Dataset Information

Pan-cancer analysis of somatic mutations and epigenetic alterations in insulated neighbourhood boundaries.

ABSTRACT: Recent evidence shows that the disruption of constitutive insulated neighbourhoods might lead to oncogene dysregulation. We present here a systematic pan-cancer characterisation of the associations between constitutive boundaries and genome alterations in cancer. Specifically, we investigate the enrichment of somatic mutation, abnormal methylation, and copy number alteration events in the proximity of CTCF bindings overlapping with topological boundaries (junctions) in 26 cancer types. Focusing on CTCF motifs that are both in-boundary (overlapping with junctions) and active (overlapping with peaks of CTCF expression), we find a significant enrichment of somatic mutations in several cancer types. Furthermore, mutated junctions are significantly conserved across cancer types, and we also observe a positive selection of transversions rather than transitions in many cancer types. We also analyzed the mutational signature found on the different classes of CTCF motifs, finding some signatures (such as SBS26) to have a higher weight within in-boundary than off-bounday motifs. Regarding methylation, we find a significant number of over-methylated active in-boundary CTCF motifs in several cancer types; similarly to somatic-mutated junctions, they also have a significant conservation across cancer types. Finally, in several cancer types we observe that copy number alterations tend to overlap with active junctions more often than in matched normal samples. While several articles have recently reported a mutational enrichment at CTCF binding sites for specific cancer types, our analysis is pan-cancer and investigates abnormal methylation and copy number alterations in addition to somatic mutations. Our method is fully replicable and suggests several follow-up tumour-specific analyses.

SUBMITTER: Pinoli P

PROVIDER: S-EPMC6964824 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types.ResultsIn this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways.ConclusionsThis new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer .

Dataset Information

Pan-cancer analysis of somatic mutations and epigenetic alterations in insulated neighbourhood boundaries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets