Published online 19 October 2011 Nucleic Acids Research, 2012, Vol. 40, Database issue D947–D956 doi:10.1093/nar/gkr881 canSAR: an integrated cancer public translational research and drug discovery resource Mark D. Halling-Brown, Krishna C. Bulusu, Mishal Patel, Joe E. Tym and Bissan Al-Lazikani* Cancer Research UK Cancer Therapeutics Unit, Institute of Cancer Research, Haddow Laboratories, Belmont, Surrey, SM2 5NG, UK Received August 22, 2011; Revised September 26, 2011; Accepted September 30, 2011 ABSTRACT canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, ex- pression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify bio- logical annotation of a target, its structural charac- terization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the func- tionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data. INTRODUCTION Advances in large-scale genomics technologies have trans- formed the drug discovery ﬁeld by providing unparalleled biological and pharmacological information about poten- tial disease causing genes. Cancer research in particular beneﬁts from these advances as it is often caused by com- plex genetic events that may not become obvious without large-scale, systematic analysis of multi-genome heteroge- neous data (1,2). However, scientists can rarely make use of the full breadth and depth of relevant data that are often available. The difﬁculty of obtaining, effectively integrating and maintaining such large sets of heteroge- neous data such as genomics, cellular biology, medicinal chemistry and structural biology is the chief factor (3). Furthermore, the majority of public resources available tend to be domain speciﬁc resulting in researchers either making their decisions based on narrow or incomplete data contexts, or spending weeks at a time compiling data relating to their query from different sources. A growing number of resources exist that attempt to bring together such heterogeneous data [e.g. iHop (4) and GeneCards (5)]; however, most simply provide compilations of data through a web interface. While they can be a useful quick look-up, most of these resources do little or no actual in- tegration of the data; instead the meta-data are provided in large dumps through a web interface and require the user to trawl through long pages of listed information. Some resources are notable for better integration and us- ability such as cancerResource (6), that provides excellent query and integration of gene expression data linked to biological annotation of targets and cellular chemical screening data, with powerful expression data query tools; but by design, excludes other data sources that may well be relevant in broader cancer translational research. Moreover, chemical activity data and search capabilities are limited, as are query mechanisms. On the other hand, the ChEMBL database (7), which is a large curated and linked database of bioactive compounds, their biological targets and pharmacological data, across multidisciplines, has powerful chemical search and clustering tools and ex- tensive bioactivity data, but is not designed to be cancer speciﬁc and thus does not include extensive, biological data relevant to oncology such as cancer gene expression and ampliﬁcation data. What was required is a resource that integrates data at a very large scale, which captures cancer-relevant biological data such as expression, ampliﬁcation, RNAi etc, together with large protein–protein interaction data, chemical screening and pharmacological activities and 3D struc- tures. Furthermore, to maximize beneﬁts from integration of these vast heterogeneous data sets, the user needs to be presented with meta-data that is logically organized and linked in a way that mimics real life uses and workﬂows. *To whom correspondence should be addressed. Tel: +44 20 8722 4000; Fax: +44 20 8722 4126; Email: bissan.al-lazikani@icr.ac.uk Present address: Mark D. Halling-Brown, NCCPM, Medical Physics, St Lukes Wing, Royal Surrey County Hospital, Guildford, GU2 7XX. The Author(s) 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.