NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

Abstract

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

Figure 1.

Curation procedure and comparison between NCG 5.0 and NCG 4.0: (A) Flowchart of the curation procedure used in NCG. After the identification of relevant publications describing cancer mutational screenings, two independent curators extract cancer genes and related information on types of screening and cancer, primary sites, screened samples and supporting methods. (B) Number of publications, screenings, cancer types and screened samples in NCG 5.0 as compared to NCG 4.0. (C) Venn diagram of cancer genes in NCG 4.0 and NCG 5.0. The reasons for the removal of 778 genes from the database are detailed in Supplementary Table S2. (D–E) Growth of NCG data in time. Shown are the number of publications, screenings and cancer genes starting from 2010, year of the first release of NCG. All screenings that were published prior of 2010, were collapsed.

Figure 2.

Overview of data in NCG 5.0: (A) Cancer mutational screenings divided according to the method that was applied to identify cancer genes in the original publication. Methods and corresponding screenings are described in Supplementary Table S1. (B–C) Fractions of known and candidate cancer genes supported by one or more methods. Gene counts are reported in brackets. (D) Number of mutational screenings and primary sites where each cancer gene has been reported as a driver. TP53 is an outlier and has been excluded from the analysis because it has been identified in 113 screenings across 22 primary sites. (E) Heatmaps of the overlap between methods identifying known and candidate cancer genes. Each box represents the percentage of cancer genes identified with one method that are also supported by another. For each method, the total number of associated cancer genes is reported in brackets.

Figure 3.

Validation of candidate cancer genes and alteration spectrum of CSMD3: (A) Fractions of validated candidate cancer genes according to the used experimental assay. Gene silencing refers to stable knockout or transient knockdown via RNA interference. Other assays include in silico protein modelling, survival analysis, drug response, protein activity, rhotekin pull-down and xenograft cancer models. (B) Percentage of candidate cancer genes that have been validated using one or more experimental approaches. The corresponding number of genes is shown above each bar. The full list of experiments and genes is reported in Supplementary Table S3. (C) Protein domain architecture of CSMD3 according to the SMART database (32). (D) Percentage of mutational screenings, cancer types, primary sites and methods that support the cancer driver role of CSMD3. Corresponding numbers are provided. (E) Expression profile of CSMD3 in normal human tissues. Tissues where the gene is expressed in GTEx and Protein Atlas are highlighted in red.

Figure 4.

Examples of NCG usage: (A) Example of information available in NCG for a given cancer gene, in this case the oncogene AKT2. NCG summarizes the gene mutation profile across cancer types, information on duplicability, orthology, protein–protein and miRNA interactions and gene expression (B) NCG can facilitate the selection of the best cell systems for experimental assays by providing the expression profile of the gene of interest in several tissues and cell lines. (C) NCG can be used to annotate altered genes from mutational screenings. (D) The advanced search interface of NCG allows the identification of drivers in a variety of cancer types. (E) NCG can be integrated in gene enrichment analysis pipelines as a source of cancer genes.