Intra-clustering analysis reveals tissue-specific mutational patterns

Abstract

The identification of tissue-specific mutational patterns associated with cancer development is challenging due to the low frequency of certain mutations and the high variability among tumors within the same cancer type. To address the inter-tumoral heterogeneity issue, our study aims to uncover infrequent mutational patterns by leveraging clustering analysis. To that end, a Network Graph of 8303 patients and 198 genes was constructed from single-point-mutation data that were retrieved from The Cancer Genome Atlas (TCGA). Patient-gene groups were retrieved with the parallel use of two separate methodologies based on the: (a) Barbers modularity index, and (b) network dynamics. An intra-clustering analysis was employed to explore the patterns within smaller patient subgroups, which involved the Fishers exact test, multiple correspondence analysis and DISCOVER. This analysis was applied over 24 statistically meaningful groups of 2619 patients spanning 21 cancer types and it recovered 42 mutational patterns that are not reported in the TCGA consortium analysis publications. Notably, our findings: (i) suggest that AMER1 mutations are a putative separative element between colon and rectal adenocarcinomas, (ii) highlight the significant presence of RAC1 in head and neck squamous cell carcinoma (iii) suggest that EP300 mutations in head and neck squamous cell carcinoma are irrelevant of the HPV status of the patients and (iv) show that mutational-based clusters can contain patients with contrasting genetic alterations. SignificanceO_LIThe submitted paper proposes a novel method to identify infrequent cancer-specific mutational patterns through network graphs and clustering analysis. C_LIO_LIThis analysis recovered: (a) 3 statistically significant cancer-gene relations that are not reported in the corresponding TCGA consortium analyses, (b) 28 known cancer-gene relations, (c) 39 significant patterns of co-occurrence or of mutual exclusivity that are not reported in current literature and (d) 14 reported significant mutational patterns. C_LI