Database Commons

a catalog of worldwide biological databases

Our knowledge of cancer genomics exploded in last several years, providing us with detailed knowledge of genetic alterations in almost all cancer types. Analysis of this data gave us new insights into molecular aspects of cancer, most important being the amazing diversity of molecular abnormalities in individual cancers. The most important question in cancer research today is how to classify this diversity to identify subtypes that are most relevant for treatment and outcome prediction for individual patients. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins they are found in and in relation to patients' clinical data. This approach allows users to find novel candidate driver regions for specific subgroups, that often cannot be found when similar analyses are done on the whole gene level and for large, diverse cohorts. Interactive interface allows user to visualize the distribution of mutations in subgroups defined by cancer type and stage, gender and age brackets, patient's ethnicity or vice versa find dominant cancer type, gender or age groups for specific three-dimensional mutation patterns.

The new era of cancer genomics is providing us with extensive knowledge of mutations and other alterations in cancer. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins in which they are found. The database also helps users analyze the distribution patterns of the mutations as well as their relationship to changes in drug activity through two algorithms: e-Driver and e-Drug. These algorithms use knowledge of modular structure of genes and proteins to separately study each region. This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level. The Cancer3D database provides access to the results of such analyses based on data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). In addition, it displays mutations from over 14,700 proteins mapped to more than 24,300 structures from PDB. This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.