canSAR: update to the cancer translational research and drug discovery knowledgebase

Abstract canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal ‘druggability’ assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack—an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.

[1]  Lincoln D Stein,et al.  The International Cancer Genome Consortium Data Portal , 2019, Nature Biotechnology.

[2]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Ian Collins,et al.  Objective, Quantitative, Data-Driven Assessment of Chemical Probes , 2017, bioRxiv.

[4]  C. Sander,et al.  Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets , 2018, Nature Genetics.

[5]  Bin Zhang,et al.  PhosphoSitePlus, 2014: mutations, PTMs and recalibrations , 2014, Nucleic Acids Res..

[6]  Johannes Goll,et al.  Protein interaction data curation: the International Molecular Exchange (IMEx) consortium , 2012, Nature Methods.

[7]  Federica Toffalini,et al.  Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data , 2010, Nucleic acids research.

[8]  Erik Bongcam-Rudloff,et al.  A DNA Sequence Directed Mutual Transcription Regulation of HSF1 and NFIX Involves Novel Heat Sensitive Protein Interactions , 2009, PloS one.

[9]  Michael K. Gilson,et al.  BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology , 2015, Nucleic Acids Res..

[10]  Alfonso Valencia,et al.  PDBe-KB: a community-driven resource for structural and functional annotations , 2019, Nucleic Acids Res..

[11]  Frances M. G. Pearl,et al.  Therapeutic opportunities within the DNA damage response , 2015, Nature Reviews Cancer.

[12]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[13]  K. Tomczak,et al.  The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge , 2015, Contemporary oncology.

[14]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[15]  Benjamin J. Polacco,et al.  A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing , 2020, Nature.

[16]  Bissan Al-Lazikani,et al.  canSAR: an integrated cancer public translational research and drug discovery resource , 2011, Nucleic Acids Res..

[17]  Phillip G. Montgomery,et al.  Defining a Cancer Dependency Map , 2017, Cell.

[18]  Barry R O'Keefe,et al.  The canSAR data hub for drug discovery. , 2016, The Lancet. Oncology.

[19]  The UniProt Consortium,et al.  UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..

[20]  Bonnie Berger,et al.  A Quantitative Chaperone Interaction Network Reveals the Architecture of Cellular Protein Homeostasis Pathways , 2014, Cell.

[21]  Albert A Antolin,et al.  canSAR: update to the cancer translational research and drug discovery knowledgebase , 2020, Nucleic Acids Res..

[22]  Albert A Antolin,et al.  Public resources for chemical probes: the journey so far and the road ahead. , 2019, Future medicinal chemistry.

[23]  Allison P. Heath,et al.  Toward a Shared Vision for Cancer Genomic Data. , 2016, The New England journal of medicine.

[24]  George Papadatos,et al.  The ChEMBL database in 2017 , 2016, Nucleic Acids Res..

[25]  Patrick Aloy,et al.  A reference map of the human binary protein interactome , 2020, Nature.

[26]  Julian Blagg,et al.  Choose and Use Your Chemical Probe Wisely to Explore Cancer Biology , 2017, Cancer cell.

[27]  Mishal N. Patel,et al.  Objective assessment of cancer genes for drug discovery , 2012, Nature Reviews Drug Discovery.

[28]  John P. Overington,et al.  The promise and peril of chemical probes. , 2015, Nature chemical biology.

[29]  B. Oakley,et al.  Faculty Opinions recommendation of A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. , 2020, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[30]  Jing Tang,et al.  DrugComb: an integrative cancer drug combination data portal , 2019, Nucleic Acids Res..

[31]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[32]  Hyojin Kim,et al.  TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions , 2017, Nucleic Acids Res..

[33]  B. Al-Lazikani,et al.  Drugging cancer genomes , 2013, Nature Reviews Drug Discovery.

[34]  James M. McFarland,et al.  Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells , 2017, bioRxiv.

[35]  Paul Workman,et al.  Distinctive Behaviors of Druggable Proteins in Cellular Networks , 2015, PLoS Comput. Biol..

[36]  Emanuel J. V. Gonçalves,et al.  Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens , 2019, Nature.

[37]  Maja Köhn,et al.  The human DEPhOsphorylation Database DEPOD: 2019 update , 2019, Database J. Biol. Databases Curation.