown. Analysis of this space would provide novel insights into the complexity of cancer, assess relative implications in complex biological processes and responses, evaluate the effectiveness of cancer models and help uncover vital facets of cancer biology not apparent from current small-scale studies. We conducted a comprehensive analysis of pancreatic cancer-expression space by integrating data from otherwise disparate studies. We found (i) a clear separation of profiles based on experimental type, with patient tissue samples, cell lines and xenograft models forming distinct groups; (ii) three subgroups within the normal samples adjacent to cancer showing disruptions to biofunctions previously linked to cancer; and (iii) that ectopic subcutaneous xenografts and cell line models do not effectively represent changes occurring in pancreatic cancer. All findings are available from our online resource for independent interrogation. Currently, the most comprehensive analysis of pancreatic cancer to date, our study primarily serves to highlight limitations inherent with a lack of raw data availability, insufficient clinical/histopathological information and ambiguous data processing. It stresses the importance of a global-systems approach to assess and maximise findings from expression profiling of malignant and non-malignant diseases

A HIF-LIMD1 negative feedback mechanism mitigates the pro-tumorigenic effects of hypoxia

The adaptive cellular response to low oxygen tensions is mediated by the hypoxia inducible factors (HIFs), a family of heterodimeric transcription factors composed of HIF-α and β subunits. Prolonged HIF expression is a key contributor to cellular transformation, tumourigenesis and metastasis. As such, HIF degradation under hypoxic conditions is an essential homeostatic and tumour suppressive mechanism. LIMD1 complexes with PHD2 and VHL in physiological oxygen levels (normoxia) to facilitate proteasomal degradation of the HIF-α subunit. Here, we identify LIMD1 as a HIF-1 target gene, which mediates a previously uncharacterised, negative regulatory feedback mechanism for hypoxic HIF-α degradation by modulating PHD2-LIMD1- VHL complex formation. Hypoxic induction of LIMD1 expression results in increased HIF-α protein degradation, inhibiting HIF-1 target-gene expression, tumour growth and vascularisation. Furthermore, we report that copy number variation at the LIMD1 locus occurs in 47.1% of lung adenocarcinoma patients, correlates with enhanced expression of a HIF target gene signature and is a negative prognostic indicator. Taken together, our data open a new field of research into the aetiology, diagnosis and prognosis of LIMD1-negative lung cancers

The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations

A Multidisciplinary Computational Approach to Model Cancer–omics Data: Organising, Integrating and Mining Multiple Sources of Data

PhDIt is imperative that the cancer research community has the means with which to effectively locate, access, manage, analyse and interpret the plethora of data values being generated by novel technologies. This thesis addresses this unmet requirement by using pancreatic cancer and breast cancer as prototype malignancies to develop a generic integrative transcriptomic model. The analytical workflow was initially applied to publicly available pancreatic cancer data from multiple experimental types. The transcriptomic landscape of comparative groups was examined both in isolation and relative to each other. The main observations included (i) a clear separation of profiles based on experimental type, (ii) identification of three subgroups within normal tissue samples resected adjacent to pancreatic cancer, each showing disruptions to biofunctions previously associated with pancreatic cancer (iii) and that cell lines and xenograft models are not representative of changes occurring during pancreatic tumourigenesis. Previous studies examined transcriptomic profiles across 306 biological and experimental samples, including breast cancer. The plethora of clinical and survival data readily available for breast cancer, compared to the paucity of publicly available pancreatic cancer data, allowed for expansion of the pipeline’s infrastructure to include functionalities for cross-platform and survival analysis. Application of this enhanced pipeline to multiple cohorts of triple negative and basal-like breast cancers identified differential risk groups within these breast cancer subtypes. All of the main experimental findings of this thesis are being integrated with the Pancreatic Expression Database and the Breast Cancer Campaign Tissue Bank bioinformatics portal, which enhances the sharing capacity of this information and ensures its exposure to a wider audienceEngineering and Physical Sciences Research Council and the Barts Cancer Institut