Urothelial cancer proteomics provides both prognostic and functional information

Abstract

Traditionally, bladder cancer has been classified based on histology features. Recently, some works have proposed a molecular classification of invasive bladder tumors. To determine whether proteomics can define molecular subtypes of muscle invasive urothelial cancer (MIUC) and allow evaluating the status of biological processes and its clinical value. 58 MIUC patients who underwent curative surgical resection at our institution between 2006 and 2012 were included. Proteome was evaluated by high-throughput proteomics in routinely archive FFPE tumor tissue. New molecular subgroups were defined. Functional structure and individual proteins prognostic value were evaluated and correlated with clinicopathologic parameters. 1,453 proteins were quantified, leading to two MIUC molecular subgroups. A protein-based functional structure was defined, including several nodes with specific biological activity. The functional structure showed differences between subtypes in metabolism, focal adhesion, RNA and splicing nodes. Focal adhesion node has prognostic value in the whole population. A 6-protein prognostic signature, associated with higher risk of relapse (5 year DFS 70% versus 20%) was defined. Additionally, we identified two MIUC subtypes groups. Prognostic information provided by pathologic characteristics is not enough to understand MIUC behavior. Proteomics analysis may enhance our understanding of prognostic and classification. These findings can lead to improving diagnosis and treatment selection in these patients.

Computational metabolomics hints at the relevance of glutamine metabolism in breast cancer

“…IMPaLA is a tool that allows ontology analyses based on metabolic pathways instead of genes. Strikingly, this network had a functional structure, grouping the metabolites into metabolic pathways as it has been previously shown for gene and protein PGMs (de Velasco et al, 2017; Gámez-Pozo et al, 2015; Gámez-Pozo et al, 2017; Trilla-Fuertes et al, 2018). Five functional nodes were defined, each with a different overrepresented metabolic pathway (Fig 2).…”

“…The activity of each functional node was calculated as previously described and comparisons between ER+ and ER-were done (de Velasco et al, 2017; Gámez-Pozo et al, 2015; Gámez-Pozo et al, 2017; Trilla-Fuertes et al, 2018). Significant differences were found between ER+ and ER-tumors regarding lipid and purine metabolism (p<0.05) (S1 Fig).…”

“…On the other hand, IMPaLA assigned a dominant metabolic function to each resulting node. In previous studies, we demonstrated that PGMs are useful for functionally characterizing gene or protein networks (de Velasco et al, 2017; Gámez-Pozo et al, 2015; Gámez-Pozo et al, 2017). However, to our knowledge, this is the first time a PGM has been applied to metabolomics data from tumor samples.…”

“…As previously described (de Velasco et al, 2017; Gámez-Pozo et al, 2015; Gámez-Pozo et al, 2017; Trilla-Fuertes et al, 2018), PGMs compatible with high dimensional data were used, using correlation as associative criteria. PGMs were built using metabolomics, gene expression or flux activity data without any a priori information.…”

“…Once the functional structure was defined, functional node activities were calculated in order to make comparisons between groups, as previously described (de Velasco et al, 2017; Gámez-Pozo et al, 2015; Gámez-Pozo et al, 2017; Trilla-Fuertes et al, 2018). Briefly, each functional node activity was calculated as the mean of the expression/quantity of genes/metabolites of each node that are related to the main node function/metabolic pathway.…”