Date Published: July 23, 2019
Publisher: Public Library of Science
Author(s): Richard Newton, Lorenz Wernisch, Enrique Hernandez-Lemus.
The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets. Here we investigate genes in the database which are unusual in that their copy number exhibits consistent heterogeneous disruption in a high proportion of the cancer datasets. We assess the potential relevance of these genes to the pathology of the cancer samples, in light of their predicted regulatory relationships and enriched biological pathways. A network-based method was used to identify enriched pathways from the genes’ inferred targets. The analysis predicts both known and new regulator-target interactions and pathway memberships. We examine examples in detail, in particular the gene POGZ, which is disrupted in many of the cancer datasets and has an unusually large number of predicted targets, from which the network analysis predicts membership of cancer related pathways. The results suggest close involvement in known cancer pathways of genes exhibiting consistent heterogeneous copy number disruption. Further experimental work would clarify their relevance to tumor biology. The results of the analysis presented in the database METAMATCHED, and included here as an R archive file, constitute a large number of predicted regulatory relationships and pathway memberships which we anticipate will be useful in informing such experiments.
Previously we have demonstrated that an analysis of matched array comparative genomics and transcriptomics human cancer datasets can reveal inter-chromosomal acting gene regulatory relationships [1–3]. By regulatory relationship we are refering to either a direct relationship, of a transcription factor on its target gene, or a very indirect one, through a pathway containing intermediate regulatory steps. We published the database METAMATCHED , comprising the results from such an analysis of a large number of publically available cancer datasets. Careful data randomisation ensures statistically significant predictions. Each dataset originated from samples of a particular type of cancer, and the datasets covered a wide range of cancer types.
In this section we first give a brief summary of the previously published meta-analysis method we use to predict gene regulatory relationships. We then describe the network-based pathway enrichment approach we use in this paper to analyse the results from the meta-analysis. Fig 1 is a flow chart illustrating all the steps involved in the analysis from array data through to enriched pathways.
In this section we first summarize the results of the meta-analysis. We then describe the primary results of the paper, namely the outcome of the network-based pathway enrichment anaysis. We first provide an overview of these results and then concentrate on genes exhibiting consistent heterogeneous copy number disruption, examining some of their enriched pathways in detail and their relevance to cancer biology.
Previously we have used matched aCGH/expression datasets to predict statistically significant gene regulatory relationships , validated the method experimentally  and computationally . In this context regulatory relationship refers to more than just direct casual relationships of transcription factors on targets, encompassing indirect casual relationships as well, through pathways containing intermediate regulatory steps.
We have added information from an exhaustive network-based pathway enrichment analysis to METAMATCHED, a database of statistically significant regulator-target predictions. In this paper we explore the relevance of these results to tumor biology. We have concentrated on genes exhibiting consistent heterogeneous copy number disruption and presented arguments why these genes could be of relevance to cancer pathways, which appear to be supported by the pathway enrichment results. The wealth of predicted regulatory relationships and pathway memberships contained in the Metamatched database provide pointers as to possible experiments that could clarify their role in cancer. We demonstrate how the predictions contained in the database can be useful in informing experiments and extending networks of regulatory relationships. We provide some interesting examples of this process, in particular for the gene POGZ.