Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

Background Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to a large number of metabolites in a cell and the presence of multiple genes associated with cancer development. Results Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets (MGPs). MGPs present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate MGPs. A GEM is a computational model that predicts reaction fluxes at a genome scale, and can be constructed in a cell-specific manner by using omics data (e.g., RNA-seq). The computational workflow is first validated by comparing the resulting metabolite-gene (MG) pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from 17 acute myeloid leukemia samples and 21 renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the MGPs predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting MGPs is also discussed. Conclusions Validation of the MGP-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting MGPs will help identify novel oncometabolites, and also suggest cancer treatment strategies.