Parent-child signals identify candidate cancer driver genes

Construction of combined network

The co-expression and physical Protein-Protein Interaction Network provided in Subchallenge 2 were combined. The networks were maintained as interaction lists, which proved to be more efficient than the memory demanding matrix manipulation. The network combination method employed was based on the system developed previously⁹. This network combination process considered the interaction existence and weighting in each of the individual networks before accepting it as part of the combined network. Linear regression was used in the calculation of the weighting in the combined network.

The individual networks are summarized in Table 1. These anonymized networks prevented any bias in the module identification process. Edges in the combined network were weighted and during the leaderboard stages this weight and its cutoff values were optimized to construct a final combined network with high-scoring edges. The clustering technique was employed on this combined network, while the signaling network was used to determine the parent-child relationships.

The 2-reach center cluster formation

The number of nodes accessible within two steps as a proportion of the total nodes in the network, is defined as the 2-reach of that node. This 2-reach value was calculated for each node in the combined network. By repeatedly selecting two genes with the highest 2-reach values from the set of genes not yet assigned to a cluster, the complete network was decomposed. These two genes were the center of two separate clusters and their immediate neighbors chosen as members. To prevent overlapping, clusters were removed from the network, resulting in further fragmentation; such fragments give rise to clusters themselves as they may be within a range of 3–100 members. The largest remaining fragment was decomposed by repeating the 2-reach cluster formation process.

DREAM Challenge scores

The DREAM Challenge assessed modules based on a collection of GWAS, which is superior to matching the predicted modules against well-known pathway databases or annotated information. The unique GWAS used in the assessment was not part of the construction of the networks provided. The Challenge used these GWAS and our modules as input to the PASCAL scoring tool⁸, which provided 104 p-values for each of our modules in the final submission. With these multiple p-values, for each of our modules, we were then required to perform a level of correction to control the False Discovery Rate (FDR) using the Benjamini-Hochberg procedure¹⁰, as some of the p-values less than 0.05 may have been by chance.

Cancer modules with parent-child drivers

Significant modules (SM) with at least one driver gene from the CGC list were classified as cancer modules (CM). These cancer modules were a subset of the set of significant disease modules as depicted in Figure 1A. The list of genes in these cancer modules (CMgenes), were a source of potential driver genes. Additionally, from the signaling network in Table 1, we identified a list of parent genes (Pgenes) to well-known driver genes as another source of potential driver genes. Figure 1B highlights the intersection of these two lists. The CMgenes were based on the network module identification and GWAS scoring, while the Pgenes were based on the parent-child relationships with well-known driver genes in the signaling network. The overlap of these two lists provided the list of candidate driver genes based on a consensus.

Figure 1. Overlapping modules and genes.

(A) The set of significant modules (SM) with well-known driver genes from the Cancer Genome Census list were identified as cancer modules (CM). (B) The intersection of the genes from the cancer modules CMgenes and the parents from the signaling network Pgenes highlight our first list of candidate driver genes.

The scripts and source code used to perform the analysis in this study are available as Extended data¹¹.

Networks

The network formed by combing the co-expression network and the protein-protein interaction (PPI) network consisted of 14,200 genes and 824,528 edges. The removal of low-confidence edges resulted in the loss of 1,583 genes, 199 of which were unique to the co-expression network, 1,201 of which were unique to the PPI network, and 183 of which were common to both. The overlap of these two DREAM Challenge networks and the combined network is illustrated in Figure 2A.

Figure 2. Venn diagram of overlapping genes.

(A) Genes from Combined Network and the individual networks. (B) Genes from the signaling network, Cancer Genome Census (CGC) and their parents. PPI, protein-protein interaction.

The signaling network included 5,254 nodes and 21,826 edges. Of the 616 CGC driver genes 470 were present in this network. These driver genes were children of 1,721 parents, with TP53 having 154 (the highest number of parents); 63 other driver genes were orphans. All of the orphan driver genes were themselves parents in this signaling network. Figure 2B shows the overlap of the signaling network, the CGC driver genes and their parents. We also see that 306 of the CGC driver genes are both parents and children of other driver genes.

Modules

Our 2-reach clustering algorithm produced 237 non-overlapping modules, ranging in size from 3 to 100, as shown in Figure 3A. Many of our modules were 50 genes in size, as this was the default module size in the cluster formation process. Our method also produced modules that were at the upper bound of 100 genes and the lower bound of 3 genes in size, a result of fragmentation of the network. The 237 modules extracted from the combined network included 4,682 genes, which was less than 33% of the total network, since only the largest fragment was further assessed for clusters.

Figure 3. Our 2-reach modules.

(A) All 237 modules. (B) 27 Significant modules with 13 cancer modules identified by solid circles.

On examination of the 104 multiple p-value scores provided by the DREAM Challenge for each of our modules, we found 27 significant modules existed, all shown in Figure 3B. The 13 solid circles are the CM, which contain known driver genes. It is difficult to get highly significant enrichment p-values for modules of a small size. Consider a module of size 4 that includes two significant genes. Even though 50% is certainly a clear enrichment, this could also occur by chance. In contrast, a module of size 100 that contains 50% significant genes would get an extremely small p-value. Of the 237 modules identified from our 2-reach method on the combine network, 27 were identified as significant at the 10% FDR, and 13 were cancer related as seen in Figure 3. There were different assessment stages during the challenge. Our best result was at the 2.5% FDR, where we were the fifth-best clustering method as identified in the Supplementary Figure 5 in the main paper¹². Figure 4 shows the details with the performance of every method from the teams listed on the x-axis as compared to the highest scoring method from subchallenge 1. The first three teams with Bayes factor K less than three indicated their methods were better than this reference method.

Figure 4. Team results.

The y-axis represents the number of significant (NS) modules identified in the final phase of subchallenge 2.

Cancer pathways and driver genes

The 35 driver genes in these 13 cancer modules contained 84 unique parents from the signaling network. In total, 27 of the 84 parents were also driver genes, which left 57 as candidate driver genes. Many of the driver genes were also parents, but module 20, module 55, module 109, and module 143 contained driver genes that were not parents, since the “#Drivers in module” column value was larger than the number of red genes provided in Table 2. In the case of module 55, with as many as nine known driver genes, only five were parents in the signaling network. Module 143 has a unique condition of containing the single CGC driver gene HLA-A, which was not a parent, and therefore does not appear in Table 2. The presence of this driver gene, qualified module 143 to be a cancer module, but only the parents of other known driver genes were considered as candidate drivers. Each of the cancer modules CM and the candidate driver genes they contain are listed in Table 2; candidate genes are listed in black.

Additional analysis of the cancer modules further prioritized our initial list; we assessed the specific parent child relationships in the subgraphs using the parent genes for each of the modules in the signaling network. On examination of the 13 subgraphs, only three (modules 15, 19 and 55) had direct parent-child relationships, where the parent was listed in the module and the child was a CGC well known driver gene. In the case of module 15, there were five driver genes FBXW7, THRAP3, ABL1, FANCD2 and YWHAE, with 13 parent genes. The five driver genes were themselves parent genes, so we were left with eight parents to consider as potential candidate driver genes as listed in Table 2. Of the five driver genes in this module, only ABL1 had a parent from this same module in the signaling network – PRKDC, as seen in Figure 5. Similarly, module 19 showed the RAF1 driver gene had three parents in the signaling network, CDK4, YWHAB, and GSK3B. Module 55 has nine driver genes, but only five were parents. RB1 was the only driver gene with the neighboring parent PPP1CB from the signaling network. This process prioritized five parents, which are underlined in Table 2. They are neighbors of existing drivers in the cancer modules, and are parents to the same driver gene in the signaling network. Of these five, CDK4 is already present in the CGC list of known driver genes. The other four genes PRKDC, YWHAB, GSK3B, and PPP1CB are shortlisted.

Figure 5. Subgraph of Module 15 in the signalling network, including all parents of the driver genes.

Five blue nodes are the known drivers. Only driver gene ABL1 is seen to have a parent which forms part of module 15.

Underlying data

Synapse: Disease Module Identification DREAM Challenge. Synapse ID syn11944943.

Extended data

Zenodo: Parent-child signals identify candidate cancer driver genes. http://doi.org/10.5281/zenodo.3740805¹¹.

This project contains the scripts and source code used to perform the analysis in this study.

Extended data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).