Dysregulated mammalian target of rapamycin (mTOR) promotes cancer, but underlying mechanisms are poorly understood. We describe an mTOR-driven mouse model that displays hepatosteatosis progressing to hepatocellular carcinoma (HCC). Longitudinal proteomic, lipidomics, and metabolomic analyses revealed that hepatic mTORC2 promotes de novo fatty acid and lipid synthesis, leading to steatosis and tumor development. In particular, mTORC2 stimulated sphingolipid (glucosylceramide) and glycerophospholipid (cardiolipin) synthesis. Inhibition of fatty acid or sphingolipid synthesis prevented tumor development, indicating a causal effect in tumorigenesis. Increased levels of cardiolipin were associated with tubular mitochondria and enhanced oxidative phosphorylation. Furthermore, increased lipogenesis correlated with elevated mTORC2 activity and HCC in human patients. Thus, mTORC2 promotes cancer via formation of lipids essential for growth and energy production.

SummaryHighly glycolytic cancer cells prevent intracellular acidification by excreting the glycolytic end-products lactate and H+ via the monocarboxylate transporters 1 (MCT1) and 4 (MCT4). We report that syrosingopine, an anti-hypertensive drug, is a dual MCT1 and MCT4 inhibitor (with 60-fold higher potency on MCT4) that prevents lactate and H+ efflux. Syrosingopine elicits synthetic lethality with metformin, an inhibitor of mitochondrial NADH dehydrogenase. NAD+, required for the ATP-generating steps of glycolysis, is regenerated from NADH by mitochondrial NADH dehydrogenase or lactate dehydrogenase. Syrosingopine treatment leads to high intracellular lactate levels and thereby end-product inhibition of lactate dehydrogenase. The loss of NAD+ regeneration capacity due to combined metformin and syrosingopine treatment results in glycolytic blockade, leading to ATP depletion and cell death. Accordingly, ATP levels can be partly restored by exogenously provided NAD+, the NAD precursor nicotinamide mononucleotide (NMN), or vitamin K2. Thus, pharmacological inhibition of MCT1 and MCT4 combined with metformin treatment is a potential cancer therapy.

Histidine phosphorylation, the so-called hidden phosphoproteome, is a poorly characterized post-translational modification of proteins. Here we describe a role of histidine phosphorylation in tumorigenesis. Proteomic analysis of 12 tumours from an mTOR-driven hepatocellular carcinoma mouse model revealed that NME1 and NME2, the only known mammalian histidine kinases, were upregulated. Conversely, expression of the putative histidine phosphatase LHPP was downregulated specifically in the tumours. We demonstrate that LHPP is indeed a protein histidine phosphatase. Consistent with these observations, global histidine phosphorylation was significantly upregulated in the liver tumours. Sustained, hepatic expression of LHPP in the hepatocellular carcinoma mouse model reduced tumour burden and prevented the loss of liver function. Finally, in patients with hepatocellular carcinoma, low expression of LHPP correlated with increased tumour severity and reduced overall survival. Thus, LHPP is a protein histidine phosphatase and tumour suppressor, suggesting that deregulated histidine phosphorylation is oncogenic.

MotivationSeveral molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.ResultsWe developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types.Availability and implementationNetICS is available at https://github.com/cbg-ethz/netics.Supplementary information Supplementary data are available at Bioinformatics online.

No abstract