Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments

Transcription factors TEAD2 and E2A globally repress acetyl-CoA synthesis to promote tumorigenesis.

Acetyl-coenzyme A (acetyl-CoA) plays an important role in metabolism, gene expression, signaling, and other cellular processes via transfer of its acetyl group to proteins and metabolites. However, the synthesis and usage of acetyl-CoA in disease states such as cancer are poorly characterized. Here, we investigated global acetyl-CoA synthesis and protein acetylation in a mouse model and patient samples of hepatocellular carcinoma (HCC). Unexpectedly, we found that acetyl-CoA levels are decreased in HCC due to transcriptional downregulation of all six acetyl-CoA biosynthesis pathways. This led to hypo-acetylation specifically of non-histone proteins, including many enzymes in metabolic pathways. Importantly, repression of acetyl-CoA synthesis promoted oncogenic dedifferentiation and proliferation. Mechanistically, acetyl-CoA synthesis was repressed by the transcription factors TEAD2 and E2A, previously unknown to control acetyl-CoA synthesis. Knockdown of TEAD2 and E2A restored acetyl-CoA levels and inhibited tumor growth. Our findings causally link transcriptional reprogramming of acetyl-CoA metabolism, dedifferentiation, and cancer

Elevated arginine levels in liver tumors promote metabolic reprogramming and tumor growth

Arginine auxotropy, due to reduced expression of urea cycle genes, is common in cancer. However, little is known about the levels of arginine in these cancers. Here, we report that arginine levels are elevated in hepatocellular carcinoma (HCC) despite reduced expression of urea cycle enzymes. Liver tumors accumulate high levels specifically of arginine via increased uptake and, more importantly, via suppression of arginine-to-polyamine conversion due to reduced arginase 1 (ARG1) and agmatinase (AGMAT) expression. Furthermore, the high levels of arginine are required for tumor growth. Mechanistically, high levels of arginine promote tumorigenesis via transcriptional regulation of metabolic genes, including upregulation of asparagine synthetase (ASNS). ASNS-derived asparagine further enhances arginine uptake, creating a positive feedback loop to sustain high arginine levels and oncogenic metabolism. Thus, arginine is a novel second messenger-like molecule that reprograms metabolism to promote tumor growth

USP29-mediated HIF1α stabilization is associated with Sorafenib resistance of hepatocellular carcinoma cells by upregulating glycolysis

Understanding the mechanisms underlying evasive resistance in cancer is an unmet medical need to improve the efficacy of current therapies. In hepatocellular carcinoma (HCC), aberrant expression of hypoxia-inducible factor 1 α (HIF1α) and increased aerobic glycolysis metabolism are drivers of resistance to therapy with the multi-kinase inhibitor Sorafenib. However, it has remained unknown how HIF1α is activated and how its activity and the subsequent induction of aerobic glycolysis promote Sorafenib resistance in HCC. Here, we report the ubiquitin-specific peptidase USP29 as a new regulator of HIF1α and of aerobic glycolysis during the development of Sorafenib resistance in HCC. In particular, we identified USP29 as a critical deubiquitylase (DUB) of HIF1α, which directly deubiquitylates and stabilizes HIF1α and, thus, promotes its transcriptional activity. Among the transcriptional targets of HIF1α is the gene encoding hexokinase 2 (HK2), a key enzyme of the glycolytic pathway. The absence of USP29, and thus of HIF1α transcriptional activity, reduces the levels of aerobic glycolysis and restores sensitivity to Sorafenib in Sorafenib-resistant HCC cells in vitro and in xenograft transplantation mouse models in vivo. Notably, the absence of USP29 and high HK2 expression levels correlate with the response of HCC patients to Sorafenib therapy. Together, the data demonstrate that, as a DUB of HIF1α, USP29 promotes Sorafenib resistance in HCC cells, in parts by upregulating glycolysis, thereby opening new avenues for therapeutically targeting Sorafenib-resistant HCC in patients

mTORC2 Promotes Tumorigenesis via Lipid Synthesis

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 devel- opment. In particular, mTORC2 stimulated sphingolipid (glucosylceramide) and glycerophospholipid (cardi- olipin) 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

Proteogenomic characterization of hepatocellular carcinoma

We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies. We identified pathways differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. These pathways are involved in the organization of cellular components, cell cycle control, signaling pathways, transcriptional and translational control and metabolism. Analyses of CNA-mRNA and mRNA-protein correlations identified candidate driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β- catenin pathway, transcriptional control, cholesterol biosynthesis and sphingolipid metabolism. The activity of targetable kinases aurora kinase A and CDKs was upregulated. We found that CTNNB1 mutations are associated with altered phosphorylation of proteins involved in actin filament organization, whereas TP53 mutations are associated with elevated CDK1/2/5 activity and altered phosphorylation of proteins involved in lipid and mRNA metabolism. Integrative clustering identified HCC subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our analysis provides insights into the molecular processes underlying HCCs

Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages.

Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated HCCs. Here we present an integrated proteogenomic analysis of HCCs across clinical stages and etiologies. Pathways related to cell cycle, transcriptional and translational control, signaling transduction, and metabolism are dysregulated and differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. We describe candidate copy number-driven driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β-catenin, AKT/mTOR and Notch pathways, cell cycle and DNA damage regulation. The targetable aurora kinase A and CDKs are upregulated. CTNNB1 and TP53 mutations are associated with altered protein phosphorylation related to actin filament organization and lipid metabolism, respectively. Integrative proteogenomic clusters show that HCC constitutes heterogeneous subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our study provides a comprehensive overview of the proteomic and phophoproteomic landscapes of HCCs, revealing the major pathways altered in the (phospho)proteome

LATS1 but not LATS2 represses autophagy by a kinase-independent scaffold function

Autophagy perturbation represents an emerging therapeutic strategy in cancer. Although LATS1 and LATS2 kinases, core components of the mammalian Hippo pathway, have been shown to exert tumor suppressive activities, here we report a pro-survival role of LATS1 but not LATS2 in hepatocellular carcinoma (HCC) cells. Specifically, LATS1 restricts lethal autophagy in HCC cells induced by sorafenib, the standard of care for advanced HCC patients. Notably, autophagy regulation by LATS1 is independent of its kinase activity. Instead, LATS1 stabilizes the autophagy core-machinery component Beclin-1 by promoting K27-linked ubiquitination at lysine residues K32 and K263 on Beclin-1. Consequently, ubiquitination of Beclin-1 negatively regulates autophagy by promoting inactive dimer formation of Beclin-1. Our study highlights a functional diversity between LATS1 and LATS2, and uncovers a scaffolding role of LATS1 in mediating a cross-talk between the Hippo signaling pathway and autophagy

Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks

Motivation: The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data. Results: We infer pathway topologies from gene knockdown data using Bayesian networks with probabilistic Boolean threshold functions. To deal with the problem of underdetermined network parameters, we employ a Bayesian learning approach, in which we can integrate arbitrary prior information on the network under consideration. Missing observations are integrated out. We compute the exact likelihood function for smaller networks, and use an approximation to evaluate the likelihood for larger networks. The posterior distribution is evaluated using mode hopping Markov chain Monte Carlo. Distributions over topologies and parameters can then be used to design additional experiments. We evaluate our approach on a small artificial dataset, and present inference results on RNAi data from the Jak/Stat pathway in a human hepatoma cell line. Availability: Software is available on request. Contact