Integrative genomic analysis identifies the core ... · 9/10/2016 · 1 Integrative genomic analysis identifies the core transcriptional hallmarks of human hepatocellular carcinoma

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Integrative genomic analysis identifies the core transcriptional

hallmarks of human hepatocellular carcinoma

Running title: Transcriptional hallmarks of liver cancer

Coralie ALLAIN1, Gaëlle ANGENARD1, Bruno CLEMENT1 and Cédric COULOUARN1

[email protected] ; [email protected]

[email protected] ; [email protected]

Author affiliations

1Inserm, UMR 991, Liver Metabolisms and Cancer, University of Rennes 1, 35033

Rennes, France

Corresponding author

Cédric COULOUARN, PhD

Inserm UMR 991, CHU Pontchaillou,

2 rue Henri Le Guilloux, F-35033 Rennes, France.

Tel : +33 2 2323 3881, Fax +33 2 9954 0137

E-mail: [email protected]

Keywords

liver / integrative genomics / cancer hallmarks / precision medicine

Research. on November 30, 2020. © 2016 American Association for Cancercancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 12, 2016; DOI: 10.1158/0008-5472.CAN-16-1559

http://cancerres.aacrjournals.org/

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Disclosure of Potential Conflicts of Interest

The authors declare no potential conflicts of interest.

Financial Support

This research was supported by Inserm and University of Rennes 1. C.C. is supported

by grants from the National Cancer Institute (Cancéropôles Ile-de-France & Grand-

Ouest), Ligue contre le cancer (CD35, 44, 49) and Association Française pour l’Etude

du Foie, France.




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Abstract

Integrative genomics helped characterize molecular heterogeneity in HCC, leading to

targeted drug candidates for specific HCC subtypes. However, no consensus was

achieved for genes and pathways commonly altered in HCC. Here we performed a

meta-analysis of 15 independent datasets (n=784 human HCC) and identified a

comprehensive signature consisting of 935 genes commonly deregulated in HCC as

compared to the surrounding non-tumor tissue. In the HCC signature, up-regulated

genes were linked to early genomic alterations in hepatocarcinogenesis, particularly

gains of 1q and 8q. The HCC signature covered well-established cancer hallmarks,

such as proliferation, metabolic reprogramming, and microenvironment remodeling,

together with specific hallmarks associated with protein turnover and epigenetics.

Subsequently, the HCC signature enabled us to assess the efficacy of signature-

relevant drug candidates, including histone deacetylase inhibitors that specifically

reduced the viability of six human HCC cell lines. Overall, this integrative genomics

approach identified cancer hallmarks recurrently altered in human HCC that may be

targeted by specific drugs. Combined therapies targeting common and subtype-specific

cancer networks may represent a relevant therapeutic strategy in liver cancer.




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Introduction

Liver carcinogenesis is a long process associated with multiple risk factors that

contribute to HCC heterogeneity, e.g. viral hepatitis, alcohol abuse, metabolic disorders

and obesity (1). Most HCC develop in the setting of chronic liver disease associated

with cycles of tissue destruction and regeneration that result in the activation of

numerous signaling pathways and the accumulation of genomic alterations. Lately,

next-generation sequencing approaches highlighted the large spectrum of mutational

processes underlying the development of HCC (2,3). Other factors such as cell plasticity

and tumor microenvironment remodeling contribute to HCC heterogeneity, as well (4).

Over the last two decades gene expression studies in HCC have revealed the

great diversity of transcriptional alterations occurring in liver carcinogenesis (5). By

integrating gene expression profiles from various sources, a consensus has been

achieved successfully and three main HCC subtypes were identified (6). However,

translating these findings into individualized treatments is still a matter of debates.

Surprisingly, the definition of recurrent transcriptional alterations in HCC has been

largely neglected, so far. Failure to define a robust common transcriptional fingerprint in

HCC may have resulted from technical variabilities and/or from the inherent HCC

molecular heterogeneity. The later even raises the question about the existence of a

substantial core expression signature in HCC (7). However, genomic characterization of

HCC mouse models demonstrated that various oncogenic pathways could generate

similar expression profiles at the tumor stage, suggesting that a common HCC signature

probably exists in humans (8). This observation prompted us to perform a meta-analysis

of publicly available human HCC datasets that were generated over a period of >10




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years. Starting from raw microarray data and by using the same analysis algorithms to

circumvent technical variabilities, our aim was to define a universal and comprehensive

transcriptional signature in HCC and to determine whether this signature could be useful

to identify clinically relevant drug candidates.

Materials and Methods

Analysis of microarray datasets

The meta-analysis was performed using gene expression datasets available from

open databases, namely Gene Expression Omnibus (GEO)

(www.ncbi.nlm.nih.gov/geo/) and ArrayExpress (www.ebi.ac.uk/arrayexpress/). Twenty-

eight liver-oriented datasets were retrieved through a systematic review of the literature

on PubMed and queries of the microarray databases using PubMed identifiers (PMID)

or keywords associated with human liver carcinogenesis and large scale gene

expression profiling, e.g. HCC, microarray and gene profiling (Supplementary Table 1).

Before performing the analysis, all microarray platforms (n=19) were re-annotated using

the Database for Annotation, Visualization and Integrated Discovery (DAVID)

(https://david.ncifcrf.gov/) (9). In total, 24,085 non-redundant annotated genes were

present at least in one out of 28 retrieved microarray datasets. Statistical analysis of

microarray data was performed using R-based BRB-ArrayTools as previously described

(10). Median-based normalization was applied and differentially expressed genes

between the tumor and surrounding non-tumor tissues (ST) were identified by a 2-

sample univariate t test (P<0.01) and a random variance model as described (10).

Permutation P values for significant genes were computed on the basis of 10,000




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random permutations. To define the core HCC gene signature only the genes up- and

down-regulated in more than 50% datasets were retained. Bias in the distribution of

chromosomal location of genes included in the core HCC signature was evaluated by

Chi-square testing using the chromosomal location of the genes present on the

integrated dataset as a background, i.e. 24,085 genes, see above. Clustering analysis

was performed using Cluster and TreeView algorithm as previously described (10).

Data mining of the core HCC gene signature

Several tools dedicated to the discovery of specific enrichments for biological

functions or canonical pathways were used, including Enrichr algorithm

(http://amp.pharm.mssm.edu/Enrichr) and ingenuity pathway analysis (IPA). IPA was

also used to examine the functional association between differentially expressed genes

and to generate the highest significant gene networks using the IPA scoring system.

Gene set enrichment analysis (GSEA) was performed by using the Java-tool developed

at the Broad Institute (Cambridge, MA, USA, www.broadinstitute.org/gsea) as

previously described (11). Connectivity map (cMap) algorithm was used to link gene

expression signatures with putative therapeutic molecules (10,12). Briefly, from the

permuted results cMap table, we focused on negative enrichments to retain only the

perturbagens (i.e. molecules) that potentially reverse the expression of genes included

in the core HCC signature. Only the perturbagens with a permuted p-value <0.003 and

a number of replicate >5 were retained.




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Cell culture

A panel of 6 liver cancer cell lines was purchased from ATCC

(www.lgcstandards-atcc.org), including SNU-475 (ATCC® CRL-2236™, grade II-IV/V),

SNU-449 (ATCC® CRL-2234™, grade II-III/IV), SNU-423 (ATCC® CRL-2238™, grade

III/IV), SNU-387 (ATCC® CRL-2237™, grade IV/V), HepG2/C3A (ATCC® CRL-

10741™) and SK-HEP-1 (ATCC® HTB-52™). ATCC performed cell lines authentication

by STR DNA profiling. The impact of selected molecules was evaluated within 6 months

after receipt. Cells were grown in a RPMI-1640 medium supplemented with 100U/ml

penicillin, 100μg/ml streptomycin and 10% fetal bovine serum. Cultures were performed

at 37°C in a 5% CO2 atmosphere. Alpha-estradiol, LY294002, rapamycin, resveratrol,

sorafenib and suberoylanilide hydroxamic Acid (SAHA, also known as vorinostat) were

purchased from Santa Cruz Biotechnology (Heidelberg, Germany). Trichostatin A was

purchased from Sigma-Aldrich (St. Louis, MO, USA). All molecules were solubilized in a

dimethyl sulfoxide (DMSO) solution. The concentration of each molecule used to treat

the cells was determined based on the detailed results table provided by cMap and a

review of the literature. Cell viability was evaluated using a PrestoBlue® cell viability

reagent (Invitrogen) after 48h and 72h of treatment with DMSO, trichostatin A (1µM),

alpha-estradiol (1µM), vorinostat (50µM), sorafenib (2µM), rapamycin (2µM), LY294002

(50µM) and resveratrol (500µM). Independent culture experiments were performed (n=4

independent biological replicates; n=4 technical replicates for each biological replicate).

The significance of differences in cell viability between experimental conditions was

determined by a two-tailed non-parametric Mann-Whitney test. For the microarray

experiments the concentrations were optimized to induce 50% cell mortality after a 72h




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drug exposure, in order to allow the extraction of nucleic acids from the remaining viable

cells. Accordingly, cells were treated with trichostatin A (0.6µM), alpha-estradiol (1µM),

vorinostat (3.7µM), sorafenib (0.65µM), rapamycin (2µM), LY294002 (45µM) and

resveratrol (166µM).

Gene expression profiling

Total RNA was purified from SNU-423 cells at 50% confluence with a

miRNAeasy kit (Qiagen, Valencia, CA, USA). Genome-wide expression profiling was

performed using the low-input QuickAmp labeling kit and human SurePrint G3 8x60K

pangenomic microarrays (Agilent Technologies, Santa Clara, CA, USA) as previously

described (10). Starting from 150ng total RNA, amplification yield was 7.0±1.3μg cRNA

and specific activity was 18.2±2.3pmol Cy3 per μg of cRNA. Gene expression data were

processed using Feature Extraction and GeneSpring softwares (Agilent Technologies).

Microarray data have been deposited in NCBI's GEO and are accessible through GEO

Series accession numbers GSE79246 and GSE85257.

Results

Generation of a compendium of gene expression profiles in human HCC

The initial step of the study was to assemble well-annotated human HCC gene

expression profiles. From the main public databases (i.e. GEO and ArrayExpress), 28

liver-oriented datasets were retrieved including 1,657 HCC gene expression profiles

from a total of 3,047 (Fig. 1A). A detailed characterization of HCC datasets is provided

in the Supplementary Table 1. Out of these 28 datasets, 15 (MDS1 to MDS15)




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included both HCC and ST tissues and thus were relevant to derive a core HCC

signature defined as a set of genes differentially expressed between HCC and ST.

Importantly, the gene expression profiles were generated from various microarray

platforms (i.e. from academic or industrial sources, including several updated contents).

Consequently, variations in both the number and the nature of interrogated gene

features between platforms greatly impede the integrative analysis of the datasets. To

overcome this issue, all the platforms (n=19, Fig. 1A) were re-annotated by using the

DAVID database (9). In total, 24,085 annotated genes were present at least in one

microarray dataset (Fig. 1A).

Identification of a comprehensive 935-gene HCC signature linked to recurrent

early genomic alterations in liver carcinogenesis

By using the same normalization, filtration and statistical algorithms a set of

genes significantly deregulated between HCC and ST tissues was identified (P<0.01;

FDR<1%; n=15 datasets; Fig. 1A). The so-called universal core HCC signature

consisted of 935 genes significantly deregulated in more than 50% HCC datasets and

included 41% up-regulated genes (Fig. 1B and Supplementary Table 2). Clustering

analysis based on the expression of these genes in 15 datasets revealed a clear

transcriptional homogeneity in the investigated tumors (n=784 HCC, Fig. 1B). Validating

the gene selection, the 935-gene HCC signature included well-known HCC biomarkers

(e.g. GPC3, PEG10) or HCC suppressor genes (e.g. DLC1) (Supplementary Table 2).

In addition, chromosomal mapping of genes included in the 935-gene HCC signature

highlighted a bias in the distribution of up- and down-regulated genes at specific




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locations known to be frequently altered in HCC (Fig. 1C and Supplementary Table 3).

Thus, up-regulated genes were significantly (P<0.001) enriched in 1q and 8q whose

amplifications were previously reported as the earliest events that occur in human

hepatocarcinogenesis (13,14). Similarly, down-regulated genes were enriched in loci

frequently subjected to early deletions in HCC (e.g. 4q). These specific locations also

correlated with the deregulation of HCC-associated genes (e.g. LAMC1 at 1q31, RAD21

at 8q24 or ALB at 4q13). Altogether, these observations demonstrated that

transcriptional changes identified in the 935-gene HCC signature significantly correlated

with early and recurrent structural genomic alterations linked to stepwise HCC

carcinogenesis in human.

The 935-gene HCC signature covers well-established cancer hallmarks

Data mining of the 935-gene HCC signature (Fig. 1D-E and Supplementary

Tables 4 and 5) identified gene networks that reflect a definite transcriptional

reprogramming associated with previously described hallmarks of cancer cells, as

exemplified thereafter (15).

Sustained proliferation and evasion to growth suppressors. Gene networks and

gene ontology analysis clearly demonstrated that active cell proliferation is the most

prominent feature within the 935-gene HCC signature (Fig. 1E). Thus, numerous cell

cycling genes were up-regulated (Supplementary Table 2), including cyclins (e.g.

CCNB1, CCNE2), cyclin dependent kinases and inhibitors (e.g. CDK1, CDK4,

CDKN2A), cell cycle and cell division checkpoints (e.g. BUB1, MAD2L1, NDC80, RAN),

together with well-established proliferation markers (e.g. PCNA, MKI67). Accordingly,




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numerous genes related to DNA replication were induced (e.g. CDC6, MCM2-4, RCF4).

These observations coincided with the induction of pro-proliferative pathways and

growth factors (e.g. ERBB3, MAPK6, YWHAH, FIBP) and the repression of negative

feed-back regulators (e.g. PINK1, DUSP1).

Genome instability, oxidative stress and apoptosis resistance. A sustained

proliferation phenotype generally associates with DNA damages, accumulation of

mutations and genome instability, which ultimately lead to apoptosis (15). Interestingly,

the 935-gene HCC signature highlighted gene deregulations that clearly sign

mechanisms of DNA damages associated with DNA hyper-replication together with

resistance to cell death (Supplementary Table 2). Thus, several genes essential for

DNA double-strand break repair (e.g. RAD21, RUVBL2) and nucleotide excision repair

(e.g. ERCC3, FEN1, OGG1) were induced. Notably, DNA glycosylase OGG1 is involved

in the excision of 8-oxoguanine, a mutagenic base byproduct which occurs as a result of

exposure to reactive oxygen species. Further implying oxidative stress in this process,

key regulators of redox homeostasis were repressed in the 935-gene HCC signature

(e.g. CTH, CBS, NFE2L2, PRDX4). Besides, several inducers of apoptosis were

repressed (e.g. CRADD, DAPK1) while genes encoding proteins that prevent apoptotic

cell death were induced (e.g. BIRC5, DAD1).

Metabolic reprogramming. Deregulation of metabolism-associated genes (e.g.

lipid, carbohydrate and amino acid metabolisms) was a prominent feature in the 935-

gene HCC signature, in agreement with metabolic changes observed in cancer cells

during tumor onset and progression (16). This was particularly noticeable for down-

regulated genes involved in liver specific metabolisms (Fig. 1D and Supplementary




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Tables 4 and 5), including those encoding acute phase plasma proteins (e.g. A2M,

ALB, CP), components of complement and coagulation cascades (e.g. C5-9, CFB, F2)

or detoxication enzymes (e.g. ADH1A, CYP2E1). While such enrichment may reflect a

loss of hepatocyte differentiation accompanying tumor development, other

deregulations affecting key enzymes of bioenergetics and biosynthesis may be directly

linked to a specific reprogramming of cellular metabolism (Supplementary Table 2). As

example, ACLY and CS were recurrently up-regulated in HCC. ACLY encodes ATP

citrate lyase, the primary enzyme for the synthesis of acetyl-CoA, a major intermediate

for biosynthetic pathways including lipogenesis. CS encodes the citrate synthase, a key

enzyme in the tricarboxylic acid cycle that contributes to lipogenesis by enhancing the

conversion of glucose to lipids. While lipogenesis was enhanced, genes encoding key

components of lipid catabolism were repressed, including genes involved in the

mitochondrial fatty acid beta-oxidation pathway (e.g. ACADM, ACADL, ECHS1). In

addition, we observed a shift toward the down-regulation of genes involved in

gluconeogenesis (e.g. PCK1, FBP1) that may contribute to enhance the rate of

glycolysis in HCC cancer cells.

Induction of angiogenesis. Fueling proliferative and metabolically active tumor

cells frequently correlates with an enhanced angiogenesis to support nutrient supply

(15). Accordingly, the 935-gene HCC signature included several angiogenesis-related

genes. As example, PLG encoding plasminogen was strongly repressed in almost all

HCC (Supplementary Table 2). Of note, plasminogen is activated by proteolysis and

converted to plasmin, an activator of matrix metalloproteases, and angiostatin, a potent

inhibitor of angiogenesis. Similarly, AMOTL2 encoding an angiomotin like 2 protein was




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repressed. Angiomotin is known to mediate the inhibitory effect of angiostatin on tube

formation. While these angiogenesis inhibitors were repressed, endothelial cell markers

were induced (e.g. ESM1).

Microenvironment remodeling and invasion. In agreement with a loss of

hepatocyte differentiation (see above), we observed a decreased expression of the

epithelial marker E-cadherin (CDH1) in >70% datasets (Supplementary Table 2). Loss

of E-cadherin is frequently observed in cancer and notably contributes to tumor

progression by increasing proliferation and invasion. The 935-gene HCC signature was

also significantly enriched in genes encoding extracellular matrix proteins (e.g. COL4A1,

COL4A2, LAMC1). Induction of these genes frequently correlates with changes in the

cellular microenvironment associated with liver fibrosis and tumor invasion, a process

largely controlled by the transforming growth factor (TGF)-β pathway. Interestingly,

SMAD2 that mediates TGFβ signals was either induced (80% datasets) or repressed,

highlighting the functional duality of the TGFβ pathway, acting as a tumor promoting or

a tumor suppressing factor in cancer, including HCC (17). SFRP1, acting as a negative

modulator of the Wnt/β-catenin pathway frequently activated in HCC microenvironment,

was repressed.

Avoiding immune destruction. Several immune-associated genes were identified

and most of them were repressed (Supplementary Table 2), including activation

markers of cytotoxic T lymphocytes and natural killer (NK) cells (e.g. CD69, KLRK1,

GZMA). In addition, immunoregulatory mediators with chemotactic activity for

competent immune cells were repressed (e.g. CCL19, CCL2/MCP1, CXCL12/SDF1).




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Emerging hallmarks enriched in HCC. In addition to the well-characterized

hallmarks of cancer cells described above, two more discrete but promising functional

categories emerged from the analysis of the 935-gene HCC signature. These specific

hallmarks were associated with protein turnover and epigenetics (Supplementary

Table 2). In agreement with an active metabolic activity, we observed an increased

expression of several translation-associated factors, including genes encoding

ribosomal subunits (e.g. RPS5, RPL38) and translational machinery (e.g. EIF4G2).

Unexpectedly, genes involved in protein ubiquitination (e.g. UBE2A, UBE2C) and

degradation through the 26S proteasome pathway (e.g. PSMA1, PSMA6, PSMD2,

PSMD4) were similarly overexpressed, evocative of a proteotoxic stress associated with

a protein hyper-production and/or mysfolding (18). The second prominent promising

HCC hallmark is the up-regulation of numerous genes acting at the epigenetic level

(Supplementary Table 2). Thus, the 935-gene HCC signature included important

regulators of chromatin assembly and remodeling (e.g. CHAF1A, HDAC1, HDAC5,

HMGB2), components of the polycomb-repressive complex 2 (e.g. EZH2, SUZ12) that

catalyzes the trimethylation of H3K27 (H3K27me3), and master regulators of microRNA

processing (e.g. DROSHA).

The 935-gene HCC signature highlights drug candidates for systemic therapies

Next, the cMap algorithm was used to identify relevant drugs in HCC, as

previously described (10). We selected drugs that generated gene expression profiles

inversely correlated with the 935-gene HCC signature with the highest confidence

(P<0.003). Six candidate molecules were identified, including two histone deacetylase




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(HDAC) inhibitors (trichostatin A and vorinostat), PI3K inhibitor LY294002, mTOR

inhibitor sirolimus (also known as rapamycin), α-estradiol and resveratrol (Fig. 2A and

Supplementary Table 6). The impact of these drugs as compared to sorafenib that is

currently used for the treatment of advanced HCC (19) was evaluated on the viability of

6 HCC-derived cell lines. Remarkably, except for α-estradiol, all the drugs significantly

(P<0.05) reduced cell viability (Fig. 2B). The SNU-423 cell line was used as a paradigm

to test the drug-induced transcriptional reprogramming based on the positive response

of this cell line to drug treatments. Thus, gene expression profiling combined with GSEA

demonstrated that these drugs could at least partly reversed the core transcriptional

programming of HCC cells, as exemplified by vorinostat, resveratrol and sorafenib (Fig.

2C). Indeed, up-regulated genes of the 935-gene HCC signature were enriched in the

gene expression profiles of DMSO treated cells (i.e. negatively enriched in drug treated

cells) while down-regulated genes were positively enriched in drug treated cells,

evidencing a drug-induced transcriptional reprogramming (Fig. 2C). These results were

further validated in SNU-387 and HepG2/C3A cell lines (Supplementary Figure 1).

Finally, data mining of genes included in the core enrichment for each drug

(Supplementary Table 7) demonstrated that the identified drugs are able to reverse the

expression of numerous genes involved in the functional networks that operate to

establish the hallmarks of HCC cancer cells.

Discussion

HCC is a deadly cancer worldwide, mainly due to late diagnosis and the absence of

effective treatments for advanced stages of the disease. A strategy of HCC stratification




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into molecular subgroups has been developed worldwide with the objective to translate

these knowledges into individualized treatments (6,20,21). Thus, Hoshida and

colleagues reported the existence of three major HCC subtypes referred to as S1, S2,

and S3 (6). These subtypes were associated with specific biological and clinical

features (Fig. 3). S1 and S2 subtypes included aggressive HCC and were associated

with an aberrant activation of the WNT signaling pathway by TGFβ (S1 subtype) or a

progenitor-like phenotype associated with MYC and AKT activation (S2 subtype). S3

subtype included good prognosis HCC that exhibited a hepatocyte-like phenotype and

CTNNB1 mutations (Fig. 3). The classification into molecular subtypes highlighted

specific pathways for drug targeting, including TGFβ, WNT and AKT inhibitors.

Conceptually, our study is slightly different as it hypotheses that optimized treatments

should take into consideration not only the molecular alterations that occur in specific

HCC subtypes but also those recurrently altered in all tumors (Fig. 3). However, the

definition of core transcriptional hallmarks in HCC has never been really explored in

details, so far. Our study fills this gap and demonstrates the existence of a substantial

transcriptional homogeneity in HCC. Hence, we provide a robust, exhaustive and

comprehensive signature of 935 genes commonly deregulated in human HCC from data

generated in various laboratories and covering all the known etiologies. We

hypothesized that analyzing multiple datasets with the same algorithms significantly

increases the accuracy of the findings as compared to conclusions raised from single

studies. Consistent with the specific objectives of Hoshida’s study (i.e. the definition of

signatures for molecular HCC subtypes) and our study (i.e. the definition of a common

transcriptional fingerprint for all HCC), there was almost no overlap between the genes




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included in the S1-3 signatures and our core HCC signature. Indeed, in the 935-gene

HCC signature, only 3%, 2% and 13% genes overlapped with the S1, S2 and S3

signatures, respectively (Supplementary Figure 2). Besides, most of genes

overlapping with the S3 signature were metabolism-associated genes that were shown

to be relatively up-regulated in S3-HCC, as compared to poorly differentiated HCC of S1

and S2 subtypes (5,6). However, regardless of HCC subtypes, these genes were

commonly repressed when compared to the surrounding non-tumor tissues, as

observed in our core HCC signature. Each of the signatures (i.e. S1-3 and core) are

then specific in term of constituting genes but could be applied to large HCC cohorts.

Altogether, we believe that our signature constitutes a unique and specific fingerprint of

recurrent and common transcriptional alterations in HCC.

One striking observation is that the 935-gene HCC signature covers almost all

hallmark capabilities of cancer cells described previously (Fig. 3) (15). It is noteworthy

that none of the genes included in the 935-gene HCC signature was directly related to

replicative immortality. This cancer hallmark is largely controlled by telomerase activity.

Actually, telomerase has been shown to be reactivated in >90% HCC, mostly due to

TERT amplification and somatic mutations or HBV insertion in the TERT promoter (3).

More than being only recurrent in HCC, genetic alterations in TERT represent early

events in human hepatocarcinogenesis (22). Importantly, we show that the 935-gene

HCC signature was associated with early chromosomal alterations described in human

hepatocarcinogenesis (14). Thus, the signature should include relevant candidate

biomarkers for early HCC diagnosis, including secreted biomarkers (e.g. SPINK1). In

addition, identifying cell surface markers (e.g. ligands and/or receptors) over-expressed




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in most HCC may lead to new drug targets with high specificity. Accordingly, innovative

nanoparticles may be formulated including specific peptides derived from the 935-gene

HCC signature, in order to increase drug delivery while reducing drug side effects.

Interestingly, the 935-gene HCC signature highlighted specific hallmarks associated

with protein turnover (i.e. synthesis and proteasomal degradation) and epigenetics (Fig.

3). Over-activation of the ubiquitin-proteasome system has been reported in several

cancers and proteasome inhibitors, e.g. bortezomib, provided promising results in

treating hematological malignancies (23). In experimental models of HCC, the

combination of sorafenib and bortezomib demonstrated synergistic anti-tumor effects

through AKT inactivation (24).

Though cMap algorithm, drug candidates were identified and validated in several

HCC cell lines, including resveratrol, HDAC inhibitors (HDACi) and PI3K/AKT/mTOR

inhibitors, in agreement with the landscape of genetic alterations frequently observed in

HCC (2). It is noteworthy that cMap results were mainly derived from MCF7 breast

cancer cells (Supplementary Table 6) that may harbor a different spectrum of

mutations than HCC cell lines. Interestingly, the comparison of genetic profiles though

the Cancer Cell Line Encyclopedia project (www.broadinstitute.org/ccle) identified a

mutation signature consisting in 16 genes mutated both in MCF7 and at least 3/6 HCC

cell lines investigated. This signature included key cancer genes involved in EMT, tumor

growth, metastasis and angiogenesis (e.g. AAK1, CDK11B, ILK, MAP3K1, MAP3K14,

NCAM1, PRKDC, and VEGFC). One can hypothesize that the efficacy of the identified

drugs may be related to these signaling pathways. Resveratrol and HDACi were shown

to target multiple cancer hallmarks in preclinical models, including effects on growth




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inhibition, cell differentiation, angiogenesis and immune surveillance (25,26). At the

molecular level resveratrol was reported to modulate cancer-related signaling pathways,

including FAS/FASL, PI3K/AKT, NFkB, and WNT (26). HDACi-induced growth arrest

has been linked to the induction of the cyclin-dependent kinase (CDK) inhibitors p21

and p27. HDACi-induced cell death involves caspase-dependent and caspase-

independent pathways. HDACi also cause hyper-acetylation and inactivation of HSP90,

leading to the degradation of proteins that require the chaperone function of HSP90,

including some oncoproteins (25). HDACi can block tumor angiogenesis by inhibiting

hypoxia inducible factors and expression of VEGF (27) and impair immune surveillance

by reducing viability and effector functions of NK cells (28). In liver cancer, we showed

that HDACi could interfere with tumor-stroma crosstalk (10) and loss of hepatocyte

differentiation (29). In HCC, the results of a multicenter phase I/II study in patients with

unresectable tumors demonstrated that HDAC inhibition with belinostat was well-

tolerated and associated with tumor stabilization (30).

However, the picture is obviously not so simple given that besides sorafenib,

most mono-therapies evaluated in phase III clinical trials failed to improve the survival of

patients with advanced HCC (31). In addition, recent results of combined therapies, e.g.

sorafenib associated with erlotinib or doxorubicin, failed to demonstrate meaningful

clinical benefits (32,33). This raises questions about the optimal backbone not only for

drug combinations (31,32) but also for combined treatment modalities, including surgery

with adjuvant multi-drug chemotherapies and biotherapies, personalized

radioembolization and immune-based therapies. Our comprehensive 935-gene HCC

signature may help to resolve this issue by identifying combinations of treatments to




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target various signaling pathways altered in HCC. We believe that evaluating these

strategies in combination with molecules targeting pathways deregulated in specific

HCC subtypes may represent promising approaches, particularly in clinical trials where

patients for each subtypes are selected based on the expression specific biomarkers.

Acknowledgements

The authors thank all the researchers who contribute to the genomic characterization of

HCC over the last two decades allowing this meta-analysis to be performed. We thank

Dr Snorri S. Thorgeirsson (NCI, Bethesda, USA) for critical reading of the manuscript.

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Figure Legends

Figure 1. Large scale meta-analysis of public genomic data identifies core

transcriptional hallmarks in human HCC. A, analytical workflow of the meta-analysis that

led to the identification of a core gene expression signature in human HCC (935-gene

HCC signature). B, heat map of the 935-gene HCC signature. Induced and repressed

genes in HCC as compared to the surrounding non-tumor tissue are represented in red

and green respectively. Missing values are colored in grey. Numbers on the top

represent the 15 HCC microarray datasets that were investigated to derive the

signature. C, specific enrichment of induced (left) and repressed (right) genes of the

935-gene HCC signature on specific chromosomal arms. D, gene ontology analysis of

induced (left) and repressed (right) genes of the 935-gene HCC signature highlighting

functional cancer hallmarks. E, top gene networks identified by Ingenuity Pathway

Analysis. Highlighted networks were associated with cell cycle and proliferation for

induced genes (left) and with cellular metabolism for repressed genes (right). DAVID,

database for annotation, visualization and integrated discovery; FDR, false discovery

rate; HCC, hepatocellular carcinoma; ST, surrounding non-tumor tissue.

Figure 2. The 935-gene HCC signature highlights relevant drug candidates. A,

connectivity map algorithm applied to the 935-gene HCC signature identified 5

candidate molecules: trichostatin A, LY294002, vorinostat, rapamycin, resveratrol and

α-estradiol. The barview shows the enrichment of treatment instance ordered by their

corresponding connectivity scores. All selected drug candidates exhibit a significant

negative enrichment as regard to the 935-gene HCC signature. B, impact of identified




26

drug candidates on the viability of 6 human HCC cell lines. Cell viability was determined

after 72hrs of treatment with drugs at various concentrations (see methods, n=4

independent experiments). Each bar represents cell viability (mean ± SD) in the

investigated cell lines (SNU-475, SNU-449, SNU-423, SNU-387, HepG2/C3A, SK-Hep-

1, from left to right). A two-tailed non-parametric Mann-Whitney test was used for the

comparison between experimental groups for each cell line (DMSO vs. untreated

control and drug vs. DMSO). * denotes a P value <0.05. C, Gene set enrichment

analysis (GSEA) of the induced genes (upper 3 panels) and the repressed genes (lower

3 panels) from the 935-gene HCC signature in the gene expression profiles of the SNU-

423 cell line treated with DMSO or vorinostat (left 2 panels), resveratrol (middle 2

panels) and sorafenib (right 2 panels). Positive and negative enrichment scores (ES)

were determined by GSEA. DMSO, dimethyl sulfoxide; HCC, hepatocellular carcinoma.

Figure 3. Therapeutic strategies integrating core and subtype-specific transcriptional

hallmarks in human HCC. The meta-analysis presented in this study enhances our

knowledge on the molecular characterization of HCC tumors and echoes previous

studies focused on HCC stratification. Recently, a consensus has been achieved

identifying 3 main HCC molecular subtypes (S1, S2 and S3) (6). S1 and S2 subtypes

are associated with a poor prognosis and included highly proliferative and poorly

differentiated tumors. S1 subtype is particularly associated with the activation of a pro-

metastatic TGFβ signaling and the S2 subtype included tumors with a progenitor-like

phenotype. S3 subtype is associated with a better prognosis, low proliferation and

includes well-differentiated tumors that retained a hepatocyte-like phenotype. At the




27

molecular level the S3 subtype is enriched in tumors that exhibit activating mutations in

CTNNB1 gene encoding β-catenin. HCC stratification into homogeneous subtypes

opened new avenues for personalized targeted therapies. The highlighting of core

transcriptional hallmarks in human HCC suggests that efficient therapies should

consider drugs targeting common HCC hallmarks together with drugs targeting specific

HCC subtypes. CTNNB1mut, mutated beta-catenin gene; HCC, hepatocellular

carcinoma; TGFβ, transforming growth factor beta.













Published OnlineFirst September 12, 2016.Cancer Res Coralie Allain, Gaëlle Angenard, Bruno Clément, et al. hallmarks of human hepatocellular carcinomaIntegrative genomic analysis identifies the core transcriptional

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