Sample-level enrichment analysis unravels shared stress phenotypes among multiple cancer types
1 Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona, Spain
2 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
3 Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain
Genome Medicine 2012, 4:28 doi:10.1186/gm327Published: 29 March 2012
Adaptation to stress signals in the tumor microenvironment is a crucial step towards carcinogenic phenotype. The adaptive alterations attained by cells to withstand different types of insults are collectively referred to as the stress phenotypes of cancers. In this manuscript we explore the interrelation of different stress phenotypes in multiple cancer types and ask if these phenotypes could be used to explain prognostic differences among tumor samples.
We propose a new approach based on enrichment analysis at the level of samples (sample-level enrichment analysis - SLEA) in expression profiling datasets. Without using a priori phenotypic information about samples, SLEA calculates an enrichment score per sample per gene set using z-test. This score is used to determine the relative importance of the corresponding pathway or module in different patient groups.
Our analysis shows that tumors significantly upregulating genes related to chromosome instability strongly correlate with worse prognosis in breast cancer. Moreover, in multiple tumor types, these tumors upregulate a senescence-bypass transcriptional program and exhibit similar stress phenotypes.
Using SLEA we are able to find relationships between stress phenotype pathways across multiple cancer types. Moreover we show that SLEA enables the identification of gene sets in correlation with clinical characteristics such as survival, as well as the identification of biological pathways/processes that underlie the pathology of different cancer subgroups.