Supplemental material to this article can be found at: http://molpharm.aspetjournals.org/content/suppl/2015/12/14/mol.115.101360.DC1
1521-0111/89/2/263–272$25.00 http://dx.doi.org/10.1124/mol.115.101360 MOLECULAR PHARMACOLOGY Mol Pharmacol 89:263–272, February 2016 U.S. Government work not protected by U.S. copyright
A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy s
Jean-Pierre Gillet,1 Jesper B. Andersen,2 James P. Madigan, Sudhir Varma, Rachel K. Bagni, Katie Powell, William E. Burgan, Chung-Pu Wu, Anna Maria Calcagno, Suresh V. Ambudkar, Snorri S. Thorgeirsson, and Michael M. Gottesman
Laboratory of Cell Biology (J-P.G., J.P.M., C-P.W., A.M.C., S.V.A., M.M.G.) and Laboratory of Experimental Carcinogenesis (J.B.A., Downloaded from S.S.T.), Center for Cancer Research, National Cancer Institute, and Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, Office of Science Management and Operations, National Institute of Allergy and Infectious Diseases (S.V.), National Institutes of Health, Bethesda, Maryland; and the Viral Technologies Group and Molecular Detection Group, Protein Expression Laboratory, Frederick National Laboratory for Cancer Research, National Institutes of Health, Frederick, Marylanld (R.K.B., K.P., W.E.B.)
Received August 14, 2015; accepted December 11, 2015 molpharm.aspetjournals.org
ABSTRACT Despite improvements in the management of liver cancer, the lines from ones matching patients with poor OS to profiles survival rate for patients with hepatocellular carcinoma (HCC) associated with good OS. We found three compounds that remains dismal. The survival benefit of systemic chemother- convert the gene expression profiles of three HCC cell lines to apy for the treatment of liver cancer is only marginal. Although gene expression profiles associated with good OS. These the reasons for treatment failure are multifactorial, intrinsic compounds increase histone acetylation, which correlates resistance to chemotherapy plays a primary role. Here, we with the synergistic sensitization of those MDR tumor cells to analyzed the expression of 377 multidrug resistance (MDR)- conventional chemotherapeutic agents, including cisplatin, at ASPET Journals on September 25, 2021 associated genes in two independent cohorts of patients with sorafenib, and 5-fluorouracil. Our results indicate that it is advanced HCC, with the aim of finding ways to improve possible to modulate gene expression profiles in HCC cell survival in this poor-prognosis cancer. Taqman-based quan- lines to those associated with better outcome. This approach titative polymerase chain reaction revealed a 45-gene signa- also increases sensitization of HCC cells toward conventional ture that predicts overall survival (OS) in patients with HCC. chemotherapeutic agents. This work suggests new treatment Using the Connectivity Map Tool, we were able to identify strategies for a disease for which few therapeutic options drugs that converted the gene expression profiles of HCC cell exist.
Introduction whereas cholangiocarcinoma (Ghouri et al., 2015) and fibro- lamellar carcinoma (Lim et al., 2014; Cornella et al., 2015) Liver cancer is the third most common cancer in the world, occur at a frequency of only ∼14% and ∼1%, respectively. The causing approximately 745,000 deaths per year (Ferlay et al., epidemiology of HCC is well known, and in the vast majority of 2015). Hepatocellular carcinoma (HCC) is by far the most cases, it arises as a consequence of underlying liver disease, – prevalent type, accounting for approximately 80% 85% of usually a viral hepatitis (Singal and El-Serag, 2015). In the primary liver cancer cases (Singal and El-Serag, 2015), case of hepatitis B, integration of the viral DNA into the hepatocyte genome results in loss of chromosomal stability, This research was funded by the Intramural Research Program of the deregulation of tumor-suppressor genes, and activation of National Institutes of Health (NIH). The project was funded in part with federal funds from the National Cancer Institute NIH [Contract proto-oncogenes, eventually leading to the development of HHSN2612008000001E]. The content of this publication does not necessarily HCC (Su et al., 2014). reflect the views or policies of the Department of Health and Human Services, Patients with early stage tumors undergo either surgical nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. resection or liver transplantation if their HCC meets the so- 1Current affiliation: Laboratory of Molecular Cancer Biology, Molecular called Milan criteria (Mazzaferro et al., 1996; Waller et al., — Physiology Research Unit URPhyM, Namur Research Institute for Life 2015). When surgery is not a suitable option, local ablation, Sciences (NARILIS), Faculty of Medicine, University of Namur, Belgium. 2Current affiliation: Biotech Research and Innovation Centre, University of including radiofrequency ablation and percutaneous ethanol Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark. injection, are standard treatment. Transcatheter arterial dx.doi.org/10.1124/mol.115.101360. s This article has supplemental material available at (molpharm. chemoembolization is recommended for patients with inter- aspetjournals.org). mediate stage HCC (EASL-EORTC, 2012; Villanueva et al.,
ABBREVIATIONS: HCC, hepatocellular carcinoma; MDR, multidrug resistance; OS, overall survival; TLDA, TaqMan low-density array.
263 264 Gillet et al.
2013). With the exception of sorafenib, a multi-tyrosine kinase Materials and Methods inhibitor for which a survival benefit of 3 months was Tumor Samples. Anonymized clinical samples of 38 HCCs and 13 demonstrated, no effective systemic therapy exists for pa- normal liver tissues were provided by Lee et al. (2004). Most of the tients with advanced HCC (Llovet et al., 2008; Llovet et al., patients had a hepatitis B virus background, but a few had hepatitis C 2015). Although sorafenib is now established as the first line of virus and alcoholic backgrounds. All the samples originated from therapy for advanced HCC, it was shown to be a substrate of untreated primary resected tumors. Importantly, even though most of ABCB1 and ABCG2, two major ABC transporters involved in the patients in this cohort were infected with HBV (which is also the multidrug resistance (MDR) and expressed in hepatocytes and common background for Chinese HCC patients), the cohort also hepatomas (Lagas et al., 2010; Tang et al., 2013). Compre- includes hepatitis B virus–, hepatitis C virus– and alcoholic-related hensive molecular profiling contributed to substantial im- HCC cases, simulating a “true” clinical situation, as patients with provement in our knowledge of the biology of liver cancer and HCC are not normally a homogeneous group. Thirty-eight HCC samples were randomly selected to be reanalyzed among samples provides a road map to facilitate the development of targeted that were previously classified into two groups based on overall therapies (Andersen and Thorgeirsson, 2012; Pinyol et al., survival (Lee et al., 2004). Lee et al. (2006) demonstrated that 2014; Bruix et al., 2015; Simon et al., 2015). Besides proof- although patients may originate from different ethnic groups, the of-concept trial testing signaling pathway inhibitors or cohort could still be considered homogeneous at the molecular level. A biomarker-based trial enrichment for defining cancer sub- total of 17 normal liver samples were analyzed. Total RNA for N1-N4 Downloaded from populations, there is still a need for unspecific drugs that was purchased commercially, N1 from Ambion (catalog no. AM7960; target all patients (Llovet and Hernandez-Gea, 2014). Austin, TX), N2 from Stratagene (catalog no. 540017; Santa Clara, Although the reasons for treatment failure are multifacto- CA), N3 from Clontech (catalog no. 636531; Mountain View, CA), N4 rial, intrinsic resistance to chemotherapy plays a primary role. from Biochain Institute (catalog no. R1234149-50; Newark, CA). RNA Here, the expression of MDR-associated genes was analyzed from N5-N17 was provided by the Laboratory of Experimental in two independent cohorts of patients with advanced HCC. Carcinogenesis, National Cancer Institute (Bethesda, MD).
TaqMan Low-Density Arrays. Expression levels of 377 MDR- molpharm.aspetjournals.org We hypothesized that the Connectivity Map Tool might reveal associated genes were measured in the previously mentioned samples compounds that reverse the gene expression profile of cancers using a custom-made Taqman Low-Density Array (TLDA; Applied from patients with poor prognosis to that of cancers from Biosystems, Foster City, CA), as previously reported (Calcagno patients who respond well to treatment (Lamb et al., 2006; et al., 2010). Lamb, 2007; Zhang and Gant, 2008). The ultimate aim is to Normalization and Filtering. The median expression of each find a new strategy to sensitize intrinsically MDR cancer. sample was subtracted from all gene-expression data for that sample.
TABLE 1 Differentially expressed genes in HCC samples compared with normal liver samples at ASPET Journals on September 25, 2021 Adjusted Upregulated Adjusted Upregulated Adjusted Downregulated Genes P Value Ratio Genes P Value Ratio Genes P Value Ratio CYP2E1 1.98E-04 239 TOP2A 3.19E-12 67 KIT 1.42E-02 3 CYP1A2 8.28E-04 233 CDKN2A 7.88E-10 45 GSTA4 4.05E-05 3 SLC22A1 5.60E-05 228 MKI67 2.56E-09 29 ACTB 2.02E-07 3 CYP3A4 1.63E-02 223 SFN 1.95E-04 17 ABCB9 4.12E-02 3 SLCO1B3-SLC21A8 7.50E-05 223 BIRC5 1.92E-10 17 C8orf33 2.63E-06 3 MT1H 2.01E-05 221 E2F1 6.92E-12 15 ABCD1 3.07E-05 3 MT1X 4.45E-09 219 XRCC2 1.95E-07 11 HSP90AA1 8.43E-03 2 SLC28A1 3.93E-04 218 CCNE1 6.66E-07 9 NRAS 3.45E-06 2 CYP2C19; CYP2C8 2.69E-03 215 SLC2A5 7.85E-03 8 UGCG 3.00E-04 2 MT1A 1.31E-05 215 SLC16A3 1.07E-04 8 CDC42 1.51E-06 2 CYP2C8 2.81E-04 215 RAD51 1.30E-07 8 TRAF1 2.53E-03 2 MT1F 2.82E-04 213 MMP9 2.29E-04 7 CDK2 7.01E-03 2 CYP2B6 3.23E-03 210 SLC7A11 1.46E-02 7 ATP1B1 5.59E-03 2 CYP2C9 7.04E-03 210 CHEK1 2.91E-05 7 PDGFRB 5.70E-03 2 GSTM5 6.08E-04 29 ITGAE 2.25E-02 6 SLC29A1 2.46E-03 2 ABCC9 3.01E-08 28 SLC7A5 1.00E-03 6 UBL5 3.66E-05 2 MT2A 1.12E-03 26 MSH2 1.23E-09 5 ABCC10 3.09E-04 2 SLCO4A1-SLC21A12 9.09E-03 26 XRCC3 1.21E-06 5 CFL1 4.85E-06 2 ABCA9 2.71E-03 25 GJA1 6.38E-09 4 CDK4 3.08E-02 2 ABCC11 8.18E-04 25 BRCA2 1.09E-04 4 XRCC1 1.22E-04 2 SLC7A2 6.73E-04 24 S100A10 3.43E-05 4 COX7A2 1.08E-03 2 ABCG4 6.47E-04 23 CLDN7 3.31E-02 4 BAX 2.51E-04 2 ABCA1 1.14E-02 22 TGFB1 2.94E-04 3 HSF1 5.28E-04 2 STAT3 7.49E-05 22 LBR 4.76E-07 3 SIRT7 1.27E-04 2 18S 3.89E-08 22 SLC1A5 2.32E-02 3 MSH6 2.82E-05 2 SEPX1 1.71E-03 22 CHEK2 4.17E-04 3 ABCF2 1.06E-03 2 PDCD8/AIFM1 3.85E-03 22 ABCC4 1.10E-02 3 ABCC5 1.35E-02 2 SLC31A1 1.36E-04 22 TXN 4.55E-10 3 CTNNB1 3.48E-03 2 AKT1 4.92E-02 22 SRC 4.41E-03 3 GPX4 2.63E-02 2 NR1H3 4.75E-02 22 RAD18 3.94E-09 3 RAD1 1.46E-02 2 CFLAR 1.46E-02 21.5 HSPB1 8.50E-06 3 ABCB10 3.02E-04 2 ERCC6;PGBD3 1.53E-02 21.4 BAK1 6.55E-07 3 FKBP1A 5.09E-04 2 TP53BP2 3.37E-08 3 ERCC2 4.33E-02 2 ABCC1 6.04E-03 3 XRCC6 3.04E-02 2 ATOX1 8.52E-05 3 APEX1 3.75E-02 2 POLH 4.75E-02 1.4 HCC Gene Expression Pattern Suggests New Treatment Strategy 265
Two of the normal (N1, N2) and seven of the HCC samples (A1, A5, t-statistic, with the P values adjusted for multiple comparisons using A8, A9, A11, B16, B18) were analyzed in duplicate. For these the Benjamini-Hochberg method. nine samples, the Pearson correlation between the duplicates was Validation of the 45 MDR-Linked Genes as a Prognostic greater than 99%. These duplicates were averaged together. One of Signature for Poor Overall Survival. The clinical data of this the genes (18S) was present as multiple probes. The expression data cohort and the analysis of the gene expression profile were published from the multiple probes for that gene were averaged together. Genes by Andersen et al. (2010). Analysis of survival data were performed by that were expressed in 10 or fewer samples were removed from the Kaplan-Meier using Mantal-Cox (log-rank) statistics (GraphPad. analysis. Prism v5; GraphPad Software, Inc., La Jolla, CA). Comparison of HCC and Normal Liver Samples. Genes Connectivity Map Analysis. The upregulated and downregu- expressed differentially in HCC and normal samples were detected lated genes found in 38 HCC samples that had an adjusted P , 0.05 in using the t statistic. The obtained P values were adjusted for multiple the TLDA data were used as input to the Connectivity Map online comparisons using the Benjamini-Hochberg method (Benjamini and web tool (http://www.broadinstitute.org/science/projects/connectivity- Hochberg, 1995). map/connectivity-map). Comparison of Samples from Groups A and B. Unsupervised Validation of the Compounds Highlighted by Using the clustering (average linkage algorithm with 1-Pearson correlation as Connectivity Map. All 20 cell lines were grown to 70%–80% the distance) was done on the normalized, filtered data. Genes confluence. Twenty-four hours before RNA extraction using an differentially expressed in groups A and B were detected using the RNeasy Micro kit (Qiagen, Valencia, CA), all cell lines were grown Downloaded from molpharm.aspetjournals.org at ASPET Journals on September 25, 2021
Fig. 1. Identification of MDR-linked gene signature. (A) Unsupervised hierarchical clustering of gene expression data from clinical samples of HCC stratified into two main groups, with few exceptions (seven good overall survival samples clustered with poor overall survival samples, i.e., B3, 4, 5, 7, 11, 12, and 19). Red and green cells reflect high and low expression levels, respectively. (B) Kaplan-Meier survival curve. The panel shows overall survival of patients with HCC using the 45 MDR- linked genes (P , 0.02). 266 Gillet et al. in Dulbecco’s modified Eagle’s medium/F12 (Invitrogen, Carlsbad, (1:1,000, catalog no. 06-599; Millipore, Billerica, MA), rabbit anti- CA). The RNA was prepared and profiled as previously mentioned GAPDH (1:5000; catalog no. 14C10; Cell Signaling, Danvers, MA). in the section entitled Validation of the 45 MDR-linked Genes as Horseradish peroxidase–linked secondary antibodies (1:10,000) were a Prognostic Signature for Poor Overall Survival. Integration from DakoCytomation (Carpinteria, CA). Bands were visualized by was performed by z-transforming each data set separately, and chemiluminescence using X-ray film. the hierarchical clustering was performed using Cluster 3.0 and Silencing the Histone Acetyltranserase GCN5. A pGIPZ TreeView1.6, software developed by Michael Eisen, Stanford Univer- shRNA construct for stable knockdown of human GCN5 (KAT2A sity, Stanford, CA. gene) was obtained from Open Biosystems (Huntsville, AL)-Thermo Cell Lines. HUH7, PLC, and HEP3B, which clustered with Scientific (catalog no. RHS4430-99293180; Somerset, NJ). A non- samples from patients with poor overall survival, and HUH1, silencing pGIPZ shRNA (catalog no. RHS4346) was used as a control. SNU182, and FOCUS clustering with samples from patients with Lentiviral particles were made via Lipofectamine 2000 (Invitrogen)– better overall survival were maintained in RPMI-1640 (Life Technol- mediated triple transfection of 293T cells with pGIPZ shRNA ogies, Invitrogen, Grand Island, NY), supplemented with 10% fetal plasmids along with the lentiviral envelope plasmid (pMD2.G, bovine serum, 100 units of penicillin/streptomycin/ml at 37°C in 5% Addgene plasmid 12259) and the lentiviral packaging plasmid
CO2 humidified air. (psPAX2, Addgene plasmid 12260). Liver cancer cells were transduced Cytotoxicity Assay to Determine Synergism of Added with either nonsilencing or GCN5-specific shRNA containing lentivi- Drugs. To assess the synergistic effects of sorafenib, 6TG, 8-Aza, ral particles in the presence of 8 mg/ml polybrene and stable cells were
doxorubicin, apigenin, cisplatin, and 5-FU in combination, Huh7, selected using 3 mg/ml puromycin for 1 week and pooled before Downloaded from Hep3B, and PLC cells were each treated with a matrix of two different determining knockdown efficiency. Knockdown efficiency was de- drugs with serial 1:2 dilutions from 100–0.001526 mM. Five thousand termined via Western blot analysis using a GCN5-specific antibody cells were seeded per well 16 hours before the addition of the drug (no. 3305) from Cell Signaling Technology. combinations. An assay using the reagent (3-(4,5-dimethylthiazol-2- yl)-2,5-diphenyl tetrazolium bromide) (MTT) was performed according Results to the manufacturer’s (Trevigen, Gaithersburg, MD) protocol to mea- sure the proliferation of cells at 72 hours after the addition of drug. Genes Differentially Expressed in HCC Compared molpharm.aspetjournals.org To evaluate sensitization effects, 20 mM of one drug was added for with Normal Liver Samples. We conducted a study on 17 24 hours and removed. The second drug was added in a serial 1:2 dilu- normal liver samples and 38 HCC samples (Lee et al., 2004, – m tion from 100 0.3906 M, and proliferation was measured at 72 hours 2006) to compare the expression profile of 377 MDR-linked using the MTT assay. Three technical replicates were performed for genes in normal and HCC samples. These genes, selected from the sensitization experiments. All additions of cells to 96-well plates, drug dilutions/additions, and the MTT assay were carried out using a the literature published over the last 30 years, were reported Hamilton Star Liquid Handler (Reno, Nevada). Synergy calculations to have a role in MDR, based primarily on in vitro studies were done using custom scripts on R (version 2.15.2). (Calcagno et al., 2010; Gillet et al., 2011). Our analysis Western Blot Immunoassay. The following antibodies were revealed 103 genes that are differentially expressed in HCC
used for a Western blot immunoassay: rabbit anti-acetyl histone H3 compared with normal liver samples. Eighty-two genes have a at ASPET Journals on September 25, 2021
TABLE 2 Differentially expressed genes in HCC samples of subtype A (poor survival) compared with subtype B (good survival)
Upregulated Adjusted Downregulated Adjusted Genes P Value P Value Ratios Genes P value P Value Ratios SLC29A2 2.85E-08 1.04E-05 13.1 SLC10A1 1.91E-07 6.94E-05 224.7 SLC16A3 2.71E-05 0.0091 5.2 SLC28A1 2.65E-07 9.63E-05 221.8 TOP2A 2.33E-06 0.0008 5.1 SLC22A1 2.98E-06 0.0010 219.5 MKI67 3.76E-06 0.0013 5.0 ABCB11 1.41E-06 0.0005 219.4 CHEK1 8.23E-07 0.0003 4.8 CYP2C19; CYP2C8 8.49E-07 0.0003 218.5 MMP9 7.54E-05 0.0249 4.7 CYP2A6; CYP2A7; 1.02E-05 0.0035 217.7 CYP2A13 XRCC2 3.97E-06 0.0014 4.5 AQP9 1.22E-05 0.0042 213.3 BIRC5 3.17E-06 0.0011 4.1 NR1I2 4.12E-07 0.0001 212.0 RAD51 1.20E-05 0.0041 3.6 CYP2C9 4.00E-06 0.0014 211.7 SLC1A5 7.00E-05 0.0233 3.5 NR1I3 8.10E-05 0.0267 211.3 BRCA2 1.62E-05 0.0055 3.0 CYP2C8 2.23E-06 0.0008 211.3 SRC 2.59E-05 0.0087 3.0 ABCG8 0.0001 0.0410 27.6 BRCA1 1.16E-06 0.0004 2.8 ABCB4 5.85E-08 2.14E-05 27.3 CDK4 1.69E-06 0.0006 2.5 ABCA6 0.0001 0.0388 25.9 AURKA 0.0002 0.0494 2.3 ABCC9 8.07E-05 0.0266 23.2 MSH2 8.62E-05 0.0282 2.2 HAGH 1.53E-06 0.0005 22.6 MRE11A 1.43E-05 0.0049 2.1 CCT8 4.29E-05 0.0144 1.9 APAF1 8.82E-06 0.0031 1.8 ATM 5.73E-05 0.0192 1.7 CDK7 8.17E-05 0.0268 1.7 CASP3 7.27E-05 0.0241 1.7 XRCC5 4.18E-07 0.0002 1.7 XRCC1 0.0001 0.0374 1.7 ABCE1 9.19E-05 0.0300 1.6 TOP1 2.50E-06 0.0009 1.6 GART 2.08E-05 0.0071 1.6 ABL1 6.56E-05 0.0219 1.5 DIABLO 1.13E-05 0.0039 1.5 HCC Gene Expression Pattern Suggests New Treatment Strategy 267 false discovery rate (FDR) , 0.05 and P value , 0.01, and 21 gene expression of the 38 HCC samples, which were pre- additional genes fulfill the less stringent criteria of FDR , 0.05 viously classified into two groups based on overall survival and P value , 0.05 (Table 1). More precisely, 32 genes were (Lee et al., 2004). Group A consisted of 20 samples taken from found to be downregulated in HCC compared with normal Chinese patients with a poor overall survival; group B samples, whereas 71 were found to be upregulated (Table 1). contained eleven samples taken from Chinese patients and Notably, eight ABC transporters were overexpressed in HCC. seven samples from Belgian patients. All group B patients Many of these drug efflux transporters are important mediators demonstrated better overall survival. of MDR (Gillet et al., 2007). Within this group of eight genes, the Unsupervised clustering of all the genes expressed in more involvement of ABCC1, ABCC4, ABCC5, and ABCC10 in MDR than 10 of the 38 samples yields distinct clusters for the HCC has been well characterized (Gillet et al., 2007). Our analysis group A subtype (poor overall survival), the HCC group B also highlighted a cluster of genes involved in cell-cycle regu- subtype (good overall survival), and the normal samples, with lation, including CDKN2A, CDK2 and 4, CCD42, and CCNE1. few exceptions (Fig. 1A). Supervised class comparisons high- Moreover, the cell-cycle checkpoints CHEK 1 and 2 and RAD1 lighted 45 genes that are differentially expressed in groups A were found to be overexpressed, as well as a large cluster of and B (FDR , 0.05), of which 29 were found to be upregulated DNA repair genes, including TOP2A, overexpressed 67-fold. in patients with poor overall survival compared with patients Four main groups of downregulated genes were uncovered, with good overall survival, whereas 16 were downregulated including several ABC transporters, CYP450s, metallothioneins, (Table 2). Perhaps the most striking finding is the upregulation Downloaded from and solute carriers. None of the downregulated ABC trans- of 12 genes related to DNA repair, including cell-cycle check- porters were involved in MDR except ABCC11, whereas points CHEK1 and ATM; the regulators BRCA1 and 2; the several downregulated SLCs have been previously identified double-strand break repair genes MRE11A, TOP2, RAD51, as drug transporters. They include SLC21A8/SLCO1B3, XRCC1, 2, and 5; and the single-strand DNA repair gene TOP1. SLC22A1/OCT1, SLC28A1/CNT1, and the copper transporter Three gene families were found to be downregulated. They SLC31A1/CTR1, which also transports cisplatin, oxaliplatin, include five ABC transporter genes (ABCA6, B4, B11, C9, and molpharm.aspetjournals.org and carboplatin (Huang, 2007; Kuo et al., 2007). G8), four cytochrome P450s (CYP2A6, 2C8, 2C9, and 2C19) and Identification of an MDR-Linked Gene Signature in a three SLCs (SLC10A1, 22A1, and 28A1), which could be Previously Established Group of Poor Overall Survival attributed to the concomitant downregulation of the nuclear Patients. We characterized the differential MDR-linked receptor genes NR1I2 and NR1I3 (di Masi et al., 2009). at ASPET Journals on September 25, 2021
Fig. 2. Unsupervised hierarchical clustering based on expression data of 45 MDR-linked genes from 53 clinical samples of HCC and 20 HCC cell lines. (A) Dendrogram presenting the clustering of the 20 HCC lines into two groups according to patient survival. (B) Dendrogram showing the reversal of the 45-gene expression profile of two HCC cell lines (HUH7 and PLC) clustered with samples from patients with poor overall survival to a gene expression profile found in patients with good prognosis after a 72-hour drug treatment (HUH7 6TG, HUH7 8-AZA, HUH7 API and PLC 6TG, PLC 8-AZA, PLC API). HUH7 cells were treated with 6.25 mM 6-TG, 1.56 mM 8-AZ, and 15 mM apigenin. PLC cells were treated with 12.5 mM 6-TG, 50 mM 8-AZ, and 25 mM apigenin. Each drug was tested individually. 268 Gillet et al. Downloaded from molpharm.aspetjournals.org at ASPET Journals on September 25, 2021
Fig. 3. Western blot analysis of histone H3 acetylation. (A) Analysis using initial drug-treatment regimen in three HCC cell lines. The untreated cell lines were compared with their treated counterparts (10 mM, 6 hours). Depsipeptide (DP) and trichostatin (TA) were used as positive controls (5 and 10 nM). (B) Analysis using optimized drug-treatment regimen in the same three HCC cell lines. Optimization of the treatment conditions increased the effect observed (20 mM, 24 hours). The untreated cell lines were compared with their treated counterparts. DP was used as positive control (5 nM). Histone acetyltransferase, GCN5, is involved in chemosensitizing drug-induced histone H3 acetylation. (C) Protein lysates from the HCC cell lines HUH7, PLC, and HEP3B, stably expressing either control or GCN5-specific shRNA, were resolved by SDS-PAGE and blotted for GCN5, b-actin, and acetylated histone H3 antibodies. Percent knockdown of GCN5 and fold changes in acetylated histone H3 levels between control and GCN5 knockdown cells, for all three cell lines, are indicated. Control and GCN5-specific shRNA expressing HUH7 (D), PLC (E), and HEP3B (F) cell lines were treated with either vehicle control HCC Gene Expression Pattern Suggests New Treatment Strategy 269
Validation of the 45 MDR-Linked Genes as a Prog- To explore further the hypothesis that changes in gene nostic Signature for Poor Overall Survival. We assessed expression patterns were due to increased histone H3 acety- the predictive power of the 45-gene signature identified as lation, we knocked down the expression of the major histone differentially expressed in A versus B subtypes on an in- H3 acetyltransferase GCN5 (Fig. 4, C–F). As hypothesized, dependent cohort of 53 HCCs obtained from white and drug treatment-induced histone acetylation decreased in the Chinese patients (Andersen et al., 2010). The gene expression HUH7 and PLC cell lines when GCN5 was knocked down and profiling was performed using Illumina bead chips (Andersen to some extent in the HEP3B cell line, when treated with et al., 2010). Figure 1B shows that the 45-gene signature effec- apigenin. tively predicts overall survival of patients with HCC (P , 0.02), Drug Combinations Show Synergistic Cytotoxicity. validating the clinical relevance of the gene signature. For each pair of drugs tested, we used combinations of varying Identification of Compounds that Sensitize Chemo- concentrations to determine whether the combination of these resistant HCC. The next step in this study was to pinpoint drugs resulted in increased cytotoxicity and whether this drugs that might efficiently alter the poor prognosis gene effect was additive or synergistic. The combination of three signature in HCC. For this, we used the Connectivity Map tool drug pairs had a significant synergistic effect on all three HCC published by Lamb and colleagues (Lamb et al., 2006), designed cell lines: 6-TG/apigenin, doxorubicin/apigenin, and sorafenib/ to reveal connections among drugs, genes, and pathologic apigenin, and to some extent, 6-TG/5-FU (Fig. 4), indicating states. The Connectivity Map algorithm (Lamb et al., 2006) that the changes we observed in gene expression patterns Downloaded from compares the direction of gene expression change from one were also associated with increases in treatment efficacy over disease state to another with the change due to a drug and above the toxicity of the drugs themselves. treatment. Drugs that cause an expression change similar to the change between poor-prognosis and better-prognosis tumors may be able to change the outcome in those with HCC,
Discussion molpharm.aspetjournals.org possibly by causing the resistant cells to become more drug- sensitive or by changing the physiology of the tumor. Using a TaqMan-based quantitative reverse transcription- We were interested in drugs that cause a change in gene polymerase chain reaction array, we studied the expression expression that matches the gene expression change from profile of 377 MDR-linked genes and found a signature of 103 group A to group B. From the upregulated and downregulated genes differentially expressed in normal liver cells and HCC. genes obtained by TLDA that had an adjusted P value , 0.05, The MDR genotype consists of the upregulation of several we found four drugs with high positive concordance, low members of the ABCC family (known as MRPs), of genes P value (P , 0.001), and a low specificity score (specificity involved in cell proliferation through regulation of the G1/S score , 0.05). These drugs were 8-azaguanine, 6-thioguanosine, cell-cycle transition, and of DNA repair genes. We also apigenin, and 0175029-0000, a pyrimidine derivative (2-[4-(2- identified downregulation of several solute carriers involved at ASPET Journals on September 25, 2021 diethylaminoethyloxy)anilino]-8-phenyl-pyrido[2,3-day] in platinum drug uptake, potentially resulting in a dramatic pyrimidin-7-one) (Supplementary Table S1). decrease in the cellular entry of this drug (Huang, 2007; Kuo To confirm our findings regarding these drugs, we per- et al., 2007). The intrinsic expression of several additional formed an integrative clustering using the 45-gene signature ABC transporters known to efflux standard chemotherapeu- in 20 HCC cell lines and the 53 HCC clinical samples of our tics, including ABCB1, ABCB4, ABCB11, and ABCG2, leaves validation set (Fig. 2A). Two HCC cell lines randomly selected, limited treatment modalities to clinicians when coupled with HUH7 and PLC, which clustered with samples from patients the MDR-linked gene signature of HCC. Many of these ABC with poor overall survival, were treated for 72 hours with a transporters have been shown to transport doxorubicin subcytotoxic dose of each of the drugs individually, except for (Szakacs et al., 2006). It should be noted that this poor-prognosis compound 0175029-0000, which is unavailable. The data MDR gene signature probably reflects a biologic state of the indicate that the treatment caused a change in the gene HCC rather than being the sole cause of the poor prognosis, expression profile of the cell lines from that of poor overall since the patients who were the source of the analyzed HCC survival to that of better overall survival (Fig. 2B). samples were not treated with chemotherapeutic agents; 6-TG, 8-AZG, and Apigenin Mediate Increased Acet- however, the presence of these drug-resistance mechanisms ylation of Histone Protein. We next hypothesized that the in poor-prognosis HCC makes it difficult to design chemother- mechanism underlying the ability of these three compounds to apy that might be effective against these cancers. On the other change gene expression patterns might be associated with hand, this 103-gene signature not only confirms the expres- increased acetylation of histone protein. This was confirmed sion of known markers of HCC such as TOP2A (Wong et al., using an antibody directed against acetylated histone H3 in 2009), which is a target for topoisomerase inhibitors, but also three HCC cell lines (HUH7, PLC, and HEP3B) treated for highlights new markers including the solute carriers SLC2A5/ 6 hours with 10 mM of any one of these drugs (Fig. 3A). GLUT5, SLC16A3/MCT, SLC7A11, and the melphalan trans- Optimization of the treatment (24 hours with 20 mM) dramat- porter SLC7A5/LAT1 (del Amo et al., 2008). It is possible that ically increased the effect observed, with a 3- to 6-fold increase these uptake transporters might facilitate cellular entry of in acetylated histone H3 and as much as a 13.5-fold increase certain yet unidentified drug species, therefore facilitating when treated with depsipeptide as a positive control (Fig. 3B). therapy.
or 20 mM of either 6-thioguanine, apigenin, or 8-azaguanine for 24 hours. Protein lysates were resolved by SDS-PAGE and blotted for GCN5, b-actin, and acetylated histone H3 antibodies. Fold changes in acetylated histone H3 levels of drug-treated cells compared with vehicle control-treated for both control and GCN5 knockdown cells are indicated. 270 Gillet et al. Downloaded from molpharm.aspetjournals.org at ASPET Journals on September 25, 2021
Fig. 4. Assessment of the synergistic effect of drug combinations. Presented are the two-dimensional (2D) plots of synergy for one drug pair that showed significant synergy for all three cell lines, HUH7, PLC, and HEP3B. The 2D plots show the difference between the measured values and additive null surface (Loewe and Muischnek, 1926). Blue and black boxes are regions of synergy, whereas pink shows antagonism. Green and yellow areas are close to the null surface (neither synergy nor antagonism). White boxes indicate conditions in which there is 100% inhibition for the drug concentration tested. HCC Gene Expression Pattern Suggests New Treatment Strategy 271
The HCC samples analyzed comprised two groups defined Acknowledgments by overall survival rate. Since previous studies indicated the The authors thank George Leiman for editorial assistance and superiority of TLDAs over high-density microarrays (con- Allison C. Meade in the Protein Expression Laboratory (SAIC- firmed in this work), we used this technique to investigate Frederick, Inc.) for assistance in liquid handling. the differences in gene expression profiles for patients with good and poor overall survival (Gillet and Gottesman, 2011). Authorship Contributions Interestingly, our analysis revealed a novel 45-gene signature Participated in research design: Gillet, Gottesman, Ambudkar, that was shown to predict overall survival. In addition to well- Bagni, Madigan, Thorgeirsson. established markers, we demonstrated 13-fold and 5-fold Conducted experiments: Gillet, Andersen, Madigan, Bagni, Powell, overexpression of SLC29A2 and SLC16A3/MCT, respectively, Burgan, Wu, Calcagno. in the poor overall survival group. SLC29A2, a nucleoside Performed data analysis: Gillet, Varma, Andersen, Wu. uptake transporter, mediates the transport of gemcitabine, Wrote or contributed to the writing of the manuscript: Gillet, cladribine, and zidovudine (Baldwin et al., 2004; Huang and Gottesman, Ambudkar, Andersen. Sadee, 2006). Huang and colleagues correlated SLC gene expression profiles in the NCI-60 cancer cell line panel with References the potencies of 119 standard anticancer drugs and identified Andersen JB, Loi R, Perra A, Factor VM, Ledda-Columbano GM, Columbano A,
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prevention: translating knowledge into practice. Clin Gastroenterol Hepatol 13: Address correspondence to: Dr. Michael Gottesman, Laboratory of Cell Downloaded from 2140–2151. Biology, 37 Convent Dr., Room 2108, Bethesda, MD 20892. E-mail: mgottes- Su YH, Lin SY, Song W, and Jain S (2014) DNA markers in molecular diagnostics for [email protected] hepatocellular carcinoma. Expert Rev Mol Diagn 14:803–817. molpharm.aspetjournals.org at ASPET Journals on September 25, 2021 Supplementary Material
A Gene Expression Signature Associated With Overall Survival in
Patients With Hepatocellular Carcinoma Suggests a New Treatment
Strategy
Jean-Pierre Gillet, Jesper B. Andersen, James P. Madigan, Sudhir Varma, Rachel K.
Bagni, Katie Powell, William E. Burgan, Chung-Pu Wu, Anna Maria Calcagno, Suresh V.
Ambudkar, Snorri S. Thorgeirsson, Michael M. Gottesman
Journal: Molecular Pharmacology Table S1: Compounds highlighted through the Connectivity Map tool
Rank for Name of drug on Connectivity Map score - Number of Connectivity Map Probability of getting Proportion of other drugs Drug signature is drug, rated Connectivity Map similarity between the experiments used score normalized an enrichment this that share this same significant for what according to gene expression signature to compute mean using scores from high if there is no signature (smaller values percent of the "n" p-value of drug and the signature similarity random selections of connection between mean that the signature is experiments? used as input genes drug signature and very specific to this input signature particular drug)
Rank C-map name Mean n Enrichment p Specificity Percent non-null
1 8-azaguanine 0.916 4 0.975 0 0 100 2 adiphenine -0.765 5 -0.909 0 0.0242 100 3 trichostatin A 0.485 182 0.306 0 0.7204 73 4 tanespimycin 0.474 62 0.297 0 0.3834 72 5 apigenin 0.851 4 0.937 0.00002 0.0234 100 6 0175029-0000 0.797 6 0.86 0.00002 0.0177 100 7 thiamphenicol -0.711 5 -0.903 0.00004 0 100 8 thioguanosine 0.821 4 0.902 0.0001 0.0177 100 9 viomycin -0.723 4 -0.9 0.00016 0.0144 100 10 GW-8510 0.846 4 0.886 0.00016 0.0939 100 11 heptaminol -0.676 5 -0.802 0.00068 0.0137 100 12 biperiden -0.644 5 -0.783 0.00086 0.0408 100 13 Prestwick-692 -0.719 4 -0.852 0.00092 0.0068 100 14 trifluoperazine 0.542 16 0.466 0.00094 0.274 75 15 gentamicin -0.621 4 -0.846 0.00101 0.0263 100 16 thiostrepton 0.753 4 0.835 0.00115 0.0539 100 17 phenoxybenzamine 0.8 4 0.829 0.00127 0.2525 100 18 fludrocortisone -0.319 8 -0.634 0.00136 0.0704 62 19 skimmianine 0.76 4 0.826 0.00143 0.0082 100 20 felbinac -0.695 4 -0.831 0.00147 0.0355 100 21 lisuride -0.528 5 -0.756 0.0016 0.0492 100 22 trimethobenzamide -0.536 5 -0.751 0.00176 0.0201 100 23 vorinostat 0.539 12 0.513 0.00198 0.4422 75 24 nadolol -0.57 4 -0.812 0.00233 0.0615 100 25 Gly-His-Lys -0.677 3 -0.892 0.0024 0.0149 100 26 daunorubicin 0.733 4 0.792 0.00366 0.0758 100 27 isoflupredone -0.713 3 -0.876 0.00385 0.1083 100 28 medrysone 0.763 6 0.667 0.00385 0.0787 100 29 3-acetamidocoumarin -0.679 4 -0.787 0.00412 0.0844 100 30 isoxicam -0.55 5 -0.711 0.00429 0.0811 80 31 luteolin 0.785 4 0.78 0.00442 0.073 100 32 Prestwick-983 -0.611 3 -0.869 0.00445 0.0206 100 33 tyloxapol 0.755 4 0.779 0.0045 0.0182 100 34 camptothecin 0.742 3 0.865 0.00463 0.2098 100 35 phthalylsulfathiazole 0.771 5 0.711 0.00473 0.0354 100 36 vincamine -0.373 6 -0.653 0.00475 0.0529 66 37 trazodone 0.749 3 0.863 0.00481 0.0373 100 38 meticrane 0.789 5 0.709 0.00495 0.028 100 39 chloropyrazine -0.582 4 -0.779 0.00497 0.0195 100 40 Prestwick-1082 -0.652 3 -0.862 0.00535 0.082 100 41 gabexate -0.589 4 -0.77 0.00567 0.052 100 42 podophyllotoxin -0.658 4 -0.768 0.00577 0.0686 100 43 chenodeoxycholic acid -0.552 4 -0.766 0.00603 0.0615 100 44 PHA-00745360 -0.394 8 -0.561 0.00654 0.1395 75 45 colistin -0.553 4 -0.762 0.00658 0.0231 100 46 atractyloside -0.53 5 -0.686 0.00703 0.0303 80 47 finasteride -0.35 6 -0.634 0.00709 0.1402 66 48 chrysin 0.775 3 0.844 0.00745 0.0351 100 49 chlorhexidine -0.306 5 -0.682 0.00747 0.015 60 50 Prestwick-1103 -0.627 4 -0.752 0.00762 0.0397 100 51 quinpirole -0.554 4 -0.752 0.00762 0.0148 100 52 norcyclobenzaprine 0.73 4 0.749 0.00762 0.0546 100 53 Prestwick-691 -0.683 3 -0.842 0.00783 0.0592 100 54 canadine -0.575 4 -0.747 0.00812 0.0604 100 55 thioperamide -0.457 5 -0.677 0.00815 0.0672 80 56 isometheptene -0.485 4 -0.747 0.00818 0.0414 100 57 monensin -0.289 6 -0.627 0.00842 0.1545 50 58 0297417-0002B 0.747 3 0.838 0.00843 0.0947 100 59 Prestwick-1084 0.734 4 0.74 0.00875 0.0103 100 60 merbromin -0.479 5 -0.671 0.00889 0.0732 80 61 etiocholanolone -0.52 6 -0.623 0.00918 0.1688 83 62 penbutolol -0.664 3 -0.833 0.00933 0.0074 100 63 timolol -0.432 4 -0.737 0.00953 0.0448 75 64 CP-320650-01 -0.331 8 -0.541 0.01019 0.1223 75 65 levomepromazine -0.428 4 -0.732 0.01038 0.0094 75 66 bufexamac 0.694 4 0.731 0.0104 0.0211 100 67 sulfadimethoxine -0.536 5 -0.662 0.01043 0.1407 80 68 furazolidone -0.403 4 -0.732 0.01044 0.0191 75 69 procaine 0.654 5 0.667 0.01077 0.0522 100 70 aciclovir -0.523 6 -0.612 0.01104 0.0729 83 71 diphenhydramine -0.603 5 -0.658 0.01137 0.0963 80 72 mebhydrolin -0.537 4 -0.726 0.01158 0.0168 75 73 pindolol -0.441 5 -0.657 0.01158 0.0206 80 74 pargyline 0.666 4 0.72 0.01237 0.0472 100 75 pheneticillin -0.457 4 -0.721 0.01239 0.0327 75 76 alprostadil -0.429 7 -0.563 0.01247 0.04 71 77 Prestwick-642 -0.462 4 -0.718 0.01271 0.0552 75 78 ajmaline -0.649 3 -0.814 0.01278 0.0355 100 79 cycloserine 0.678 4 0.718 0.01297 0.0385 100 80 repaglinide 0.674 4 0.717 0.01307 0.0741 100 81 dicycloverine -0.32 5 -0.645 0.01402 0.0382 60 82 LY-294002 0.375 61 0.198 0.0146 0.651 65 83 chlorpromazine 0.495 19 0.351 0.01462 0.0705 84 84 H-7 0.651 4 0.704 0.01597 0.25 100 85 prochlorperazine 0.511 16 0.374 0.01604 0.4029 81 86 carteolol -0.342 4 -0.703 0.01605 0.0296 50 87 dapsone -0.41 5 -0.635 0.01668 0.084 80 88 resveratrol 0.619 9 0.487 0.01689 0.3382 100 89 diethylstilbestrol -0.307 6 -0.588 0.01706 0.082 83 90 nitrofurantoin 0.666 5 0.641 0.01718 0.0467 100 91 tranexamic acid -0.484 5 -0.632 0.01738 0.2282 80 92 guanabenz -0.528 5 -0.632 0.01748 0.1308 80 93 methyldopate -0.347 4 -0.698 0.01751 0.0438 75 94 midodrine -0.389 5 -0.631 0.01762 0.1579 60 95 ifenprodil 0.689 4 0.696 0.01774 0.0435 100 96 tetracycline -0.401 5 -0.627 0.01874 0.0566 60 97 omeprazole 0.73 4 0.692 0.01884 0.1009 100 98 vinblastine -0.616 3 -0.79 0.01893 0.1145 100 99 bisacodyl 0.67 4 0.691 0.01898 0.0851 100 100 isoniazid -0.414 5 -0.624 0.01949 0.0994 80 101 lasalocid -0.437 4 -0.691 0.01959 0.1296 75 102 iohexol -0.495 4 -0.688 0.02037 0.0511 75 103 cetirizine 0.717 4 0.685 0.02093 0.0571 100 104 levobunolol -0.443 4 -0.685 0.02137 0.1154 75 105 naringenin -0.392 4 -0.684 0.02172 0.0968 50 106 sulfamonomethoxine -0.42 4 -0.679 0.02357 0.0861 50 107 hexetidine -0.453 4 -0.677 0.02401 0.0769 75 108 imipenem 0.667 4 0.676 0.02409 0.0153 100 109 pyrithyldione -0.6 4 -0.677 0.02425 0.0775 75 110 bepridil 0.689 4 0.675 0.02441 0.1722 100 111 iproniazid -0.299 5 -0.611 0.02473 0.0602 60 112 calcium folinate -0.527 5 -0.611 0.02489 0.1221 80 113 streptozocin -0.389 4 -0.674 0.02524 0.0556 75 114 ethotoin 0.661 6 0.564 0.02525 0.0661 100 115 PF-00539745-00 -0.517 3 -0.769 0.02538 0.0889 100 116 iopamidol -0.48 4 -0.673 0.02572 0.0588 75 117 antimycin A 0.69 5 0.613 0.02577 0.1011 100 118 ellipticine 0.64 4 0.669 0.02674 0.2265 75 119 propafenone 0.698 4 0.668 0.02688 0.06 100 120 iocetamic acid -0.344 4 -0.668 0.02741 0 75 121 alpha-estradiol 0.466 16 0.353 0.02747 0.2558 81 122 DL-thiorphan 0.799 2 0.883 0.02789 0.1029 100 123 dipyridamole 0.663 6 0.558 0.02827 0.0872 100 124 methazolamide 0.671 4 0.664 0.02865 0.0283 100 125 harmine 0.632 4 0.661 0.03032 0.084 75 126 trihexyphenidyl -0.427 3 -0.754 0.03047 0.0902 100 127 sulfametoxydiazine 0.714 4 0.66 0.03077 0.0849 100 128 clemizole 0.682 5 0.6 0.03104 0.0788 100 129 naproxen -0.218 9 -0.456 0.03155 0.0915 55 130 clorsulon -0.533 4 -0.657 0.03173 0.078 75 131 Prestwick-665 0.675 5 0.599 0.03192 0.0488 100 132 meteneprost -0.4 4 -0.654 0.03338 0.1797 75 133 halcinonide 0.607 5 0.595 0.03362 0.0584 100 134 triflupromazine -0.372 4 -0.653 0.03364 0.0172 75 135 dequalinium chloride 0.697 4 0.653 0.03374 0.0797 100 136 chlortalidone -0.398 4 -0.651 0.03481 0.0651 75 137 sulconazole 0.716 4 0.65 0.03553 0.1438 100 138 ceforanide -0.41 4 -0.649 0.03567 0.0659 75 139 6-azathymine 0.716 4 0.647 0.03724 0.0507 100 140 nifenazone -0.318 5 -0.581 0.03831 0.0395 80 141 glafenine 0.63 4 0.645 0.03841 0.0709 100 142 ketotifen -0.407 4 -0.643 0.03863 0.1148 75 143 trioxysalen 0.736 4 0.642 0.04018 0.1044 100 144 oxybenzone -0.366 4 -0.64 0.04024 0.1127 75 145 alsterpaullone 0.778 3 0.726 0.04046 0.2791 100 146 scriptaid 0.691 3 0.725 0.04054 0.2685 100 147 homatropine -0.246 5 -0.577 0.04095 0.0633 60 148 protriptyline 0.604 4 0.639 0.04132 0.0894 75 149 copper sulfate -0.21 4 -0.637 0.04176 0.0294 75 150 acepromazine 0.69 4 0.637 0.04245 0.0444 100 151 ampyrone -0.352 5 -0.572 0.0433 0.0694 80 152 withaferin A 0.646 4 0.635 0.04337 0.3211 100 153 ebselen 0.73 3 0.72 0.04351 0.196 100 154 morantel 0.727 5 0.575 0.04376 0.0686 100 155 benzocaine -0.374 4 -0.634 0.04386 0.1611 75 156 eucatropine 0.592 6 0.526 0.04483 0.1583 100 157 cloperastine 0.657 6 0.526 0.04531 0.2643 100 158 guanadrel -0.398 5 -0.567 0.046 0.1812 60 159 acetylsalicylic acid 0.439 13 0.365 0.04631 0.096 76 160 beta-escin 0.602 6 0.524 0.04674 0.1989 100 161 hexestrol 0.685 4 0.629 0.04697 0.1714 100 162 dextromethorphan 0.571 4 0.628 0.04812 0.0522 75 163 acemetacin -0.383 4 -0.626 0.04824 0.1716 75