canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates ...genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.
The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. ...Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.
Understanding the molecular control of cell lineages and fate determination in complex tissues is key to not only understanding the developmental biology and cellular homeostasis of such tissues but ...also for our understanding and interpretation of the molecular pathology of diseases such as cancer. The prerequisite for such an understanding is detailed knowledge of the cell types that make up such tissues, including their comprehensive molecular characterisation. In the mammary epithelium, the bulk of the tissue is composed of three cell lineages, namely the basal/myoepithelial, luminal epithelial estrogen receptor positive and luminal epithelial estrogen receptor negative cells. However, a detailed molecular characterisation of the transcriptomic differences between these three populations has not been carried out.
A whole transcriptome analysis of basal/myoepithelial cells, luminal estrogen receptor negative cells and luminal estrogen receptor positive cells isolated from the virgin mouse mammary epithelium identified 861, 326 and 488 genes as highly differentially expressed in the three cell types, respectively. Network analysis of the transcriptomic data identified a subpopulation of luminal estrogen receptor negative cells with a novel potential role as non-professional immune cells. Analysis of the data for potential paracrine interacting factors showed that the basal/myoepithelial cells, remarkably, expressed over twice as many ligands and cell surface receptors as the other two populations combined. A number of transcriptional regulators were also identified that were differentially expressed between the cell lineages. One of these, Sox6, was specifically expressed in luminal estrogen receptor negative cells and functional assays confirmed that it maintained mammary epithelial cells in a differentiated luminal cell lineage.
The mouse mammary epithelium is composed of three main cell types with distinct gene expression patterns. These suggest the existence of a novel functional cell type within the gland, that the basal/myoepithelial cells are key regulators of paracrine signalling and that there is a complex network of differentially expressed transcription factors controlling mammary epithelial cell fate. These data will form the basis for understanding not only cell fate determination and cellular homeostasis in the normal mammary epithelium but also the contribution of different mammary epithelial cell types to the etiology and molecular pathology of breast disease.
Integrating transcriptomic sequencing with conventional cytogenetics, we identified WWTR1 (WW domain-containing transcription regulator 1) (3q25) and CAMTA1 (calmodulin-binding transcription ...activator 1) (1p36) as the two genes involved in the t(1;3)(p36;q25) chromosomal translocation that is characteristic of epithelioid hemangioendothelioma (EHE), a vascular sarcoma. This WWTR1/CAMTA1 gene fusion is under the transcriptional control of the WWTR1 promoter and encodes a putative chimeric transcription factor that joins the amino terminus of WWTR1, a protein that is highly expressed in endothelial cells, in-frame to the carboxyl terminus of CAMTA1, a protein that is normally expressed only in brain. Thus, CAMTA1 expression is activated inappropriately through a promoter-switch mechanism. The gene fusion is present in virtually all EHEs tested but is absent from all other vascular neoplasms, demonstrating it to be a disease-defining genetic alteration. A sensitive and specific break-apart fluorescence in situ hybridization assay was also developed to detect the translocation and will assist in the evaluation of this diagnostically challenging neoplasm. The chimeric WWTR1/CAMTA1 transcription factor may represent a therapeutic target for EHE and offers the opportunity to shed light on the functions of two poorly characterized proteins.
Therapies that target estrogen signaling have made a very considerable contribution to reducing mortality from breast cancer. However, resistance to tamoxifen remains a major clinical problem. Here ...we have used a genome-wide functional profiling approach to identify multiple genes that confer resistance or sensitivity to tamoxifen. Combining whole-genome shRNA screening with massively parallel sequencing, we have profiled the impact of more than 56,670 RNA interference reagents targeting 16,487 genes on the cellular response to tamoxifen. This screen, along with subsequent validation experiments, identifies a compendium of genes whose silencing causes tamoxifen resistance (including BAP1, CLPP, GPRC5D, NAE1, NF1, NIPBL, NSD1, RAD21, RARG. SMC3. and UBA3) and also a set of genes whose silencing causes sensitivity to this endocrine agent (C10orf72, C15orf55/NUT, EDF1, INGS, KRAS, NOC3L, PPP1R15B, RRAS2. TMPRSS2, and TPM4). Multiple individual genes, including NF1, a regulator of RAS signaling, also correlate with clinical outcome after tamoxifen treatment.
Next generation sequencing has enabled systematic discovery of mutational spectra in cancer samples. Here, we used whole genome sequencing to characterize somatic mutations and structural variation ...in a primary acral melanoma and its lymph node metastasis. Our data show that the somatic mutational rates in this acral melanoma sample pair were more comparable to the rates reported in cancer genomes not associated with mutagenic exposure than in the genome of a melanoma cell line or the transcriptome of melanoma short-term cultures. Despite the perception that acral skin is sun-protected, the dominant mutational signature in these samples is compatible with damage due to ultraviolet light exposure. A nonsense mutation in ERCC5 discovered in both the primary and metastatic tumors could also have contributed to the mutational signature through accumulation of unrepaired dipyrimidine lesions. However, evidence of transcription-coupled repair was suggested by the lower mutational rate in the transcribed regions and expressed genes. The primary and the metastasis are highly similar at the level of global gene copy number alterations, loss of heterozygosity and single nucleotide variation (SNV). Furthermore, the majority of the SNVs in the primary tumor were propagated in the metastasis and one nonsynonymous coding SNV and one splice site mutation appeared to arise de novo in the metastatic lesion.
Given the steady increase in breast cancer rates in both the developed and developing world, there has been a concerted research effort undertaken worldwide to understand the molecular mechanisms ...underpinning the disease. The data generated from numerous clinical trials and experimental studies shed light on different aspects of the disease. We present a new version of the ROCK database (rock.icr.ac.uk), which integrates such diverse data types allowing unique analyses of published breast cancer experimental data. We have added several new data types and analysis modules to ROCK, which allow the user to interactively query and research the huge amounts of available experimental data and perform complex correlations across studies and data types such as gene expression, genomic copy number aberrations, micro RNA expression, RNA interference, survival analysis, clinical annotation and signalling protein networks. We present the recent and major functional updates and enhancements to the ROCK resource, including new analysis modules and microRNA and NGS data integration, and illustrate how ROCK can be used to confirm known experimental results as well as generate novel leads and new experimental hypotheses using the Wnt signalling cell surface receptor
FZD7
and the
Myc
oncogene. ROCK provides a unique breast cancer analysis platform of integrated experimental datasets at the genomic, transcriptomic and proteomic level. This paper presents how ROCK has transitioned from being simply a database to an interactive resource useful to the broader breast cancer research community in our effort to facilitate research into the underlying molecular mechanisms of breast cancer.
Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the ...structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.
RNA interference (RNAi) screening is a state-of-the-art technology that enables the dissection of biological processes and disease-related phenotypes. The commercial availability of genome-wide, ...short hairpin RNA (shRNA) libraries has fueled interest in this area but the generation and analysis of these complex data remain a challenge. Here, we describe complete experimental protocols and novel open source computational methodologies, shALIGN and shRNAseq, that allow RNAi screens to be rapidly deconvoluted using next generation sequencing. Our computational pipeline offers efficient screen analysis and the flexibility and scalability to quickly incorporate future developments in shRNA library technology.
To interrogate the complex mechanisms involved in the later stages of cancer metastasis, we designed a functional in vivo RNA interference (RNAi) screen combined with next-generation sequencing. ...Using this approach, we identified the sialyltransferase ST6GalNAc2 as a novel breast cancer metastasis suppressor. Mechanistically, ST6GalNAc2 silencing alters the profile of O-glycans on the tumor cell surface, facilitating binding of the soluble lectin galectin-3. This then enhances tumor cell retention and emboli formation at metastatic sites leading to increased metastatic burden, events that can be completely blocked by galectin-3 inhibition. Critically, elevated ST6GALNAC2, but not galectin-3, expression in estrogen receptor-negative breast cancers significantly correlates with reduced frequency of metastatic events and improved survival. These data demonstrate that the prometastatic role of galectin-3 is regulated by its ability to bind to the tumor cell surface and highlight the potential of monitoring ST6GalNAc2 expression to stratify patients with breast cancer for treatment with galectin-3 inhibitors.
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