Patterns of Intra-Clustering Analysis Reveal Hidden Oncogenic Relations

The identification of genes that can initiate and progress carcinogenesis through mutations is a challenging issue due to the sparsity of mutations and the high mutational heterogeneity between tumors of the same cancer type. While current methods mostly rely on finding recurrently mutated genes, this work aims to recover infrequently mutated genes that can contribute to cancer, by exploiting clustering analysis. To that end, we constructed a Network Graph of 8303 patients and 198 genes from single-point-mutation data, retrieved from the The Cancer Genome Atlas (TCGA), to find patient-gene groups with the parallel use of two separate methodologies: (a) one based on Barber’s modularity index, and (b) one based on network dynamics. A systematic analysis was applied over 23 statistically meaningful groups of 2037 patients spanning 22 cancer types, derived from both methodologies, using the Fisher’s exact test followed by the Benjamini-Hochberg false discovery rate method. This procedure recovered 32 known significantly mutated genes and 4 putative driver genes that are not identified by standard driver-gene identification algorithms used in the TCGA consortium analysis. This study has also revealed 14 statistically significant patterns not reported in the current literature, resulting in a total of 18 hidden oncogenic relations. Notably, among the results, skin cutaneous melanoma is related to IL7R. However, current literature suggests that only IL7R overexpression is linked to skin cancers. To the best of our knowledge, this is the first reported pan-cancer intra-clustering analysis.