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Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes

Gholamreza Bidkhoria,b,1, Rui Benfeitasa,1, Martina Klevstigc,d, Cheng Zhanga, Jens Nielsene, Mathias Uhlena, Jan Borenc,d, and Adil Mardinoglua,b,e,2

aScience for Life Laboratory, KTH Royal Institute of Technology, SE-17121 Stockholm, Sweden; bCentre for Host-Microbiome Interactions, Dental Institute, King’s College London, SE1 9RT London, United Kingdom; cDepartment of Molecular and Clinical , University of Gothenburg, SE-41345 Gothenburg, Sweden; dThe Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden; and eDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden

Edited by Sang Yup Lee, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, and approved November 1, 2018 (received for review April 27, 2018) Hepatocellular carcinoma (HCC) is one of the most frequent forms of of markers associated with recurrence and poor prognosis (13–15). , and effective treatment methods are limited due to Moreover, genome-scale metabolic models (GEMs), collections tumor heterogeneity. There is a great need for comprehensive of biochemical reactions, and associated and transporters approaches to stratify HCC patients, gain biological insights into have been successfully used to characterize the of subtypes, and ultimately identify effective therapeutic targets. We HCC, as well as identify drug targets for HCC patients (11, 16–18). stratified HCC patients and characterized each subtype using tran- For instance, HCC tumors have been stratified based on the uti- scriptomics data, genome-scale metabolic networks and network lization of acetate (11). Analysis of HCC metabolism has also led topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differ- to identification of potential anticancer metabolite analogs that ences in metabolic and signaling pathways reflecting at genomic, would not be toxic for noncancerous liver tissues (16). These ob- transcriptomic, and proteomic levels. These subtypes showed large servations indicated the vital need for integrating large-scale omics differences in clinical survival associated with altered kynurenine data and systems-level analyses. However, while these methods metabolism, WNT/β-catenin–associated metabolism, and PI3K/ implicitly consider metabolic network structure, they do not per- AKT/mTOR signaling. Integrative analyses indicated that the three mit stratifying tumors based on network heterogeneity itself, and subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, instead rely on identification of key /metabolites and tumor PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to cata- stratification based on their expression levels. In turn, topology- lyze the same reactions. Based on systems-level analysis, we iden- driven network analyses, including –protein interaction, tified 8 to 28 subtype-specific genes with pivotal roles in controlling signaling, and transcriptional regulatory and metabolic networks the metabolic network and predicted that these genes may be tar- (19–21), provide an alternative view for characterizing tumors. geted for development of treatment strategies for HCC subtypes by For instance, network analysis enabled the identification of es- performing in silico analysis. To validate our predictions, we per- sential genes from a lethality perspective, as well as those capable formed experiments using HepG2 cells under normoxic and hypoxic – conditions and observed opposite expression patterns between and indispensable for controlling networks (4, 22 25). However, genes expressed in high/moderate/low-survival tumor groups in re- sponse to , reflecting activated hypoxic behavior in patients Significance with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic Hepatocellular carcinoma (HCC) is a heterogeneous and deadly network-driven approach, which may also be applied to other can- form of liver cancer. Here, we stratified and characterized HCC cer types, and this stratification may have clinical implications to tumors by applying graph and control theory concepts to the drive the development of precision medicine. topology of genome-scale metabolic networks. We identified three HCC subtypes with distinct differences in metabolic and hepatocellular carcinoma | biological networks | personalized medicine | signaling pathways and clinical survival and validated our re- genome-scale metabolic models | systems biology sults by performing additional experiments. We further iden- tified HCC subtype-specific genes pivotal in controlling the epatocellular carcinoma (HCC) is a prevalent form of liver entire metabolism and discovered genes that can be targeted Hcancer and the third-leading cause of cancer-related world- for development of efficient treatment strategies for specific wide mortality with an increasing prevalence globally (1). Due to HCC subtypes. Our systems-level analyses provided a system- its large heterogeneity, a complete understanding of the molecular atic way for characterization of HCC subtypes and identifica- mechanisms underlying HCC onset and progression remains elu- tion of drug targets for effective treatment of HCC patients. sive. Comprehensive approaches capable of incorporating inter- Author contributions: G.B. and A.M. designed research; G.B., R.B., and M.K. performed tumor variability, while providing biological insights, are thus of research; G.B. and R.B. analyzed data; and G.B., R.B., M.K., C.Z., J.N., M.U., J.B., and A.M. great need for revealing the underlying molecular mechanisms of wrote the paper. HCC progression, characterization of HCC subtypes, and identi- The authors declare no conflict of interest. fying therapeutic targets for development of effective treatment This article is a PNAS Direct Submission. strategies for specific patient groups. Published under the PNAS license. Systems biology approaches have been employed to character- 1G.B. and R.B. contributed equally to this work. ize the tumors and study the altered biological processes (2–5). 2To whom correspondence should be addressed. Email: [email protected]. Characterizations of HCC using omics data including genomics, This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. transcriptomics, proteomics, and metabolomics are currently 1073/pnas.1807305115/-/DCSupplemental. available (6–12). This integrative analysis enabled the identification

www.pnas.org/cgi/doi/10.1073/pnas.1807305115 PNAS Latest Articles | 1of10 Downloaded by guest on September 30, 2021 topology-driven methods do not take into account biological similar to each other in comparison with HCC fGGNs at the functionality, one important strength in GEM analyses. network level. While molecular classifications and identification of sig- We then identified genes that are pivotal in controlling the full natures have undoubtedly provided important contributions for networks using a network controllability approach. For instance, revealing the underlying molecular mechanism involved in the based on control theory, one may define the minimum driver node occurrence of HCC (12, 26, 27), a function-driven yet compre- sets (MDSs) as those nodes that influence the dynamics of a di- hensive characterization of HCC was lacking. Here, we used an rected network as previously defined (22, 31). Based on this no- unsupervised and systematic method to take advantage of the tion, nodes (genes) may be classified as indispensable, neutral, and directed relationships in tumor metabolism. We integrated mul- dispensable, namely those whose removal from the network re- tiomics data with metabolic network-based analysis to introduce a spectively increase, do not change, or decrease the minimum whole network-driven stratification of HCC tumors. We observed number of MDSs. One prime example of their importance was consistent tumor stratification across different datasets consisting shown for indispensable , which are commonly targeted by of hundreds of HCC tumors. Through the utilization of the entire disease-causing (22). Importantly, in a network, a gene metabolic network for tumor stratification, our method relied on may be classified either as indispensable, neutral, or dispensable the network topology rather than the known signatures or mo- based on its role in affecting the MDS number. Here, we took lecular features. Importantly, though we have only considered the advantage of the curated, directed, and comprehensive features of metabolic network information, different HCC subtypes displayed GEMs to characterize the gene–gene relationship which captures substantial differences at the metabolic, signaling pathways as metabolic associations and their functionality. We identified MDSs well as clinical survival. Additionally, we identified HCC subtype- and node dispensability at the gene–gene level by using fGGNs, specific therapeutic targets that have important roles in controlling and further defined “controlling genes” as those with a pivotal the cancer network but not in noncancerous liver samples. Finally, importance in controlling network dynamics, that is, MDSs or in- we experimentally observed that expression of genes associated dispensable genes. The approach used here to systematically and with good and poor prognosis tumors shows opposite responses functionally characterize metabolism may also be extended to under hypoxic conditions. other and diseases. Results We found 224 and 313 genes in HCC and noncancerous fGGNs, respectively, which identified as MDS in >80% of the – Functional Gene Gene Networks for Characterizing Metabolic Heterogeneity networks. These genes are associated with transport reactions, in HCC. We retrieved The Cancer Genome Atlas (TCGA) metabolism, oxidative , transcriptomics and clinical data for 369 HCC individuals, along metabolism, and carnitine shuttle (Dataset S2). Among the MDS with 50 matched noncancer liver samples from the Genomic Data genes, 85 and 68 are exclusive to HCC and noncancerous Commons portal (12). We split this transcriptomics dataset into networks, respectively. two parts: a test set, consisting of 186 patients with detailed clinical Next, we identified 188 indispensable genes in ≥80% of the information for clinical and signature data analysis, and a valida- HCC fGGNs, whereas 248 indispensable genes in ≥80% of the tion set, consisting of 183 patients with detailed clinical informa- noncancerous networks. Our observations indicated that in- tion. We integrated the transcriptomics data in the test set with dispensable genes tend to be connected to a higher number of an HCC-specific genome-scale metabolic model (16) to generate genes (i.e., higher degree), indicative of high centrality in both patient-specific HCC and nontumor liver GEMs (HMR2; SI Ap- − HCC and noncancerous networks (Q < 10 7, Mann–Whitney U pendix, Materials and Methods). After excluding 10 nonfunctional test; Fig. 1C). In HCC, indispensable genes are connected to 32 GEMs (<6%), we constructed personalized directed “functional” gene–gene networks (fGGNs), a novel approach introduced here other genes (median degree), as opposed to neutral and dis- for revealing the importance of a metabolic gene in HCC (SI Ap- pensable genes, which respectively show median degrees of 17 pendix,Figs.S1andS2) inspired by a previous approach (28) (de- and 9, respectively. We also found qualitatively similar obser- tails are in SI Appendix, Materials and Methods). Throughout, fGGNs vations in noncancerous networks for genes that are indispens- present gene–gene–directed networks (thus detailing a clear signal able (median degree 34), neutral (median degree 22), and flow between genes), where genes (enzymes) are nodes connected dispensable (median degree 11). Our analysis suggested that by edges if a metabolite of a gene’s reaction serves as a indispensable genes tend to be involved in a higher number of for the reaction driven by the other gene, or if the two genes reactions (Dataset S2), thus displaying higher importance for are required for the reaction to occur (SI Appendix,Fig.S2). controlling network dynamics. However, dispensable and in- After validation of fGGNs against randomly generated net- dispensable genes may show similar degrees, indicating that not works (SI Appendix, Fig. S3A), we compared heterogeneity all highly connected genes (i.e., hubs) have network-controlling across patients by testing fGGN similarity within and between properties. Nevertheless, most indispensable genes are more HCC versus noncancer liver. We investigated genes based on central in both HCC and noncancerous networks. Among the their network centrality, given that central nodes tend to act as 412 and 429 controlling genes (MDSs and indispensable) in HCC hubs with higher biological importance (21, 24, 29, 30). For in- and noncancerous fGGNs, we identified 116 HCC-specific and stance, we evaluated (Dataset S1) their degree centrality (num- 133 noncancerous-specific controlling genes. Reaction-level ber of genes associated with every gene), betweenness centrality dispensability and controllability are detailed in Dataset S2. (number of gene-connecting shortest paths that pass through a We performed in silico gene essentiality analysis using per- gene), eccentricity centrality (maximum distance between a gene sonalized GEMs. Essentiality analysis of all 2,892 metabolic and other genes), and closeness centrality (length of the shortest genes in GEMs showed that >95% of HCC samples have not path between a gene and all other genes). Comparison of these grown when MDSs or indispensable genes are silenced, much scores within HCC and noncancer groups indicated that the higher than the observed fractions of silencing of other genes former group is substantially more heterogeneous than the latter, (<50%) (Fig. 1C). Based on the controllability and MDS clas- where the median node absolute deviation for each of the pa- sifications, we also observed clear separation of HCC and non- rameters tends to be larger in HCC compared with nontumor cancer fGGNs as indicated by principal component analysis (Fig. samples (Fig. 1 A and B). In turn, between-group comparison 1D), otherwise not achieved when solely considering gene ex- showed substantial differences between HCC and nontumor pression (SI Appendix, Fig. S3C). These observations showed samples at the network level (SI Appendix, Fig. S3B). Overall, all that despite the high heterogeneity expressed at the gene level in tested parameters showed that noncancer fGGNs are more HCC, network analyses identified distinct and important genes

2of10 | www.pnas.org/cgi/doi/10.1073/pnas.1807305115 Bidkhori et al. Downloaded by guest on September 30, 2021 that may be used to efficiently stratify HCC and noncancer samples based on network controllability. % of varying genes Betweenness A 30 (%) Network-Based Stratification Reveals Biological and Clinical Survival HCC 20 (%) noncancerous Differences in HCC. After identifying the general features of 10 (%) fGGNs in terms of gene centrality, dispensability, and controlla- Closeness Degree bility, we used these concepts for in-depth characterizations of HCC and HCC subtypes. We stratified and characterized the HCC subtypes using the complete metabolic network as described above. To do so, we introduced the utilization of fGGNs to stratify Eccentricity tumors based on data and techniques previously employed to stratify tumors based on somatic mutations (32). We B * * combined the personalized fGGNs into a single generic fGGN, 200 ** ** which is representative of the features of all 186 patients and Dispensable consists of 1,972 metabolic genes (SI Appendix, Materials and Indispensable Methods), and used this generic fGGN for stratification of HCC patients. Through integration of patient transcriptomics data with Neutral the generic fGGN and employment of network smoothing to

eergeD 100 * Q < 10-7 spread the influence of each expression profile on the neighbor- hood of the network, we generated expression profiles that reflect the fGGN structure. These expression profiles were subsequently stratified using nonnegative matrix factorization. We identified an optimum number of three HCC subtypes (SI 0 Appendix, Fig. S3D), each consisting of 85, 49, and 52 patients HCC Noncancerous with substantial gene expression, biological process, and clinical C * * survival differences (Fig. 2 and Dataset S3). These subtypes are 100 henceforth termed iHCC1, iHCC2, and iHCC3. We also per- ● ●●● ● ● ● ● ● ● formed tumor stratification using Recon3D (33) as a reference ● ● ● SYSTEMS BIOLOGY ● ● model rather than HMR2-derived (34, 35) stratification. We obtained relatively good agreement with the Recon 3D stratifi- Controlling cation: 81, 83, and 75% of samples are respectively categorized (MDS + Indispensable) SI 50 as iHCC1, iHCC2, and iHCC3 by both HMR2 and Recon3D ( Other Appendix, Fig. S4), even though Recon3D does not include ∼37% of the metabolic genes in HMR2. -100 % of samples with no growth * Q < 10 We performed differential expression analysis based on the RNA-sequencing data of HCC subtypes and identified 2,409 0 differentially expressed genes between iHCC2 versus iHCC3, HCC Noncancerous 2,318 genes between iHCC1 versus iHCC3, and 1,115 genes between iHCC1 versus iHCC2 (Q < 0.05; SI Appendix, Dataset D 20 S3). Cancer hallmark gene set enrichment analysis (36) high- HCC lighted significant differences in hallmarks of cancer (Q < 0.01; ● ●●●●● ● ● ● ●●●●●●●● 10 ● ●●●●●●● Fig. 2A). For instance, iHCC3 displayed up-regulation of bi-

●● ●● ●●● ●● ● ●●●● ● ● ●● ● ●●●●●●●●●●●● ● ●●●●●●●●● ological processes associated with targets, mTOR, , ● ●●

● ●● ● inflammatory response, mitosis, G2M checkpoint, and DNA ●● ● 0 ●●● ● ●● ●●● ● ●●●●●●● ● ● ● ● ●● ●●● PC2(11.2%) ● ● ● ● ●●●● ●●●●● repair compared with iHCC1/iHCC2. iHCC2 also showed acti- ●● ● ● ● ● ●●●●●●●●●● ●●●●●●●●●●●●● ● ● ● ● ● ● Noncancerous vation of the WNT/β-catenin pathway. We also found that bi- −10 ological processes associated with mitosis and the cycle are down-regulated in iHCC2 compared with iHCC1/iHCC3 and −10 010 inflammation is up-regulated in iHCC1/iHCC3. PC1 (40.3%) Among the differentially expressed genes between the low- and high-survival iHCC3 and iHCC1 groups, we identified sev- Fig. 1. Network-based approaches to identify driver genes involved in pro- eral prognostic markers (SI Appendix, Fig. S5 and Dataset S3). gression of HCC. (A) Radar plot of median node absolute deviation for be- For instance, compared with iHCC3, tumors from iHCC1 displayed tweenness, closeness, degree, and eccentricity indicates a larger variability in up-regulated expression of 64 favorable prognostic markers and the HCC vs. noncancer networks. (B) Relation of degree centrality and con- down-regulated expression of 45 unfavorable prognostic markers trollability in cancer and noncancerous samples. Groups of genes were classi- Q < SI Appendix fied based on their dispensability (indispensable, dispensable, neutral) when ( 0.05; , Fig. S5), among the 469 metabolic genes identified in each category in >80% of the fGGNs for HCC and noncancerous previously identified as prognostic markers in liver cancer (15). networks. Indispensable genes tend to be more central than neutral or dis- In turn, iHCC2 showed mixed up- and down-regulation of these pensable, in both HCC and noncancerous tissues (for all six comparisons, Q < prognostic markers. iHCC3 tumors additionally presented down- − 10 7,Mann–Whitney U test). For each group (indispensable vs. dispensable vs. regulated expression of 123 (out of 157) liver-specific genes (Q < neutral), we observed no statistical differences in degrees of HCC vs. non- 0.05; Dataset S3), up-regulation of genes associated with previously cancerous for the three tested comparisons (Q > 0.2). (C) Silencing of con- identified immune signatures (37), and metastasization such as > < trolling genes leads to lethality in 95%ofHCCs(vs. 50% for silencing of HIF1α,IL1,TNFα,NFκB, and TGFβ (Dataset S3). other genes). In noncancerous samples, silencing either kills all or none of the We also observed that survival differences of the three groups samples, where all controlling genes lead to lethality (48% for other genes). − Both comparisons show statistically significant differences (Q < 10 100,Mann– are consistent with expression of prognostic markers, where Whitney U test). (D) Principal component analysis of cancer and noncancer for iHCC1 presents the highest survival rate, followed by iHCC2, controllability of fGNNs. Ellipses indicate 95% confidence regions (one outlier and iHCC3 (log- P < 0.001; Fig. 2B). Though differences are is identified at this confidence level). observed between the three groups, iHCC3 tumors are markedly

Bidkhori et al. PNAS Latest Articles | 3of10 Downloaded by guest on September 30, 2021 transition, and inflammation compared with iHCC1 or iHCC2 A Angiogenesis UP NOTCH signaling (Dataset S3). Apical junction We also performed gene set enrichment analysis using Piano (36) Inflammatory response iHCC2 Mitotic spindle formation and biological processes terms retrieved from the Molecular Sig- EMT natures Database (MSigDB) to reveal iHCC subtype-specific re- G2M checkpoint E2F targets sponses (Dataset S4). For instance, iHCC1 displayed up-regulated Xenobiotic metabolism DN and indole metabolism and down-regulated noncoding metabolism RNA metabolism and ribosome biogenesis (Q < 0.05) compared Fatty acid metabolism with tumors of iHCC2 and iHCC3. Tumors of iHCC2 displayed Adipogenesis (Q < 0.05) up-regulated heme metabolism, glutamine metabolism, metabolism drug metabolism and transport, and oxidative demethylation, but Oxidative phos. UP Androgen response down-regulated cell development and G protein-coupled recep- WNT β catenin signaling tor signaling, compared with iHCC3 and iHCC1. Tumors of ROS pathway ROS pathway Heme metabolism iHCC3 displayed the largest changes in biological processes com- Bile acid metabolism pared with iHCC1 or iHCC2, with up-regulation of multiple pro- Xenobiotic metabolism Myc targets cesses associated with cell proliferation, cell-cycle progression Androgen response iHCC3 and mitosis, development, segregation, mTOR signaling organization, immune response, DNA replication, and recombina- EMT DN E2F targets tion (Q < 0.05). In turn, iHCC3 displayed down-regulated fatty acid G2M checkpoint WNT β catenin signaling UP β-oxidation, lipid oxidation, small-molecule and ca- Notch signaling mTOR signaling Mitotic spindle formation EMT tabolism, metabolism of several amino acids including , UV response Inflammatory response glutamate, , , and aspartate, drug , and Myogenesis Apical junction Hypoxia Mitotic spindle formation response to xenobiotic stimulus (Dataset S4). Consistent with the Angiogenesis TNFα signaling via NFkB substantial differences between iHCC3 and the two other tumor G2M checkpoint E2F targets groups, iHCC2 tumors displayed similar metabolic behavior to those MYC targets of iHCC1 (Dataset S5), and their gene expression is more similar to IL6 JAK STAT3 signaling those of iHCC1 than to those of iHCC3 (Fig. 2B; mean Spearman’s Xenobiotic metabolism DN ρ ∼ 0.9 iHCC1 vs. iHCC2, <0.8 iHCC3 vs. iHCC1 or iHCC2). Bile acid metabolism Comparison of personalized fGGNs in each subtype further sup- Adipogenesis ported the results of our analysis (SI Appendix,Fig.S6). Peroxisome Oxidative phos. Importantly, our stratification method highlighted several iHCC1 Heme metabolism “stratifying” genes whose expression is substantially different Androgen response between the three iHCC groups. This is the case of XDH, KMO, Cholesterol homeostasis Q < 0.05 TDO2, and SC5D in iHCC1; GLUL, AQP9, RHBG, SLC1A2, B C SLC13A3, ACSS3, AOX1, and CYP3A4 in iHCC2; and PKM, 1.0 1.0 ● A ● ● ● G6PD, PGD, ENO1, SRM, and ALDOA in iHCC3 (Fig. 3 and ● ●●● ●●● ●● ●●● ● ●●● ●● ● ● ●● ● ●● ●●● ● ●● ●●●●●● ● ● ●●● ●●●●●●● ●●●● B SI Appendix ● ●● ● ●● ●● ●●●● and , Fig. S7). Other genes such as MTHFD1, 0.8 ● ●●●● ●●●●●●● ● ● ●● ●●●●●●●●●● ● ●● ●●●●● ●● ●●●●●● ● ● ●●●● ● ●●●●● ALDH6A1, and ACSM2B are similar in both iHCC1 and iHCC2 iHCC1 ●●●●●●● ● ● ● ● ● ● ● ● ● ● 0.6 ● but differ significantly in comparison with iHCC3. 0.8 ● iHCC2 ● ● iHCC3 0.4 ● The Association Between Metabolic, Wnt/β-Catenin, and PI3K/AKT/ ρ vs mean of iHCC3 Survival rate iHCC1 mTOR Signaling Pathways. The above results indicated that the iHCC2 0.2 0.6 iHCC3 iHCC subgroups present specific features at the survival re- P<0.05 Q < 0.01 currence signature, gene expression, prognostic marker, and

0.0 Spearman metabolic level identified solely based on network analysis. 012345 0.6 0.8 1.0 Years Spearman ρ vs mean of iHCC1 These tumors are also differentially associated with known HCC properties such as HIPPO signature, hypermethylation, DNA Fig. 2. Network-based approaches identified three different HCC subtypes. copy number, cholangiocarcinoma-like traits (6), RS65 gene- < (A) Gene set-enriched biological processes (Q 0.05) in different HCC subtypes based risk scores (38), and HB16 signature (7) (Fig. 3A and including iHCC1, iHCC2, and iHCC3. Arrows indicate direction of change (e.g., – Dataset S3). For instance, 84% of iHCC2 subjects are men (vs. iHCC2 shows up-regulated heme metabolism compared with iHCC1). (B)Kaplan ∼ Meier survival analysis shows significant differences in patient survival between 50% in other iHCCs), and about half of the patients in the three HCC subtypes (iHCC1 > iHCC2 > iHCC3). (C) Correlation plot between iHCC2 and iHCC3 displayed alcoholic liver disease, much higher tumorsandmeangeneexpressioniniHCC1andiHCC3(Q < 0.01) showed that than the <25% observed in iHCC1 (Q < 0.01). Additionally, iHCC2 tumors tend to be more similar to iHCC1 than iHCC3. This is reinforced by iHCC2 tumors also showed lower genome doubling, higher − the higher Euclidean distance between iHCC3 fGGNs and other fGGNs in this or hypermethylation, and CDKN2 silencing (Fig. 3A; Q < 10 4), the two other subtypes, compared with distances within or between iHCC1 and and all iHCC2 tumors showed α-fetoprotein (AFP) <300 ng/mL. − iHCC2 (SI Appendix,Fig.S6). EMT, epithelial-to-mesenchymal transition; ROS, iHCC1 and iHCC2 tumors are associated (Q < 10 4, χ2 test) with reactive oxygen species. markers of differentiation (>54% tumors display Hoshida 3) (8) and maturity (>79% HB16 C1). In turn, no distinct from those of iHCC2 and iHCC1. We found that a larger iHCC3 tumors showed differentiation markers (0% Hoshida 3) number of genes are differentially expressed between iHCC3 and and instead are associated with known markers of low survival (Q < 0.05, χ2 test; Fig. 3A and Dataset S3) including National iHCC1/iHCC2 compared with iHCC1 versus iHCC2, and several Cancer Institute proliferation (NCIP) subtype score A (>96%), cancer hallmarks are simultaneously enriched in iHCC3 in com- high recurrence risk Seoul National University recurrence A parison with either iHCC1 or iHCC2 (Fig. 2 ). For instance, (SNUR) subtype (>76%) (14), and high expression of recurrence iHCC3 tumors presented down-regulation of oxidative phosphory- risk marker CD24 (log fold change ∼2.55 for comparison vs. lation, fatty acid metabolism, and adipogenesis, and up-regulation iHCC1, Q < 0.00085). The lower survival and predominance of of DNA repair, G2M checkpoint, epithelial-to-mesenchymal aggressive tumors in iHCC3 are associated with the significantly

4of10 | www.pnas.org/cgi/doi/10.1073/pnas.1807305115 Bidkhori et al. Downloaded by guest on September 30, 2021 A iHCC1 iHCC2 iHCC3 iHCC subtype Alcoholic liver disease Body weight Grade AFP>300 RPPA clusters mRNA clusters CC like Alcoholic Liver Body weight HIPPO Disease Normal Hoshida Yes RS65 Obese HB16 No Overweight SNUR NCIP Genome doublings CTNNB1 Grade AFP>300 CDKN2A silencing G1 No TP53 mutation Hypermethylation G2 Yes IMPDH1 PKM G3 TRPM2 G6PD RPPA clusters mRNA clusters PGD ENO1 2 5 SRM 1 TYRO3 3 PDE9A DGUOK MTHFD1L 1 ALDOA SLC38A1 CCL HIPPO HSD11B1 CCL AH SLC1A2 HCC SOH AQP9 GLUL RHBG Hoshida RS65 SLC13A3 CYP3A4 3 High SLCO1B3 2 Low ACSS3 AOX1 1 ALDH5A1 ALDH2 HB16 SNUR SORD C1 High CYP2C9 C2 NAGS Low CES5A SLC13A5 NCIP Genome Doublings SLC6A12 A 2

XDH B SYSTEMS BIOLOGY KMO TDO2 0 CYP11A1 CTNNB1 mut. CDKN2A silencing ACSM2B mutated Silenced PCK2 OTC no no CDO1 MAT1A TP53 mutation Hypermethylation ALDH6A1 MTHFD1 mutated 4 CYP2C8 not mutated CYP4A22 ACADL SC5D 1 GLYAT Gene expression GRHPR SLC27A5 (row-normalized) DPYS SULT2A1 0 0.5 1

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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Gene expression (row-normalized) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● MTHFD1L MTHFD2 MTHFD1 RHBG RHCG SLC13A3 SLC7A6 XDH AQP9 GLUL

Fig. 3. HCC tumors stratified based on metabolic network analysis. (A) We determined three novel HCC subtypes and show the stratifying genes in iHCC1 (green), iHCC2 (cyan), and iHCC3 (orange). (B) Expression of stratifying genes and enzymes catalyzing the same reactions in the three iHCC groups is shown.

(Q < 0.02, χ2 test) larger proportion of advanced tumors in this associated with MYC and AKT activation, as indicated by group [>51% grade (G) 3, <49% G1 and G2] compared with the high incidence of Hoshida 2 (in 96% of tumors; Fig. 3A). iHCC2 (30% G3, <70% G1 and G2) or iHCC1 (<22% G3, >77% Additionally, we identified the top 25 genes coexpressed with G1 and G2). iHCC2 also showed altered P450 and stratifying/controlling genes in each iHCC for 360 TCGA tumors, xenobiotic metabolism in comparison with the two other clusters and observed positive coexpression of AKT1 and MTOR and (SI Appendix, Figs. S8 and S9). stratifying/controlling genes in iHCC3 and their coexpressed genes Interestingly, we had several observations associate altered (Pearson’s r > 0.32, Q < 0.01; Fig. 4). AKT1 and MTOR were Wnt/β-catenin and PI3K/AKT/mTOR signaling with the novel negatively coexpressed with stratifying/controlling genes in iHCC1 iHCC phenotypes described here. Most iHCC3 tumors were and iHCC2. In turn, Hoshida signatures were not substantially

Bidkhori et al. PNAS Latest Articles | 5of10 Downloaded by guest on September 30, 2021 PI3K/AKT/mTOR signaling iHCC1 FASN Kynurenine pathway ELOA RASGEF1B ELOVL6 UBE4B HAL PACS2 UBIAD1 DNAJC11 NAGA ORAI3 DHCR7 ISOC1 EPB41 KPNA6 GCH1 INF2 CEP104 ACOX1 SLC35E2B GBP7 TAT EIF4G3 PPP4R3A DCAF11 TIGD2 MFN2 EBP ACSL1 TRAF3 OPA1 KIF1B HSD17B13 ALDH8A1 MARK3 FDX1 CPSF2 AADAT RCOR1 ZBTB42 TDO2 AKT1 UBR4 SLC6A1 SLC10A1 ACSS2 FBP1 SLC7A2 WNT β-catenin signaling PPP2R5E SLC25A15 MTOR GHR KMO UBR7 ARG1 FURIN DCAF5 GFM1 ACACB CYP4V2 SC5D ASPG QRICH1 SHQ1 CEP170B UBE3C NUDT16 XDH BTBD7 ZBTB1 FCF1 ARFGEF2 F11 RIDA GRAMD4 LNPK SCP2 KNG1 ALDOB PPP1R2 RAB11FIP2 VPS13D PPP1R13B AMDHD1 AREL1 CDC42BPB MYO18A APOOL BHMT USP19 HMGCS2 SDS DLG5 HEATR5A HIPK2 ADGRA3 ROCK2 BAG5 STRIP1 FITM2 MTA1 ADCY9 GYS2 ZNF684 HSD17B6 ACMSD CPS1 NFATC3 TRIP11 SOS2 CFH ATG2B ARHGEF26 ATF1 ITPR2 SLC38A4 NR1I2 SLC27A2 FGGY ASL CTNNB1 PAPOLA FNIP2 TRMT61B PCNX1 GOT1 UGGT1 GLYCTK ABAT SLC22A1 HPX CYP2A6 SPRYD4 ATXN7L3B PTPRG APOL6 AGXT APOF MERTK UGT1A4 FH AMACR DAO C11orf58 STARD13 TWF1 PDLIM7 UGP2 PCYT2 TM7SF2 KIAA0930 TRIB2 FAM8A1 ARIH1 PDS5B GNPDA1 GPHN LIN7A ACTB TPP1 ETV5 HIBADH MAPRE1 MAP7D1 NUP93 DIRC2 PRSS23 ANXA2 PPT1 CD63 ATP13A2 SMARCC1 TSPAN15 RIC1 LIMK1 SEC22C WDR48 PDGFD FAIM EMP3 UTP23 GOLGA4 FAM129B COL6A3 RANBP6 NDRG3 LOXL2 NIPA2 TTC27 TMSB4X OLFML2B PDCD10 WDR12 ISYNA1 SLC44A2 FLNA CHSY1 TUBA1A STAU2 GORASP1 ELMO3 PUM3 RHBDF1 CALM2 KIAA2026 PABPC1 PREPL MITD1 MFF KIF12 RAB20 TPM4 CLEC11A NUAK1 KIAA1429 ERCC3 LSP1 ANXA9 CASP8 UNC50 DRAM2 SH3BGRL3 LGALS1 MRC2 NUDCD1 DSTYK PPP1R13L SPRED1 C2CD2 RNF44 THUMPD2 TSN HDAC7 PLAU RAD21 NEPRO PLEKHB2 VCL SERPINH1 CETN3 KCNK5 SLC9A1 FHOD1 TUBB6 MMP14 STT3B ARMC1 ORC4 WDR75 TMEM43 GPNMB WASHC5 ZDHHC3 IFNGR2 PRMT2 CTDNEP1 WARS S100A11 EXT2 METTL6 MTDH BOK TXNDC9 NIF3L1 TMEM51 MSMO1 ZNF638 VSTM4 SPINT1 TIMP2 RHOBTB2 ZNF696 GNS TGFB3 CPPED1 NOTUM CWC22 NIFK MVP PNMA1 PKM PIP4K2C SMARCAL1 BCAR1 CDK16 ADPGK ACTR3 NEK3 LIPT1 GLUL CHMP4B ACSS1 PSAP MPV17 MTFR1 RHBG DUSP11 NOP58 METTL9 ALDOA COQ8A ACSS3 SLC25A6 G6PD GALNS ACER3 ZNF385A RNF43 GLB1 SQLE PDCD6IP PPP2R1A SRM NUBPL C12orf75 NCDN DBN1 ENO1 CDC14B SLC13A3 AQP9 CRTAP RABEP2 PGD OSBPL8 ASAH1 SNTB1 TBX3 SLC1A2 KPNA4 RHOQ CYP3A4 EHMT2 DCTN5 RABL6 FAM214B AOX1 DNAJC13 G6PC3 XPOT HSD17B4 NADK2 SORD RIPPLY1 MAFG CLASP2 CLIC1 EXOSC10 OXSR1 ANXA4 UBE2I HDDC2 PLBD2 ADH1B THUMPD3 TBC1D10B SCPEP1 LZIC CLDN2 GPAM CCNB1 TMEM39B CAPZB ADH6 RALY CD2BP2 TMEM150CTSC22D1 SLC16A11 ZNF438 ECM2 FBXO31 GNB1 ADPRHL2 YARS KIF2C TPD52L2 RAB7A CLTC HSDL2 HLF IRS1 UBP1 PANX1 SAFB LCMT1 AGTRAP UTP11 ARHGAP1 PTGFRN PTK2 ZCCHC17 GSK3B SPARCL1 CNOT10 KDM1A ACSL5 COPS7B ANKS3 YBX1 PLEKHM1 ABCG2 PSRC1 CMTM4 MLH1 LINC01124 AAAS CDC20 CSTF2 ATAD3B PANK4 ST6GALNAC4 NOC2L PML ATAD3A MRTO4 UBE2J2 WRAP73 SZRD1 SLC52A2 Q < 0.01 TKT iHCC3 RCC1 ZBTB17 AKR1C3 iHCC2 Positive co-expression Negative co-expression

Fig. 4. Coexpression analysis highlights the association between stratifying and controlling genes in iHCC subtypes. Stratifying and controlling genes for iHCC1, iHCC2, and iHCC3 and their top 25 coexpressed genes are included. Coexpression between iHCCs was determined based on TCGA transcriptomics data of 369 HCC tumor samples. We additionally included AKT1 and MTOR, factors involved in PI3K/AKT/mTOR signaling, and CTNNB1, which en- codes for the β-catenin in the . Edges indicate positive (red) or negative (blue) Pearson correlations (Q < 0.01).

different between iHCC1 and iHCC2 (22 and 11% Hoshida 1, (Gene Expression Omnibus accession no. GSE9843), we observe respectively, Q > 0.3, χ2 test). that all tumors with CTNNB1-phosphorylating activation and The following five observations suggested a strong association mutation showed high expression of iHCC2-stratifying genes. between disturbed Wnt signaling and the iHCC2 phenotype. Additionally, tumors showing RPSA, AKT, or IGFR activation First, 75% of iHCC2 tumors showed mutations in CTNNB1, a showed high expression of iHCC3-stratifying genes, thus reinforc- gene that codes for β-catenin in the Wnt pathway (Fig. 3A), ing the relationship between PI3K/AKT/mTOR signaling activa- substantially higher than the <13% observed in iHCC1 and tion and iHCC3. Tumor stratificationbasedoniHCC-stratifying − iHCC3 (Q < 10 5, χ2 test). Second, iHCC2 tumors also showed genes showed differential distribution in the HCC subgroups pre- up-regulated expression of β-catenin target genes, for instance viously identified (10) (Dataset S6). Lastly, a transcriptomics dataset GLUL, SLC1A2, with four HCC samples displaying CTNNB1 mutation (27) showed and ornithine aminotransferase (SI Appendix, Fig. S7). Third, high expression of many iHCC2-stratifying genes including GLUL, coexpression analysis indicated that stratifying/controlling genes RHBG, SLC13A3, and ACSS3 (SI Appendix,Fig.S11). These ob- in iHCC2 and their coexpressed genes are positively coexpressed servations thus indicated distinct genomic features for the iHCC2 with CTNNB1 (Pearson’s r > 0.32, Q < 0.01; Fig. 4). This is not and iHCC3 phenotypes, respectively associated with aberrant Wnt observed in the case of iHCC3/iHCC1 genes, which are nega- signaling and PI3K/AKT/mTOR signaling activation. Interestingly, tively coexpressed with AKT1 or MTOR (Pearson’s r < −0.2, three stratifying genes (TDO2, KMO, XDH) and two coexpressed Q < 0.01). Fourth, the association between Wnt signaling in genes (AADAT, ACMSO) are involved with the kynurenine path- + iHCC2 and AKT activation in iHCC3 was also identified using way (KP) (Fig. 4), a leading to NAD production an independent dataset of 91 HCC microarray samples and as- and associated with tryptophan metabolism (39). iHCC1 also shows sociated immunohistochemistry (SI Appendix, Fig. S10). Associ- up-regulated tryptophan metabolism in comparison with the two ations between different HCC tumors and IFN, proliferation other iHCC groups (Dataset S4). (PI3K/AKT activation), CTNNB1 phosphorylation/mutation Together with the above observations, the validations in three (i.e., Wnt signaling), or polysomy were previously independent datasets at the genomic, transcriptomic, and pro- identified (10). Using the authors’ previously defined classes teomic level (10, 13, 26, 27, 40) additionally reinforced our

6of10 | www.pnas.org/cgi/doi/10.1073/pnas.1807305115 Bidkhori et al. Downloaded by guest on September 30, 2021 confidence in the stratifying genes and survival differences in transporters also showed substantial switching between iHCC1 iHCC1, iHCC2, and iHCC3. Specifically, the metabolic network- and iHCC3. derived antagonistic expression of stratifying genes identified in Controlling genes are also differentially expressed between the 186 HCC tumor transcriptomics data is consistently observed in three HCC subtypes (Dataset S6), indicating different control- (i) a validation transcriptomics dataset of 183 HCC tumors lability metabolic behavior between them. For instance, in fatty attained from TCGA (SI Appendix, Fig. S12A); (ii) a microarray acid elongation, ELOVL6 is a controlling gene in iHCC2 but dataset consisting of 221 HCC samples (SI Appendix, Fig. S12B); (iii) coexpression analysis of 369 HCC tumors from TCGA (Fig. 4); (iv) a microarray dataset comprising 91 HCC tumors (SI iHCC1 with Appendix, Fig. S10); and (v) a comparison of CTNNB1 mutant A iHCC2 with Highest flux SI Appendix 2nd highest flux iHCC3 with versus a noncancerous transcriptomics set ( , Fig. Highest flux S11). Additionally, survival analysis performed on the validation SI Appendix Warburg effect Fatty acid TCGA dataset or an additional dataset (13, 26) ( , ROS metabolism Fig. S12) was consistent with the observed survival differences in Oxidative metabolism metabolism phosphorylation Fatty acid Glycine, serine and metab. iHCC1 > iHCC2 > iHCC3 (Fig. 2B). biosynthesis metab. TCA Pentose Glycosphingolipid Tryptophan metab. pathwaypathway Alternative Metabolic Differences Between HCC Subtypes. We fur- metab. β metab. Xenobiotic metab. by Fatty acid transfer cytochrome P450 ther identified metabolic differences between iHCC1, iHCC2, reactions , , and metabolism metab. and iHCC3 at a pathway- and reaction-centered level using GEMs. Fatty acid β oxidation metab. Estrogen metab. metab. Sulfur metab. GEMs were generated for each cluster through MADE (41) and Phytanic acid Steroid metab. β oxidation metab. Bile acid biosynthesis TIGER (42), using as input the differentially expressed genes and Cholesterol metab. and recycling Carnitine shuttle Amino and metab. considering maximization of biomass as an objective function. We Pyruvate metab. nucleotide sugar metab. found that fluxes in each of the models (Fig. 5A) were consistent with the hallmarks of cancer identified above (Fig. 2A) and with the B Cell membrane expression data mapped onto Kyoto Encyclopedia of Genes and FBP2 -1P SGPP2 PPAP2C FBP1 PGM5 Genomes (KEGG) metabolic pathways (Dataset S3), as well as with FAD F6P PGM2PGM1 PPAP2C FBP G6P the substantial metabolic differences between iHCC3 and iHCC1 or PFKM GCK HKDC1 PFKP HK2 glucose SLC7A8 HK3 iHCC2. Specifically, iHCC3 GEMs showed lower fluxes in metab- SLC3A1 ALDOA SLC7A7 SLC38A4 ALDOB SYSTEMS BIOLOGY olism of amino acids, cofactors and coenzymes, pyruvate, fatty acid SLC7A1 SLC7A2 SLC7A6 GAP DHAP oxidation, carnitine shuttle, , and oxidation phosphorylation Amino acids SLC1A2 SLC7A11 SLC1A7 GAPDH compared with iHCC1/iHCC2, and lower in iHCC2 than iHCC1. SLC38A1 SLC38A3 Compared with iHCC1/iHCC2, iHCC3 showed higher glycolytic SLC36A3 SLC1A1 BPG SLC38A5SLC25A20 ACYP2 but lower cycle (TCA) fluxes consistent with a strong MIA3 SLC1A5 SLC6A7 ACYP2ACYP2 Warburg effect, as well as higher fluxes of fatty acid biosynthesis. BPG isocitrate Additionally, differences in the gene expression indicated that FABP6 AKG PGA OGDHL DHA SLC27A6 FABP4 ACO1 the three iHCC groups rely on alternative enzymes for catalyzing butyrate ENO1 DHTKD1 FABP5 ENO2 ACO2 B B ENO3 the same reactions (Fig. 3 and 5 ). For instance, acetate is citrate succinyl-CoA Glucose converted to acetyl-CoA by acetyl-CoA synthases; the reaction PEP SLC2A2 SLC2A9 PKM is catalyzed by ACSS1, ACSS2, and ACSS3. iHCC1 expresses SLC2A10 CS SUCLG2 SLC5A11 PKLR CLYBL SLC2A1 SLC2A4 ACSS2, localized in the , whereas iHCC2 and iHCC3 pyruvate acetyl-CoA Mitochondria succinate express ACSS3 and ACSS1, respectively, which are localized in SDHA OAA SDHB SI Appendix Citrate SLC13A3 SLC13A5 acetyl-CoA SDHC the mitochondria ( , Fig. S13). Cleavage of fructose- ACSS1 SDHD fumarate ACSS3 malate 1,6-bisphosphate is catalyzed by aldolase and the reaction is ACSS2 FH Choline SLC44A3 SLC22A1 acetate catalyzed by ALDOA, ALDOB, and ALDOC. While iHCC1 highly acetate ALDH3A1 ALDH9A1 ALDH1B1 ALDH2 expresses the liver-specific ALDOB, iHCC3 highly expresses the Estrone sulfate ALDH3B1 PGE2 SLCO4A1SLCO1B1 ALDH7A1 nonspecific ALDOA, and iHCC2 shows similar expression for both Thyroxine acetaldehyde genes. Alternative utilization of pyruvate is also observed. iHCC1/ iHCC2 show high expression of the liver-specific PKLR, whereas C ACSS1 ALDOB iHCC3 shows high expression of PKM. PKLR has recently been ALDOA 28 8 TDO2 G6PD FBP1 identified as a liver-specific target for effective treatment of fatty GNS XDH liver disease and HCC (43). Our analysis enabled the identification ADPGK iHCC3 0 iHCC1 KMO of the right patient population where PKLR or PKM inhibitors can be used for effective treatment of patients. ENO1 5 125 IDH1 TALDO1 SORD in iHCC1 is switched to 2 in iHCC3 SLC35D2 ARG1 PDXK iHCC2 CLYBL whereas ENO3 is substituted for ENO1. Additionally, PFKFB4, AMDHD2 PNPO HSD17B6, and GLYATL2 in iHCC3 are switched to PFKFB1, ACSS3 SQLE HSD17B1, and GLYAT in iHCC1 and iHCC2, respectively. We LIPT1 GLB1 9 also had similar observations of expression of genes that encode SLC13A3 for aldehyde dehydrogenases (e.g., ALDH1B1, ALDH9A1, ALDH2, ALDH3A2, ALDH3B1), among others (SI Appendix,Fig.S13). Fig. 5. iHCC subgroups rely on alternative enzymes catalyzing the same re- A number of amino acids, , cofactors, and hormone trans- actions and display specific synthetic lethal genes. (A) porters are also differentially expressed between the three clusters, performed on iHCC-specific models shows that iHCC1 or iHCC3 displays the and in particular between iHCC3 and iHCC1/iHCC2 (Fig. 5B). highest reaction fluxes, followed by iHCC2. The predominant color in each box These observations translated into distinct central metabolism, shows the iHCC subtype that displays the highest flux. (B) Metabolic genes involved in transport, glycolysis, and the TCA colored according to expression particularly between the high- and low-survival groups including in each subgroup. (C) Numbers of synthetic lethal genes found in iHCC sub- iHCC1 and iHCC3, while iHCC2 shared many of these properties groups are shown, highlighting five synthetic lethal genes per subgroup. No with iHCC1. Several membrane transporters including amino synthetic lethal genes are simultaneously identified in iHCC1 and iHCC3, but acid, glucose and monosaccharide, choline, butyrate, and citrate several are found between iHCC2 and the other subgroups.

Bidkhori et al. PNAS Latest Articles | 7of10 Downloaded by guest on September 30, 2021 ELOVL5 is a controlling gene in iHCC1 and iHCC3. In glyc- Discussion erolipid metabolism, PLA2G12B is a controlling gene in iHCC1, Given the high heterogeneity in HCC, previous studies stratified PLA2G16 is a controlling gene in iHCC3, and both are controlling HCC patients through unsupervised clustering of tumors based genes in iHCC2. In purine and pyrimidine metabolism, NME6 and on genomics and transcriptomics data (6, 7, 13). This led to the NT5E are controlling genes in iHCC3 but not in iHCC1/iHCC2, which showed another NME as a controlling gene. In glycolysis, PKLR and BPGM are controlling genes in iHCC1/iHCC2, but PKM and PGAM1 are controlling genes in iHCC3. In histidine A iHCC3

● ● ● ● ● ● metabolism, NAA15 and SLC40A1 are controlling genes in iHCC1 1.0 ● ● ● ● ● ● ● ● and SLC11A2 is a controlling gene in iHCC2, whereas NAA30 and ●● ● ● ● ● ● ● ●● ● ● ● SLC40A1 are controlling genes in iHCC3. ● ● ● We showed the importance of controlling genes. Next, we 0.5 ● ●● ● ● ● performed synthetic lethality analysis for those stratifying and ● ● Normoxia ● ● ● ● controlling genes that were found just in HCC networks. This ● Hypoxia ● ●● ● ● ● ● ● ● ● ● enabled the identification of several potential therapeutic targets. ● ● ● ● ● ● ● ● 0 ●● ● ● ● ● ● *Q > 0.01 Among controlling and stratifying genes, we find 8, 9, and 28 subtype- Gene expression (normalized) PKM ALDOA ENO1 PDE9A MTHFD1L SLC38A1 specific genes in iHCC1, iHCC2, and iHCC3, respectively, whose in iHCC3 (controlling only) silico knockout leads to lethality in their respective subtype but not in 1.0 ● ● ● ● ● ● ● ● ● ● C ● ● ●● ● ● ● ● the others (Fig. 5 ). Among these, we identified ALDOB, TDO2, ● ● ● ● ● ● ● ● and KMO in iHCC1, ACSS3, SQLE, and LIPT1 in iHCC2, and ● ● ● ● ● ● ● ● ACSS1, ALDOA, and G6PD in iHCC3. Knockout of 12 controlling/ ● ● ● ● ● ● 0.5 ● stratifying genes simultaneously leads to lethality in iHCC1 and iHCC2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● and includes IDH1, SORD, and ARG1. Several of these synthetic ● ● ● ● ● ●● ● ● ● ● lethal genes are known targets of DrugBank drugs, including some ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● Food and Drug Administration (FDA)-approved drugs (Dataset S7). Gene expression (normalized) OCRL PTPN12 HPSE ACLY LPCAT1 RRM2 SPTLC2 These observations pointed toward several HCC-specific potential therapeutic targets that may be used to target low- (iHCC3), in- B iHCC2 C iHCC1

● ● ● ● ● ● ● ● ● ● termediate- (iHCC2), and high- (iHCC1) survival groups. We 1.0 ● ***** ● ● ● ● ● identified FDA-approved drugs and inhibitors, among other ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● drugs, whose known targets include these genes (Dataset S7). ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● Poor Survival-Associated Genes Showed Hypoxic Behavior. Several ● ● ● ● ● ● ● ● ● ● ● ● stratifying genes in the three iHCC groups are associated with ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● SI Appendix ● ● ● metabolism (Fig. 3 and ,Fig.S10)suchas ● ● ● ● ● ● ● ● 0 ●● ● ● ● ● ● ● ●● ● ● ●● ●●●●●● ●●●●●● ● G6PD, PKM, and ALDOA in iHCC3, or ALDH2 and MTHFD1 Gene expression (normalized) ACSS3 AOX1 AQP9 CYP3A4 RHBG SLC13A3 GLUL XDH KMO TDO2 SC5D in iHCC1 and iHCC2. Gene expression differences translated into A D ADP metab. altered redox metabolism (Fig. 2 ) and defenses (e.g., Anatomical structure formation in morphogenesis or glutathione-based H O scavenging; Dataset S5). Ad- ATP generation from ADP 2 2 metab. ditionally, we have previously observed stratification of HCC Generation of precursors and energy samples based on acetate metabolism and hypoxia (11). Indeed, RRM2 Glucose metab. MTHFD1L Glycosyl compound metab. the expression of HIF1A is substantially higher in iHCC3 than in metab. PTPN12 Homeostasis iHCC1 or iHCC2 (log2 fold change ∼ 1, Q < 0.05). Monosaccharide metab. NAD metab. We performed a transcriptomics analysis of HepG2 cells NADH metab. OCRL SI Appendix Nucleobase containing grown under hypoxia and normoxia ( ,Fig.S14). We small molecule metab. found (SI Appendix,Fig.S14C; Q < 0.01) that differentially NDP metab. expressed genes are associated with responses to stress and NMP metab. PDE9A NTP metab. , NADH, ADP, and RNA metabolism. We found that Nucleotide phosphorylation and tissue development biological processes Organonitrogen compound metab. are up-regulated under hypoxia. In turn, DNA metabolism, LPCAT1 replication and repair, and -related processes are up- Oxidation reduction regulated under normoxia. Further, among the differentially Redox coenzyme metab. PKM Phosphate containing expressed genes, the expression of the stratifying genes PKM, compound metab. ALDOA, MTHFD1L, ENO1, and PDE9A and controlling Phosphorylation ENO1 Protein complex organization genes in iHCC3 is significantly higher under hypoxic than Protein oligomerization Q < A Purine containing compound metab. normoxic conditions ( 0.01; Fig. 6 ). Interestingly, stratifying ALDOA Purine NMP biosynthesis and controlling genes in iHCC2 or iHCC1 are either unchanged or HPSE Purine ribonucleoside monophosphate biosynthesis show down-regulated expression under hypoxia (Fig. 6 B and C, Pyruvate metab. respectively). Additionally, among the 28 controlling genes ex- Response to external stimulus Log fold change Response to wounding clusive to iHCC3 (Fig. 5C), we find that the expression of OCRL, Ribonucleoside diphosphate metabolism Tissue development PTPN12, HPSE, ACLY, LPCAT1, RRM2, and SPTLC2 is up- −3 0 Wound healing regulated under hypoxia (Fig. 6A). Among those stratifying/con- trolling genes up-regulated under hypoxia in iHCC3, we found Fig. 6. Stratifying and controlling genes in iHCC3 show specific responses to multiple up-regulated biological processes that also involve these hypoxia. HepG2 cell lines were grown under normoxic or hypoxic conditions (n = 6 per condition) and transcriptomics data were generated. Expression of genes, including those involved in energetic, carbohydrate, and D stratifying and controlling genes in iHCC3 (A), iHCC2 (B), and iHCC1 (C), and nucleotide metabolism and tissue development (Fig. 6 ). These gene association with enriched biological processes (D; Q < 0.05). All genes observations indicate that stratifying and controlling genes in with the exception of CYP3A4, GLUL, XDH, KMO, and TDO2 (Q > 0.01) are iHCC3 and iHCC1/iHCC2 tumors respond strongly and antago- differentially expressed between hypoxia and normoxia. NMP, NDP, and NTP nistically to hypoxic behavior. indicate nucleoside mono, di-, and triphosphate, respectively.

8of10 | www.pnas.org/cgi/doi/10.1073/pnas.1807305115 Bidkhori et al. Downloaded by guest on September 30, 2021 valuable discovery of many patient group-specific differences suggesting a potential association between those diseases and such as cholangiocarcinoma-like feature traits (6), hepatic stem- iHCC1 tumors. Overall, iHCC1 showed the highest fluxes in like phenotypes (7), signaling differences (8–10), recurrence risk metabolism of amino acids, cofactors and coenzymes, pyruvate, (14, 38), and metabolism (11). In our study, we introduced an fatty acid oxidation, carnitine shuttle, steroids, TCA, and oxidative fGGN-based characterization and stratification of HCC patients. phosphorylation. We combined objective-dependent and -independent approaches iHCC2 showed higher similarity to iHCC1 compared with to perform a functional metabolic network-based stratification of iHCC3, but also exhibited specific features including lower fatty hundreds of HCC patients. Our analyses combined genomics, acid biosynthesis and high glutamine metabolism, and β-catenin– transcriptomics, proteomics, and clinical data across four datasets associated up-regulated fatty acid oxidation. One of the main with hundreds of HCC tumors, in silico analysis including genome- features of iHCC2 tumors is the association with β-catenin scale metabolic modeling, gene silencing, coexpression and net- pathway alterations. CTNNB1 encodes for β-catenin, which is work analysis, and additional cell-line experiments. We identified mutated in ∼20% of HCCs (48) (SI Appendix, Fig. S15). Muta- distinct differences in metabolic and signaling pathways and in tions in this gene are associated with increased concentration of clinical survival between three major HCC subtypes, iHCC1 to nuclear β-catenin and its target genes (e.g., glutamine synthetase iHCC3. The three iHCCs presented 18 metabolic genes highly GLUL and glutamate transporter SLC1A2) and lower patient expressed by one group but not the others, namely stratifying survival (49, 50). Glutamine synthetase is involved in genes. Our analyses showed that these genes can be consistently detoxification, and β-catenin–controlled induction of GLUL used for stratifying HCC tumors in independent cohorts. We have leads to autophagy in HCC (51). β-Catenin controls mitochon- additionally identified 32 controlling genes, those that display drial homeostasis by regulating the TCA and fatty acid oxidation, pivotal roles in controlling networks and whose targeting would and protects against -induced liver injury or ethanol- lead to lethality in one of the HCC subtypes. induced metabolic stress (SI Appendix, Fig. S16) (52). This is Tumors in the low-survival and progressive (higher-grade) consistent with the overactivation of the pathways involved in iHCC3 group showed several signatures of low survival and high detoxification, that is, drug and xenobiotic metabolism in com- recurrence (13, 14) and high expression of markers of poor parison with other subtypes. β-Catenin also regulates the expres- survival (15). In turn, the high- and moderate-survival groups sion of acetaldehyde dehydrogenases (e.g., ALDH2, ALDH3A1, iHCC1 and iHCC2 were associated with markers of low re- and ALDH3A2) (52), thus controlling TCA fluxes, as well as the currence, high survival, hepatocyte differentiation, and maturity stratifying gene acyl-CoA oxidase (AOX1), which is involved in (8, 14, 38). iHCC1, iHCC2, and iHCC3, respectively, displayed fatty acid β-oxidation (53). Our modeling analyses additionally SYSTEMS BIOLOGY high expression of genes involved in acetate metabolism, indicated that iHCC2 showed low fatty acid biosynthesis fluxes, ACSS2, ACSS3, and ACSS1. These observations are consistent consistent with the negative regulation of this process by β-catenin. with our previous analyses which showed that HCC tumors can Sorafenib, a drug that targets expression of liver-related Wnt- be stratified based on acetate metabolism (11). ACSS2 is highly target GLUL and leads to higher sensitization in HCC tumors expressed by healthy liver tissue (11), consistent with the high with high β-catenin activation (54), thus presents as a potential expression displayed by the high-survival iHCC1 group. On the drug in iHCC2 but not in the other tumor groups. other hand, ACSS1 is highly expressed in iHCC3, consistent with Finally, iHCC3 tumors were associated with multiple features previous observations in a low-survival group (11). Finally, we iden- of malignant tumors, including hypoxic behavior, epithelial-to- tified that iHCC2 displays high expression of ACSS3, unlike mesenchymal transition, higher fluxes in fatty acid biosynthesis, other iHCCs. and a strong Warburg effect. For instance, TGF-β, HIF-α, and In turn, iHCC3 tumors showed high expression of ACSS1 and NF-κB genes associated with hypoxic response, , and are associated with hypoxic environments. Experiments with malignancy are up-regulated in iHCC3, and iHCC3 shares sev- HepG2 cells additionally showed strong and opposing responses eral signature activities of metastatic tissues (55). One of the to hypoxia by different iHCCs. Stratifying and controlling genes main features of this tumor group is the association with PI3K/ in iHCC3 are up-regulated under hypoxia compared with nor- AKT/mTOR signaling activation. It also showed downstream moxia. This is opposed by those in iHCC1 and iHCC2, which are activation of synthetase (ASNS), glycolysis, and the either unchanged or show decreased expression under hypoxic pentose phosphate pathway by PI3K/AKT/mTOR signaling (56), conditions. These observations indicated opposing hypoxic re- consistent with our observation in iHCC3 (SI Appendix, Fig. sponses under low and high survival. S16). Up-regulation of ASNS in iHCC3 is significantly correlated iHCC1 is the tumor group with the highest survival, and with metastatic potential, and overexpression of ASNS promotes showed a high inflammation response compared with iHCC2. metastatic progression (57). Drugs targeting PI3K/AKT/mTOR Interestingly, several stratifying genes or their coexpressed genes signaling or these processes, such as L-, rapamycin, are involved in the KP. This pathway is found upstream of NAD or their analogs, thus arise as potential therapeutics for the biosynthesis, and is the main tryptophan sink in the cell (39). treatment of iHCC3 but not the other iHCCs. Several of its genes are up-regulated in in obesity Overall, these observations highlighted distinct differences in (44, 45), consistent with the observation that 56% of patients in metabolic and signaling pathways in HCC tumors that stem from iHCC1 are obese or overweight. Several metabolites of the KP the high intertumor heterogeneity and were associated with pa- have been associated with inflammatory and immune responses, tient survival. In silico predictions enabled identification of sev- for instance in the induction of cytokines and macrophage- eral HCC subtype-specific potential therapeutic gene targets that induced chemokines (46). Interestingly, KP overactivation has offer full control over the metabolic network. Revealing the been observed in type 2 (T2D) and is one of the T2D- mechanistic differences between HCC subtypes, together with driving mechanisms observed in prediabetic patients (47). The the identification of HCC subtype-specific drug targets, may observation that T2D is one of the risk factors for HCC (47) foster the development of efficient treatment strategies and raises the possibility that the iHCC1 phenotype is associated with precision medicine for HCC patients. T2D, unlike other iHCCs. This is reinforced by the observation that the genes TDO, KMO, AADAT and ACMSD, and IL6R, Materials and Methods stratifying genes in iHCC1 or their coexpressed genes, are up- All of the materials and methods in this study are detailed in SI Appendix, regulated in obesity or T2D (47). Finally, both T2D and obesity Materials and Methods, including gene expression retrieval, processing, and showed activated fatty acid oxidation, similar to iHCC1, which validation datasets; generation of personalized and subtype GEMs; construc- displayed the highest fatty acid oxidation of the three iHCCs, tion of personalized and generic directed functional gene–gene networks;

Bidkhori et al. PNAS Latest Articles | 9of10 Downloaded by guest on September 30, 2021 centrality and controllability of personalized fGGNs; identification of control- targeting synthetic lethal genes; validation; hypoxia experiments in HepG2 ling genes through in silico gene silencing analysis; network-based stratifica- cells; and statistics. tion of fGGNs; generation of fGGNs and tumor stratification based on Recon3D; differential expression and gene set enrichment analysis; KEGG ACKNOWLEDGMENTS. This work was financially supported by the Knut and pathway analysis; coexpression analysis; identification of potential drugs Alice Wallenberg Foundation.

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