REVIEWS
Strategies for Hepatocellular Carcinoma Therapy and
Diagnostics: Lessons Learned from High Throughput
and Profiling Approaches
Kai Breuhahn,1 Gregory Gores,2 and Peter Schirmacher1
Over the last decade, numerous small and high-dimensional profiling analyses have been
performed in human hepatocellular carcinoma (HCC), which address different levels of
regulation and modulation. Because comprehensive analyses are lacking, the following
review summarizes some of the general results and compares them with insights from other
tumor entities. Particular attention is given to the impact of these results on future diagnostic and therapeutic approaches. (HEPATOLOGY 2011;53:2111-2120)
H
epatocellular carcinoma (HCC) is a unique
tumor entity by several measures. Its causes
(chronic viral hepatitis, alcoholic and nonalcoholic steatohepatitis, aflatoxins, and several hereditary diseases [e.g., genetic hemochromatosis]) are
much better defined than in other adulthood cancers
and are demonstrable in approximately 90% of cases.
Consequently, HCC is the most relevant paradigm of
virus- and inflammation-associated cancer. This opens
a large field for primary (immunization, specific hygienic measures) and secondary (screening programs,
therapy of predisposing diseases) prevention, but it is
also the reason for another peculiarity: conflicting
morbidity. The majority of HCC patients (>80% in
highly industrialized countries) have chronic hepatitis
or cirrhosis, which influences disease progression and
may severely restrict patient prognosis, therapeutic
options, and design of clinical trials.1 The morphological sequence of premalignant and early malignant
changes is well defined, but the lesions are hardly accessible by diagnostic means,2 which constitutes a significant difference from colon, breast, or skin cancer.
Abbreviations: CGH, comparative genomic hybridization; HBV, hepatitis B
virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; miRNA,
microRNA.
From the 1Institute of Pathology, University Hospital, Heidelberg, Germany;
and the 2Mayo College of Medicine, Mayo Clinic, Rochester, MN
Received December 14, 2010; accepted March 6, 2011.
Supported by the German Research Foundation (SFB/TRR77), the
Helmholtz Alliance on Immunotherapy of Cancer, and the German Federal
Ministry for Education and Research (Virtual Liver Network).
Address reprint requests to: Peter Schirmacher, M.D., Institute of Pathology,
University Hospital, Im Neuenheimer Feld 220, 69120 Heidelberg, Germany.
E-mail: [email protected]; fax: (49)-6221-56-5251.
C 2011 by the American Association for the Study of Liver Diseases.
Copyright V
Published online in Wiley Online Library (wileyonlinelibrary.com).
DOI 10.1002/hep.24313
Potential conflict of interest: Nothing to report.
Well-characterized model systems are available for
mechanistic as well as preclinical analyses,3 and HCC
cell lines have been workhorses for biochemical as well
as molecular biological and recently even systems biological analyses, providing a wealth of basic research
data for current translational approaches.
Because of the obstacles characterized above, HCC
has long been an orphan tumor disease with regard to
translational research efforts, clinical trial perspectives,
and therapeutic options, which stands in sharp contrast
to its enormous clinical relevance. HCC is the sixth
most frequent cancer and the third most frequent
cause of cancer-related death, and numbers of cases are
rising, even in industrialized countries.4 Nevertheless,
knowledge about molecular pathogenesis of HCC is
lagging behind other major tumor diseases, such as
breast and colon cancer, where multiple systemic treatment options are starting to convert in many cases previously untreatable metastatic tumors into a chronic
disease. Recently, this picture has started to change for
HCC and has gained some momentum: The growing
Chinese economy has fueled the industry’s interest and
improved options for clinical trials and novel therapeutics. The successful SHARP (Sorafenib HCC Assessment Randomized Protocol) trial established sorafenib
as the first effective and approved systemic treatment
for HCC and proved that despite all obstacles, trials
employing systemic treatments can be successful.5
Other treatment options such as radioembolization6
and oncolytic approaches7 have entered the field.
Meanwhile, more than 150 phase 1 to 3 trials are
ongoing, but only some of them are based on rational
approaches using knowledge about molecular pathogenesis of human HCC. Comprehensive, large-scale
profiling approaches on representative collectives are
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BREUHAHN, GORES, AND SCHIRMACHER
missing so far, but numerous analyses have been performed at the genomic, epigenetic, and expression
level, providing insight into relevant mechanisms, targets, as well as markers, suggesting future strategies for
systemic HCC treatment.
Technical Aspects
Genomic, microRNA (miRNA), and some proteinbased assays are more robust and less vulnerable to
influences imposed by imperfect sample conditions.
These approaches are well applicable to formalin-fixed,
paraffin-embedded samples and thus have found their
way into even routine diagnostics. In contrast, assays
based on larger coding or noncoding transcripts
depend highly on material preservation and assay conditions. This does not restrict their potential as exploratory technologies, but it impedes their comparability
and restricts meta-analyses and diagnostic applicability.
Currently, methylation analyses pose significant challenges in data acquisition as well as interpretation.
Broad spectrum proteomic or metabolomic approaches
are certainly further away from application and have
not been used for significant HCC collectives.
Profiling data analyses can be performed in unsupervised and supervised fashion. Although unsupervised
analyses are believed to be less biased, in most cases
geographic parameters or the fact that, for example,
only resection specimens are used inherently influences
data interpretation. However, due to profound knowledge about its etiology, translational HCC research
needs hypothesis-driven, supervised analyses guided by
epidemiological, clinical, or experimental nominators
to identify factors modulating its development or progression. These factors may include viral and nonviral
etiology,8,9 sex,10 tumor recurrence,11 intrahepatic
metastasization,12 response to therapy,13 and fetal-type
gene expression pattern.14 Knowing and controlling
this bias impeding all supervised and unsupervised
HCC analyses is of utmost relevance for drawing conclusions and making strategic decisions.
The source of the tissue samples is an important
bias, because etiology varies dramatically depending on
the geographic region of origin.4 Hepatitis B virus
(HBV) etiology is less frequent, and aflatoxin-based
effects are usually absent in collectives from Western
industrialized countries compared with countries in
eastern Asia and southern Africa, whereas the effects of
alcohol consumption and metabolic syndrome are
more prevalent. Furthermore, significant differences
exist in the relative frequency of HBV versus hepatitis
C virus (HCV) infection. In addition to geographic
HEPATOLOGY, June 2011
differences, collectives based on resection specimens
address limited disease and are biased for nonmetastatic, less aggressive tumors of presumably better
spontaneous course, and also for a lower frequency of
cirrhotic changes in the nontumorous liver. These factors have already been demonstrated to correlate with
differences in the results of the respective analyses;
thus, we currently have no single analysis in hand that
is truly unbiased. Consequently, many array-based
analyses have obtained inconsistent and partly contradictory results. One possible way to control this problem is through meta-analyses that integrate as many
data from different studies as possible or reflections
comparing results from different types of studies.
Unfortunately, these approaches suffer from variations
in regard to technical platforms, sample preparation
and evaluation, normalization, and data analyses; they
have successfully been performed with robust genomic
data,15 but recently some success has also been
achieved using transcriptomic HCC data.16 There is a
strong need for comprehensive analyses addressing several levels of regulatory processes in a single collective
and for analyses of collectives that are less biased; the
results are likely to differ from those obtained so far.
Whether ongoing large-scale but still biased efforts for
systematic analysis of cancer genomes such as the International Cancer Genome Consortium will improve this
specific situation in HCC has yet to be seen.
Screening for Aberrations
Genetic Analyses
Historically, comparative genomic hybridization
(CGH) represented the first molecular method to
screen tumor tissue for genetic changes in a comprehensive manner. More than 40 single studies in human
HCC have elaborated recurrent chromosomal imbalances that correlated with etiology (e.g., losses of 4q, 8q,
13q, and 16q with HBV; losses of 8p in HCV-negative cases) or tumor progression (losses of 4q and
13q).15 Self-organizing tree algorithms identified gains
of 1q21-23 and 8q22-24 as early and the gain of
3q22-24 as late genomic events, demonstrating sequential gain of genetic instability.18 In contrast to conventional CGH, array-CGH approaches provide higher
genomic resolution and therefore allows one to scale
down the correlations of more and smaller aberrations
with clinicopathological features such as microvascular
invasion and tumor grading.19 Moreover, specific alterations (e.g., 1q32.1, 4q21.2-32.33) discriminate
between HBV- and HCV-associated HCCs,9 and the
HEPATOLOGY, Vol. 53, No. 6, 2011
high resolution of this technique allows for the precise
delineation of respective candidate oncogenes and tumor-suppressor genes, as demonstrated for Jab1, YAP,
and Mdm4.9,20,21 In summary, three main conclusions
can be drawn from these studies: (1) HCC is a chromosomally instable cancer that, in general, accumulates
high numbers of macro- and microimbalances; (2) early
chromosomal imbalances precede malignant transformation, because they are detectable in a significant
number of premalignant lesions; and (3) etiology matters, because several chromosomal macroimbalances
correlate with the underlying cause of the HCC. The
reason for this observation has not been clearly defined.
Mutational activation and inactivation of individual
genes are frequently observed in most HCCs and represent
protumorigenic events independent of genomic instability.
Here, especially loss-of-function as well as gain-of-function
mutations in TP53 facilitate tumor proliferation, cell
migration, and cell survival.22 In addition, several mutations with low or moderate frequency have been described
for HCC, for example, in AXIN1/2,23 CTNNB1,24, M6P/
IGF-2R,25 TCF1/HNF1a,26 PIK3CA,26 K-RAS,27 and
p16/CDKN2/INK4A28 (Table 1). Data collected so far
demonstrate that few high-frequency mutations and many
low-frequency events contribute to the molecular heterogeneity of HCC. The spectrum of mutations will certainly
be expanded by high-throughput sequencing technology
in the near future.
Epigenetic Analyses
In carcinogenesis, global DNA hypomethylation has
been associated with activation of oncogenes and
genomic instability,29 whereas hypermethylation of
CpG (cytosine guanine dinucleotide) islands located
especially in gene regulatory sequences (e.g., of the Ras
target RASSF1A, the adhesion molecule CDH1, and
the cell cycle regulator p16/CDKN2/INK4A) resulted
in transcriptional silencing.26,30 Methylation changes
may occur early in the process of cancer development,
and CpG island hypermethylation of regulatory
regions of tumor-relevant genes is a frequent event
accumulating in multistep hepatocarcinogenesis.31
Only a few studies have analyzed the global and promoter-specific levels of DNA methylation in hepatocarcinogenesis. First published data have revealed clear
differences in DNA methylation between HCC and
surrounding nontumorous tissue based on specific promoter hypermethylation and global hypomethylation.32 In this regard, genomic hypomethylation correlated with genomic instability in HCC, whereas
methylation of CpG islands was associated with poor
prognosis.33 In addition, DNA methylation status cor-
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Table 1. Low- and High-Frequency Mutations in HCC
Gene
Low frequency (<20%)
Axin 1
Axin 2
TCF1/HNF1a
PIK3CA
K-RAS
p16/CDKN2/INK4A
SMAD2
SMAD4
RB1
PTEN
High frequency (>20%)
TP53 (low aflatoxin exposure)
TP53 (high aflatoxin exposure)
CTNNB1
M6P/IGF-2R
Mutation Frequency
References
5%-13%
3%
5%
2%
1% (33%*)
6.3%
3%
6%
14%
0%-10%
23,26,82
23
26
26
26,27
28
105
105
106
107-109
10%-28%
48%
13%-43%
21%-55%
100,101
100
26,102
25,103,104
*Vinyl chloride exposition.
Abbreviations: CDKN2, cyclin-dependent kinase inhibitor 2; CTNNB1, catenin
(cadherin-associated protein) beta 1; HNF1a, hepatocyte nuclear factor-1
alpha; IGF-2R, insulin-like growth factor-2 receptor; K-RAS, v-Ki-ras2 Kirsten rat
sarcoma viral oncogene; M6P, mannose-6-phosphate; PTEN, phosphatase and
tensin homologue; SMAD, small mothers against decapentaplegic; TCF1, transcription factor 1.
related with tumor recurrence after hepatectomy, cancer-free survival, and overall survival.34 Using class
comparison analysis, HBV-, HCV-, and alcohol-specific promoter methylation patterns have been
described, suggesting etiology-dependent methylation
in early stages of hepatocarcinogenesis.32 Knowledge
about these modifications in tumorigenesis is certainly
fragmentary, but epigenetic analyses may represent valuable tools for diagnosis and classification in the early
stages of liver tumor development.
Transcriptional Analyses
Coding Transcripts. Most transcriptomic studies in
HCC have used cDNA or oligonucleotide high-density
microarrays. Despite varying technical platforms, biological controls, and mathematical algorithms, these
approaches have identified partly novel tumor-relevant
genes and networks (e.g., PEG10, insulin-like growth
factor-II [IGF-II], Claudin10, RhoC, AP-1, and cell
cycle regulators).14,35-37 Some studies have correlated
expression profiling data in HCC with etiology,8 vascular invasion,38 drug response,13 recurrence,12 and
survival.36 Unsupervised clustering of transcriptomic
data provided subtyping of HCC that was related to
tumor-associated inflammation as well as tumor cell
proliferation and apoptosis.35,39 Furthermore, specific
expression signatures derived from global gene expression analyses correlated well with the histological classification of premalignant lesions (low- and high-grade
Dysplastic Nodules) and HCCs.40 Ye et al.41 also
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demonstrated that transcriptomic signatures significantly
differed between HCCs with and without metastatic
spread, whereas expression profiles of respective primary
and metastatic tumors varied only by a few genes.
Hierarchical clustering has revealed that HCCs can
be divided into subgroups based on transcript profiles.
Lee et al.36 described the existence of two distinct
groups of HCC characterized by poor survival specifically within the group that showed high expression of
genes involved in proliferation and antiapoptosis. This
study further demonstrated that the transcriptional
pattern of HCCs that shared a signature with fetal
hepatoblasts exhibited poor prognosis.14 Yu et al.42
demonstrated an association between their identified
subclasses and tumor dedifferentiation (grading G1/2
versus G3/4) as well as overall survival.
Despite all limitations, a recent meta-analysis integrating a high number of HCC data (>600) from independent gene expression profiling analyses was able to
demonstrate the existence of three distinct molecular
subclasses (S1-S3) and confirmed some important previous findings (e.g., activation of Wingless pathway, existence of a (proliferation) signature). However, it also
exemplified some of the difficulties ahead by, for example, showing that activation of typical Wingless-dependent gene expression did not correlate with mutations in
CTNNB1.16 In summary, transcriptional signatures
have allowed for classification of HCCs according to
their molecular and biological characteristics26 and have
turned out to be a valuable tool in the identification of
tumor-relevant genes and pathways in human HCC.
Noncoding RNA/miRNA. A steadily increasing
number of studies have examined differential expression of noncoding RNAs (especially miRNAs) in
HCC. miRNAs bind complementary sequences in the
30 end of messenger RNAs and therefore represent
effective posttranscriptional regulators of mammalian
gene bioactivity.43 In addition, miRNAs directly affect
promoter activity through binding and/or modifying
DNA methylation.44,45 So far, miRNAs with oncogenic or tumor-suppressing potential have been identified,46,47 and recent results indicate that different
stages of hepatocarcinogenesis as well as liver tissues
with HBV or HCV infection can be differentiated
from each other based on their miRNA fingerprints.48
This is supported by other studies showing that distinct miRNA signatures were associated with alcohol
consumption and HBV infection,49 tumor differentiation and progression,46,50 metastasis, survival, and
relapse.51,52 Although the key miRNAs identified significantly differed between various studies,48,49,53 some
miRNAs such as miR-122a48,49,54 and miR-22347,48
HEPATOLOGY, June 2011
have recurrently been identified through independent
approaches. Using specific antagomirs and agomirs, it
is possible to associate distinct miRNA activity with
cellular processes, but because each miRNA potentially
interacts with several different targets, it is difficult to
define the precise mechanisms and pathways by which
miRNAs mediate their biological effects. Recently,
reduced miR-26 expression was linked to NF jB and
interleukin signaling, shorter survival, and better
response to IFN-a therapy.55 In addition, independent
studies demonstrated the relevance of other miRNAs
such as miR-139,56 miR-125b,57 miR-221,46 and
miR-18158 in the regulation of tumor-relevant proteins
and processes in hepatocarcinogenesis. Recent data
have shown that on the basis of c-MYC–dependent
miRNA signatures detected in hepatoblastoma, it is
possible to discriminate between HCCs with an invasive phenotype and patients with lower survival probability.59 Based on their high stability even in formalinfixed, paraffin-embedded tissues, miRNAs represent
promising molecular markers for diagnostic HCC classification, prognostic stratification, and drug response
prediction even under routine clinical conditions.
Protein Analyses
Proteomic analysis (e.g., by two-dimensional gel
electrophoresis, or MALDI-TOF [matrix-assisted laser
desorption/ionization time-of-flight] or SELDI-TOF
[surface-enhanced laser desorption/ionization time-offlight] mass spectrometry) is believed to be more informative than other screening approaches because
proteins represent the main functionally active principle in cells. Moreover, only moderate correlations
between mRNA and protein abundance demonstrate
the value of protein assays for biomarker identification
in blood and liver tissues.60,61 For HCC, distinct protein profiles that discriminate between HBV- and
HCV-associated tumors have been identified.62 In
addition, many differentially expressed proteins (partly
derived from patients’ sera) in HCCs have been
described, including heat shock protein 90 (HSP90)63
and stathmin.64 However, because in the different studies the number of identified proteins is relatively low
(normally far below 100), protein-based assays have
been of limited value for subtyping of HCCs so far.
Many studies have used immunohistochemistry for
the analyses of distinct factors and protein families in
HCC. These include signaling pathway constituents
(e.g., b-catenin,24 different FZD receptors,65 and
c-MET66), cell cycle regulators (e.g., p16/CDKN2/
INK4A,27 and survivin67), and transcriptional regulators (e.g., p53,24 c-MYC,24,68 FBPs,69 YAP,70 and
HEPATOLOGY, Vol. 53, No. 6, 2011
SMADs71,72). In addition, these analyses discriminate
between different subcellular localizations of proteins
(e.g., for b-catenin or p53) and provide the possibility
of assessing protein expression in a semiquantitative
manner using high-throughput imaging systems and respective mathematical analysis algorithms. In conclusion, protein-based assays may provide the most relevant information with regard to levels and location of
bioactive units in cells; however, technical limitations
currently prevent these approaches from being available
for unbiased analyses in larger HCC collectives.
Integrative Approaches
Integrating different types of analyses in HCC has
supported the identification of genes and pathways
that are often aberrantly regulated by several different
low-frequency mechanisms. For example, aberrant activation of pleiotropic growth factors, receptors, and
their downstream signaling pathway components represents a central protumorigenic principle in hepatocarcinogenesis. Constitutive dysregulation of these pathways
(e.g., hepatocyte growth factor/c-MET, IGF/IGF1 receptor, transforming growth factor b [TGFb]/epidermal growth factor receptor, and TGFb/TGF receptor
signaling) occurs at different mechanistic levels, such
as regulation of expression, cell type–specific and subcellular localization, and mutational (in)activation.73,74
In addition, crosstalk between pathways and with other
tumor-relevant factors (e.g., HSPs, COX-2, and p53)
further demonstrate that integrative approaches including genomic, transcriptomic, and protein analyses are
necessary to understand the complex and dynamic
interplay between different oncogenic modules and
pro/antioncogenic mechanisms.
Moreover, integrative approaches have started to
support classifying groups of HCCs according to specific overall molecular characteristics, prognostic
impact, and even some predictive implications. Laurent-Puig et al.75 identified two molecular subgroups
characterized by high chromosomal instability (associated with Axin-1 and TP53 mutations) or more stable
conditions (associated with CTNNB1 mutations).
HCCs from the first group were less differentiated,
more frequently exhibited HBV infection, and in the
case of loss of heterozygosity of 9p and 6q, showed
poorer prognosis. Based on array-CGH analyses,
Katoh et al.76 equally identified genetically homogenous classes of HCC (two clusters and six subclusters).
In contrast to the previous study, specific chromosomal
alterations (gains of 1q, 6p, and 8q and losses of 8p)
in one cluster were associated with high chromosomal
instability and poor patient survival. In addition, no
BREUHAHN, GORES, AND SCHIRMACHER
2115
correlation of CTNNB1 or TP53 mutations to any of
the groups was detectable. Of relevance, some subclusters harbored genomic amplifications of genes involved
in mammalian target of rapamycin (mTOR) and vascular endothelial growth factor (VEGF) signaling.
Based on the integration of genomic data and gene
expression profiles, Woo et al.77 identified 50 potential
driver genes in HCC. In fact, tumor class defined by
the expression of this signature predicts the prognostic
outcome of patients with HCC.
Boyault et al.26 described the existence of six HCC
groups (G1-G6) that were characterized by distinct clinical and molecular features. For example, G1-G3
tumors exhibited more chromosomal instability and a
tendency for poorer prognosis than G4-G6 HCCs.
TP53 mutations accumulate in the subgroups G2 and
G3, whereas mutations in CTNNB1 are characteristic
for G5 and G6 tumors. Interestingly, this grouping
showed some similarities with the molecular classification of Lee et al., such as the existence of groups with
chromosomal instability, poor survival, and hepatoblast
characteristics.14,36 By integration of genomic, transcriptomic, and protein information of HCV-associated
HCCs, Chiang et al.39 defined five molecular classes of
HCC that partly overlapped with previously described
groups. These classes were characterized by known molecular features such as activation of the Wingless pathway, proliferation, chromosomal instability,26 and
induction of interferon-stimulated genes.35 In addition,
this study identified a new subgroup characterized by
polysomy of chromosome 7. Overall, there is increasing
evidence that robust and clinically relevant subclassification requires integration of different technologies; future
analyses will have to elaborate minimal diagnostic patterns in order to allow rational up-front testing.
Lessons Learned from Profiling Analyses
Although comprehensive analyses of all aspects in a
representative collective of HCCs are missing and the
existing data are either incomplete or biased, several
conclusions can be safely drawn.
First, the vast majority of HCCs represent a classical
chromosomally instable tumor (CIN-phenotype) carrying multiple genomic imbalances that is comparable to
other adulthood malignancies, such as breast and sporadic colon carcinoma or pancreatic cancer. Microsatellite instability (mismatch repair deficiency) and methylator phenotypes seem to have little, if any, overall impact.
Chromosomal instability increases during tumor progression, suggesting it as a driving factor.78 Because genomic
macroimbalances at some genomic loci (but not global
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chromosomal instability) can be observed already in premalignant Dysplastic Nodules,15 an important question
is: Which processes force chromosomal instability or
govern relative chromosomal stability, and at which stage
during tumor progression are they altered? There is experimental evidence for p53-dependent senescence surveillance that contributes to chromosomal stability,79,80
but it is unlikely to be the only factor. Molecular mechanisms of chromosomal stability governance may not
only offer insights into mechanisms of HCC progression
but may also be valuable therapeutic targets even in progressed, chromosomal-instable HCC.
Second, somatic mutations in HCCs (in addition to
chromosomal imbalances) include point mutations,
small/medium size insertions and deletions, and balanced chromosomal translocations. Some of the HCC
mutations are of high frequency (such as codon 249
mutations in TP53 in aflatoxin-exposed collectives),81
but the vast majority of these mutations affect only
small subpopulations of HCCs, as has been demonstrated for Axin mutations.23,82 Mutational analyses of
single genes in different HCC populations underscore
the molecular heterogeneity of human HCCs as demonstrated also by chromosomal imbalances. Comprehensive mutational data for HCC collectives are lacking and will be collected in the frame of the
International Cancer Genome Consortium.17 The first
deep sequencing data have been obtained in other
adulthood type cancers, such as colon, breast, and pancreatic cancer, where mutations in coding regions of
cancer genomes average about 60-80 mutations and in
some cases may even reach triple digits.83,84 Thus, the
number of mutations is much higher than previously
expected; this is even more evident in light of the fact
that these mutations are complemented by epigenetic
changes and gross chromosomal imbalances. The situation becomes more complex, considering the impact of
these mutations, because data from breast and colorectal cancer suggest that some of them are driver
mutations, whereas the vast majority of mutations may
be associated only with small fitness advantages.83
There is little doubt that the situation in HCC will be
comparable, but the specific impact for tumor therapy
is unknown and remains to be analyzed.
Third, there is convincing evidence that etiology
leaves its molecular traces in the tumor genome,
leading to specific genomic imbalances, mutations, epigenetic changes, and resulting alterations in host gene
expression. Whether these effects are direct consequences of the specific carcinogenic mechanisms (e.g.,
exerted by HBV integrations or direct genotoxic effects
of mycotoxins) or represent indirect effects due to
HEPATOLOGY, June 2011
functional selection of complementary protumorigenic
mechanisms cannot be answered globally. Nevertheless,
etiological ‘‘fingerprints’’ offer insight into the stepwise
process of molecular carcinogenesis and the interrelation of different oncogenic mechanisms and provide
openings for secondary preventive strategies. HCC was
one of the first and is certainly one of the best-studied
paradigms for molecular cancer epidemiology,81 and
this may fuel the search for as-yet undetected etiological mechanisms.
Fourth, comprehensive approaches in other tumor
entities, such as breast, colon, and pancreatic cancers
have convincingly shown that in common solid cancers
of adulthood, a surprisingly high number of pathways
(12-15) is altered in a protumorigenic manner.83,84
These different affected pathways seem to cover most
of the tumor-relevant functions, but at the same time
significant functional overlaps exist between them.74
As outlined above, current evidence for HCC points
in the same direction. Frequently affected pathways
and effectors include Wnt signaling, growth factor–
induced signaling (e.g., IGF and TGFb), or cellular
gatekeepers such as p53. Matching affected pathways
and underlying molecular changes shows that these
pathways can be altered at different points, which has
already been proven for Wnt/Wingless signaling (e.g.,
Axin-1/Axin-2 and b-catenin mutations, increased cadherin-17).23,24,82,85 Interestingly, growth factor research
in HCCs has shown that in each given pathway, frequent typical alterations (nodal points?) exist, but these
changes differ between the pathways, varying from
aberrant ligand expression (e.g., IGF-II)86 to receptor
bioavailability (e.g., c-MET)66 to alterations in intracellular signal transducers (e.g., TGFb signaling).71,72
The cause for these observations is unknown, but it
may offer some hints about how therapeutic
approaches should be designed in order to target essential points of interference. It is reasonable to conclude
that the spectrum of molecular changes present in a
given HCC also contains stochastic elements largely
selected by function. There is no evidence for a dominant driver mechanism and resulting addiction to it, as
can be observed in several childhood malignancies and
gastrointestinal stromal tumor.
Finally, comprehensive analyses have started and are
likely to provide molecular subgrouping of HCC. Initial
attempts have been made (e.g., by J. Zucman-Rossi and
her group), clearly demonstrating the feasibility of the
approach.26 Improvement can be expected from further
meta-analyses of existing data and novel comprehensive
analyses on well-characterized collectives. There is significant evidence that molecular classification reflects
HEPATOLOGY, Vol. 53, No. 6, 2011
functional aspects and correlates with prognosis. At least
some of the subgroups are likely to be relevant for therapy and predictive diagnostics, as exemplified by IGFIR26,35 and mTOR-associated signaling.87
Consequences for Therapeutic Approaches
What are the consequences for drug development, clinical trials, and molecular (predictive)
diagnostics?1,88
There is certainly sufficient room and need for further (pathway) targeted approaches. Constitutive activation, for example, by mutation or ligand based stimulation of growth factor signaling pathways, is a
common theme most likely relevant in every case of
HCC.74 On the other side, many different pathways
can be affected, and their functional consequences in
regard to proliferation, motility, antiapoptosis, and
angiogenesis significantly overlap. Thus, response to
specific tyrosine kinase–directed approaches may be
limited and can be expected only in subgroups of
HCCs, and secondary resistance is likely to occur
soon, because there is little if any evidence for a specific pathway addiction in HCC. From a mechanistic
point of view, approaches to inhibit tyrosine kinase/
growth factor signaling pathways should be as broad as
possible and should consider complementary and combinatorial settings up front. Identification of patients
who may benefit (more) from these approaches
requires comprehensive biomarker analyses accompanying the clinical trails. This is state-of-the-art in most
other malignancies, but has not been thoroughly
respected in HCC, probably due to the fact that HCC
is the only relevant tumor entity that does not necessarily require tissue-based diagnosis prior to therapy.
Because molecular definition of responsive subgroups
is not possible without tissue access, this difference
may cause more trial failures than expected or necessary and may turn out to be a negative aspect of HCC
in comparison with other tumor entities.
The fact that protumorigenic alterations in relevant
pathways in HCCs may occur at different (nodal)
points may limit the application of specific inhibitors
and has to be respected in predictive diagnostic
approaches as well as drug and subsequent trial
design.88 A question that must always be addressed is
the size of the responsive patient collective and
whether it justifies the clinical and commercial effort.
Some of these aspects can be better addressed by novel
adaptive trial concepts.89,90 It also has to be considered
that drug companies may choose to lower the thera-
BREUHAHN, GORES, AND SCHIRMACHER
2117
peutic benefit rather than the size of the patient collective amenable to treatment.
Because breakthrough achievements are unlikely to
result from specific (pathway) targeted approaches in
HCCs, our attention should also focus on mechanisms that are constantly needed by the tumor (that
is, the tumors’ ‘‘Achilles’ heels’’). These represent either necessarily required cellular functions that support a protumorigenic phenotype or are central mechanisms that allow for tumor persistence or
progression. Examples of the first are the chaperone
network (e.g., HSP90 and interacting factors)91 as
well as all factors that support tumor cell proliferation
and cell cycle progression. Tumor-associated neoangiogenesis may represent a double-edged sword: on one
hand, it is an indispensable prerequisite for tumor
growth; on the other hand, it is required to build up
sufficient intratumoral drug concentrations. Recent
results indicate that the effect of antiangiogenic
approaches may depend on tumor characteristics (e.g.,
tumor cell biology and stroma content) that may need
further attention.92
Examples for central tumor-relevant mechanisms
may provide an even more attractive basis for therapeutic concepts. Global down-regulation of miRNAs is
found in most tumors and suggests a role for the
miRNA processing machinery. There is recent evidence
for a critical role of dicer and some link to the p53
family members.93,94 It will have to be shown whether
this holds true in HCC and can be modulated in an
antineoplastic manner. Tumor cell aneuploidy, as present in almost all HCCs, is a condition usually not
compatible with cell survival under physiological conditions; this may explain the usually higher apoptosis
rate of malignant tumors, but tumor cells must also
have established mechanisms to prevail and maintain
all vital cell functions despite the presence of significant aneuploidy. First screens have demonstrated genes
that may provide increased aneuploidy tolerance;95 the
future will show whether they may represent valid and
innovative drug targets.
These considerations provide different challenges for
drug design. Tumor cell specificity may not be
achieved by addressing pathways or specific mechanisms that are more or less exclusive to tumor cells;
instead, pharmacokinetics and pharmacodynamics
may have to be modulated in order to favor tumor
cell–associated activity or activation of the drug
employing tumor preferential mechanisms.96
Predictive marker analyses do not play a role in current clinical diagnostics in HCC, but it will be necessary to include them in future clinical trials. Even if
2118
BREUHAHN, GORES, AND SCHIRMACHER
broader therapeutic approaches are tested, predictive
marker analyses may well indicate response as well as primary and secondary resistance to therapy. Paradigms
include microsatellite instability testing for chemotherapy
response in colorectal cancer and ERCC1 (excision repair
cross-complementing 1) testing regarding platinum-based
chemotherapy in non–small cell lung cancer.97,98 Clearly,
the value of histological subtyping and molecular predictive diagnostics exceeds target gene evaluation.
Knowledge about molecular pathogenesis of HCC
has dramatically improved in recent years, and some
progress has been made (or is just ahead) in translation
into clinical application,1 but there is room for
improvement. In particular, comprehensive molecular
analyses and further rationally designed clinical trials
based on molecular evidence (e.g., targeting IGF-IR
and mTOR) are eagerly awaited.99
Acknowledgment: The critical discussion and helpful comments of Hendrik Bläker and Federico Pinna
are gratefully acknowledged.
HEPATOLOGY, June 2011
13.
14.
15.
16.
17.
18.
19.
20.
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