CanProVar 2.0: An Updated Database of Human Cancer Proteome Variation

Abstract

Identification and annotation of the mutations involved in oncogenesis and tumor progression are crucial for both cancer biology and clinical applications. Previously, we developed a public resource CanProVar, a human cancer proteome variation database for storing and querying single amino acid alterations in the human cancers. Since the publication of CanProVar, extensive cancer genomics efforts have revealed the enormous genomic complexity of various types of human cancers. Thus, there is an overwhelming need for comprehensive annotation of the genomic alterations at the protein level and making such knowledge easily accessible. Here, we describe CanProVar 2.0, a significantly expanded version of CanProVar, in which the amount of cancer-related variations and noncancer specific variations was increased by about 10-fold as compared to the previous version. To facilitate the interpretation of the variations, we added to the database functional data on potential impact of the cancer-related variations on 3D protein interaction and on the differential expression of the variant-bearing proteins between cancer and normal samples. The web interface allows for flexible queries based on gene or protein IDs, cancer types, chromosome locations, or pathways. An integrated protein sequence database containing variations that can be directly used for proteomics database searching can be downloaded.

Keywords: cancer; database; proteome; variation.

Figure 1. The system architecture of CanProVar 2.0

Five query methods are provided, and the output information is shown through different background colors.

Figure 2. Variation content

(A) Comparison of crVARs among different data sources between CanProVar version 1.0 and 2.0. (B) The frequency statistics for cancer types in CanProVar 2.0 show that much more crVARs have been identified in ovarian, breast, skin, and head and neck cancers than other cancer types.

Figure 3. Example of differential expression of a crPRO and its partners

Protein with differential expression, cancer-related variation, and its interaction partners are displayed in graph.

Figure 4. New searching methods

CanProVar 2.0 introduced three additional searching methods based on protein sets defined by protein lists (A), chromosome locations (B) and biological pathways (C).

Figure 5. Example results of a protein list-based query

(A) The amounts of crVARs in the query proteins in different cancer types are displayed in a heatmap, in which darker colors correspond to more variations. (B) The detailed information about these variations can be found when the user selects the number of variations in a specific cancer type.

Figure 6. Example of a chromosome location-based query

In the searching results, the accumulated mutation numbers (crVARs) are plotted by the chromosome position and the data cross cancer types are illustrated in different colors. Therefore, a hot chromosome band with a significantly higher number of crVARs can be spotted easily and clearly. For example, we found a peak of crVARs in chromosome band ‘chr17 p13.1’.

Figure 7. Example of results of a biological pathway-based query

By entering a KEGG pathway ID, e.g. has00010, or selecting a name from the menu of pathways implemented in CanProVar 2.0, the user can see the crPROs and crDEPs as highlighted in different colors on the graph of the given pathway. For instance, in the p53 signaling pathway, most of members have crVARs and half of these crPROs also showed differential expression between cancer and normal samples.

Figure 8. Significantly mutated proteins in cancers

(A) The crVARs: ncrVARs ratio analysis revealed 167 significantly mutated proteins across all cancer types, of which the ratios are higher than 3.0. (B) The crVARs: ncrVARs ratios in ovarian cancer. (C) The crVARs: ncrVARs ratios in breast cancer. (D) The crVARs: ncrVARs ratios in skin cancer.

Figure 9. Association network of cancers

This network was constructed based on shared crVARs. Two cancer types were linked if they had common crVARs. The network has 217 edges, and the edge width represents the number of shared crVARs between the two connected cancer types. The node size corresponds to the degree in the network.