Sample Level Enrichment Analysis (SLEA) unravels shared stress phenotypes among multiple cancer types

//Sample Level Enrichment Analysis (SLEA) unravels shared stress phenotypes among multiple cancer types

Sample Level Enrichment Analysis (SLEA) unravels shared stress phenotypes among multiple cancer types

We are happy to share with you the results of a new publication, which has been published today in Genome Medicine:

Gunes Gundem and Nuria Lopez-Bigas. Sample level enrichment analysis (SLEA) unravels shared stress phenotypes among multiple cancer types. Genome Medicine. 4:28 doi:10.1186/gm327

In this manuscript we introduce SLEA, which we have described earlier in this blog, and we use it to explore the interrelation of different stress phenotypes in multiple cancer types. We also ask if these phenotypes could be used to explain prognostic differences among tumor samples.

First we do SLEA using Gitools with the set of genes related to Chromosome Instability (CIN genes) in a breast cancer dataset (Ivishina et al., 2006). Next we use the result of SLEA to stratify the tumors, and find that tumors with upregulation of CIN genes have worse prognosis than the others (see figure in the left).

Heatmap of tumor samples as columns and gene modules related to stress response phenotypes as rows. Enrichment is shown with colors from blue (down-regulation) to red (up-regulation) while gray values indicate no significant deviation from the expected median value.

We then ask what is the relation between the expression of CIN genes and other genes related to stress phenotypes. For this we perform SLEA with gene sets related to several stress phenotypes and we find that the tumors with over-expression of CIN genes displayed a transcriptional program pointing to evasion of the senescence barrier and particular stress phenotypes, indicating strong interdependencies between these different pathways (see figure in the right). We corroborate this relationships in 11 different datasets of different cancer types. The results of the 11 datasets analyzed can be browsed in the supplementary web browser.

We also perform a robustness analysis of the SLEA method to find that SLEA results are robust for datasets that include more than 80 samples. Overall this article shows 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. To learn more about SLEA you can visit http://bg.upf.edu/slea.

 

By | 2012-03-30T10:45:40+00:00 March 30th, 2012|Categories: BG News|Tags: , , |0 Comments

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