Rosalind J. Cutts

BioMart Central Portal: an open database network for the biological community

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities. Database URL: http://central.biomart.org.

Prognostic and Therapeutic Impact of Argininosuccinate Synthetase 1 Control in Bladder Cancer as Monitored Longitudinally by PET Imaging

Targeted therapies have yet to have significant impact on the survival of patients with bladder cancer. In this study, we focused on the urea cycle enzyme argininosuccinate synthetase 1 (ASS1) as a therapeutic target in bladder cancer, based on our discovery of the prognostic and functional import of ASS1 in this setting. ASS1 expression status in bladder tumors from 183 Caucasian and 295 Asian patients was analyzed, along with its hypothesized prognostic impact and association with clinicopathologic features, including tumor size and invasion. Furthermore, the genetics, biology, and therapeutic implications of ASS1 loss were investigated in urothelial cancer cells. We detected ASS1 negativity in 40% of bladder cancers, in which multivariate analysis indicated worse disease-specific and metastasis-free survival. ASS1 loss secondary to epigenetic silencing was accompanied by increased tumor cell proliferation and invasion, consistent with a tumor-suppressor role for ASS1. In developing a treatment approach, we identified a novel targeted antimetabolite strategy to exploit arginine deprivation with pegylated arginine deiminase (ADI-PEG20) as a therapeutic. ADI-PEG20 was synthetically lethal in ASS1-methylated bladder cells and its exposure was associated with a marked reduction in intracellular levels of thymidine, due to suppression of both uptake and de novo synthesis. We found that thymidine uptake correlated with thymidine kinase-1 protein levels and that thymidine levels were imageable with [(18)F]-fluoro-L-thymidine (FLT)-positron emission tomography (PET). In contrast, inhibition of de novo synthesis was linked to decreased expression of thymidylate synthase and dihydrofolate reductase. Notably, inhibition of de novo synthesis was associated with potentiation of ADI-PEG20 activity by the antifolate drug pemetrexed. Taken together, our findings argue that arginine deprivation combined with antifolates warrants clinical investigation in ASS1-negative urothelial and related cancers, using FLT-PET as an early surrogate marker of response.

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.

The pancreatic expression database: recent extensions and updates.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) is the only device currently available for mining of pancreatic cancer literature data. It brings together the largest collection of multidimensional pancreatic data from the literature including genomic, proteomic, microRNA, methylomic and transcriptomic profiles. PED allows the user to ask specific questions on the observed levels of deregulation among a broad range of specimen/experimental types including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Here we provide an update to PED, which has been previously featured in the Database issue of this journal. Briefly, PED data content has been substantially increased and expanded to cover methylomics studies. We introduced an extensive controlled vocabulary that records specific details on the samples and added data from large-scale meta-analysis studies. The web interface has been improved/redesigned with a quick search option to rapidly extract information about a gene/protein of interest and an upload option allowing users to add their own data to PED. We added a user guide and implemented integrated graphical tools to overlay and visualize retrieved information. Interoperability with biomart-compatible data sets was significantly improved to allow integrative queries with pancreatic cancer data.

Paclitaxel resistance increases oncolytic adenovirus efficacy via upregulated CAR expression and dysfunctional cell cycle control

Resistance to paclitaxel chemotherapy frequently develops in ovarian cancer. Oncolytic adenoviruses are a novel therapy for human malignancies that are being evaluated in early phase trials. However, there are no reliable predictive biomarkers for oncolytic adenovirus activity in ovarian cancer. We investigated the link between paclitaxel resistance and oncolytic adenovirus activity using established ovarian cancer cell line models, xenografts with de novo paclitaxel resistance and tumour samples from two separate trials. The activity of multiple Ad5 vectors, including dl922‐947 (E1A CR2‐deleted), dl1520 (E1B‐55K deleted) and Ad5 WT, was significantly increased in paclitaxel resistant ovarian cancer in vitro and in vivo. This was associated with greater infectivity resulting from increased expression of the primary receptor for Ad5, CAR (coxsackie adenovirus receptor). This, in turn, resulted from increased CAR transcription secondary to histone modification in resistant cells. There was increased CAR expression in intraperitoneal tumours with de novo paclitaxel resistance and in tumours from patients with clinical resistance to paclitaxel. Increased CAR expression did not cause paclitaxel resistance, but did increase inflammatory cytokine expression. Finally, we identified dysregulated cell cycle control as a second mechanism of increased adenovirus efficacy in paclitaxel‐resistant ovarian cancer. Ad11 and Ad35, both group B adenoviruses that utilise non‐CAR receptors to infect cells, are also significantly more effective in paclitaxel‐resistant ovarian cell models. Inhibition of CDK4/6 using PD‐0332991 was able both to reverse paclitaxel resistance and reduce adenovirus efficacy. Thus, paclitaxel resistance increases oncolytic adenovirus efficacy via at least two separate mechanisms – if validated further, this information could have future clinical utility to aid patient selection for clinical trials.

Using BioMart as a framework to manage and query pancreatic cancer data

We describe the Pancreatic Expression Database (PED), the first cancer database originally designed based on the BioMart infrastructure. The PED portal brings together multidimensional pancreatic cancer data from the literature including genomic, proteomic, miRNA and gene expression profiles. Based on the BioMart 0.7 framework, the database is easily integrated with other BioMart-compliant resources, such as Ensembl and Reactome, to give access to a wide range of annotations alongside detailed experimental conditions. This article is intended to give an overview of PED, describe its data content and work through examples of how to successfully mine and integrate pancreatic cancer data sets and other BioMart resources. Database URL: http://www.pancreasexpression.org

BCCTBbp: the Breast Cancer Campaign Tissue Bank bioinformatics portal

BCCTBbp (http://bioinformatics.breastcancertissue bank.org) was initially developed as the data-mining portal of the Breast Cancer Campaign Tissue Bank (BCCTB), a vital resource of breast cancer tissue for researchers to support and promote cutting-edge research. BCCTBbp is dedicated to maximising research on patient tissues by initially storing genomics, methylomics, transcriptomics, proteomics and microRNA data that has been mined from the literature and linking to pathways and mechanisms involved in breast cancer. Currently, the portal holds 146 datasets comprising over 227 795 expression/genomic measurements from various breast tissues (e.g. normal, malignant or benign lesions), cell lines and body fluids. BCCTBbp can be used to build on breast cancer knowledge and maximise the value of existing research. By recording a large number of annotations on samples and studies, and linking to other databases, such as NCBI, Ensembl and Reactome, a wide variety of different investigations can be carried out. Additionally, BCCTBbp has a dedicated analytical layer allowing researchers to further analyse stored datasets. A future important role for BCCTBbp is to make available all data generated on BCCTB tissues thus building a valuable resource of information on the tissues in BCCTB that will save repetition of experiments and expand scientific knowledge.

O-miner: an integrative platform for automated analysis and mining of -omics data

High-throughput profiling has generated massive amounts of data across basic, clinical and translational research fields. However, open source comprehensive web tools for analysing data obtained from different platforms and technologies are still lacking. To fill this gap and the unmet computational needs of ongoing research projects, we developed O-miner, a rapid, comprehensive, efficient web tool that covers all the steps required for the analysis of both transcriptomic and genomic data starting from raw image files through in-depth bioinformatics analysis and annotation to biological knowledge extraction. O-miner was developed from a biologist end-user perspective. Hence, it is as simple to use as possible within the confines of the complexity of the data being analysed. It provides a strong analytical suite able to overlay and harness large, complicated, raw and heterogeneous sets of profiles with biological/clinical data. Biologists can use O-miner to analyse and integrate different types of data and annotations to build knowledge of relevant altered mechanisms and pathways in order to identify and prioritize novel targets for further biological validation. Here we describe the analytical workflows currently available using O-miner and present examples of use. O-miner is freely available at www.o-miner.org.

Abstract 1431: Gene expression analysis of argininosuccinate synthetase loss and the effects of pegylated arginine deiminase in malignant pleural mesothelioma

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Malignant pleural mesothelioma (MPM) is a devastating asbestos-related malignancy that is increasing in many countries worldwide with few systemic treatment options beyond platinum and antifolate chemotherapy. Deficiency of the arginine biosynthetic enzyme argininosuccinate synthetase (ASS1) occurs in up to 50% of MPM cell lines and primary tumors and is being validated as a biomarker in patients treated with the arginine-depleting agent, pegylated arginine deiminase (ADI-PEG20). To understand the role of ASS1 loss and the effect of the ADI-PEG20 in MPM we used pathway analysis tools on microarray gene expression data from a representative panel of MPM cell lines. First, we identified that ASS1 loss was linked to several protumorigenic functions including increased cell invasiveness and migration, which were confirmed subsequently using invasion assays and organotypic modelling, respectively. We also detected a large number of enriched pathways connected to the immune response including communication between innate and adaptive immune cells and interferon signalling. Furthermore, ASS1 deficiency was linked to worse outcome with a median survival of 6 months in ASS1 low expressors compared to 12 months for ASS1 high expressors using a retrospective dataset (n=41; p=0.003). Second, ADI-PEG20 treatment modulated numerous pathways in ASS1-deficient cells including suppression of mTOR and folate metabolism, while promoting stress, oxidant and amino acid signalling. Third, bioinformatics analyses of drug interactions using Connectivity map revealed that arginine deprivation using ADI-PEG20 may potentiate several chemotherapeutic and targeted agents in the clinic. Taken together, our bioinformatics approach links loss of ASS1 in MPM cells to a more aggressive phenotype and identifies several potential combination strategies with ADI-PEG20 for further clinical investigation. Citation Format: Rosalind Cutts, Puthen V. Jithesh, Barbara Delage, Phuong Luong, Gareth Thomas, Claude Chelala, Peter W. Szlosarek. Gene expression analysis of argininosuccinate synthetase loss and the effects of pegylated arginine deiminase in malignant pleural mesothelioma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1431. doi:10.1158/1538-7445.AM2014-1431

An Integrated Systems Approach to the Study of Pancreatic Cancer

This chapter provides an overview of the different molecular technologies being exploited to elucidate the mechanisms underlying pancreatic cancer, which is key to the development of novel diagnostic and prognostic biomarkers as well as the implementation of effective therapies. We also describe approaches developed for integrated profiling analysis, discussing studies implementing a robust comparative analysis method for the study of primary data and specific data integration systems, such as the Pancreatic Expression Database, which have been designed to mine, integrate and visualise genes and pathways reported as being associated with pancreatic cancer. We discuss the International Cancer Genome Consortium project on pancreatic cancer, its data portal and underlying data mining technology. We also highlight some of the major issues that are a barrier to research efforts, in particular the lack of good clinical reporting in studies, the need for detailed controlled vocabularies and the importance of interoperability between research resources to maximise data mining and data sharing.