Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining.

Dinesh Kumar Barupal,Brett S Phinney,Jan Budczies,Oliver Fiehn,Carsten Denkert,Bertrand Perroud,Bei Gao

doi:10.18632/oncotarget.26995

Abstract

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p <0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p <0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p <0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

Highlights

Estrogen receptor signaling is one of the main molecular features that determines the aggressiveness and the clinical course of breast cancer
The estrogenindependent growth of ERneg tumors depends on a range of biological pathways, including central energy and nucleotide metabolism [1, 2], motivating to characterize metabolic dysregulations associated with the aggressive www.oncotarget.com tumor phenotype
We used raw p-values to interpret the molecular differences across all multi-omics levels of cellular regulation using bioinformatics approaches

Summary

Introduction

Estrogen receptor signaling is one of the main molecular features that determines the aggressiveness and the clinical course of breast cancer. Human metabolic network’s operation and regulation is governed by up to 10% genes in the human genome Many of these genes and associated pathways are dysregulated and fuel a tumor’s growth, they are potential drug targets. A multi-omics approach has been successfully used to characterize metabolic dysregulation and identify potential new therapeutic targets in lung cancer [19], but such investigations are limited for breast tumors. These approaches yield different lists of potential targets, creating a challenge to identify which genes and pathways can be targeted in follow-up experiments. Poorly-studied significant genes and associated metabolic pathways provide an additional repertoire to identify new drug targets beyond the handful of genes

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