Báo cáo khoa học: A metabolomics perspective of human brain tumours pdf

MINIREVIEW
A metabolomics perspective of human brain tumours
Julian L. Griffin
1
and Risto A. Kauppinen
2
1 Department of Biochemistry, University of Cambridge, UK
2 School of Sport and Exercise Sciences, University of Birmingham, Edgbaston, UK
Introduction
The global analysis of metabolites, such as by mass
spectrometry (MS) and high resolution multinuclear
nuclear magnetic resonance spectroscopy (MRS),
places cells, tissues or organisms in biological context
by defining metabolic phenotypes [1,2]. Such metabolo-
mic approaches are being used to profile cell lines,
tumours and systemic metabolism in human cancer
tissue ex vivo and in vivo, and will provide another
functional genomic tool for cancer research [3]. Whilst
‘-omic’ technologies are complementary to one
another, the metabolome provides specific advantages
when compared with the transcriptome and proteome.
For in vitro purposes the work is cheap on a per
sample basis. Furthermore, being downstream of
gene transcription, changes in metabolites may well
be amplified, and there is not necessarily a good
quantitative relationship between mRNA concentra-
tions and function, especially for proteins whose con-
centration is determined by their rate of degradation
or whose activity is controlled by allosteric effects or
post translational modification. This suggests that meta-
bolomics may be particularly sensitive to changes in a

biological system, and have a more direct correlation
with the phenotype produced.
This minireview focuses on metabolomics of human
brain tumours obtained in the first hand by multinu-
clear MRS and MS using both ex vivo and in vivo
approaches. Over recent years a wealth of data have
indicated that ‘metabolite phenotypes’ bear great
potential for clinical diagnosis, tumour grade assess-
ment and finally, monitoring of treatment response of
brain tumours. Looking to the future, the technology’s
impact on diagnosis through minimally invasive
screening will also be discussed.
Keywords
brain; metabolomics; NMR spectroscopy;
pattern recognition; tumour
Correspondence
J. Griffin, Department of Biochemistry,
University of Cambridge, Tennis Court Road,
Cambridge, CB2 1QW, UK
Fax: +44 1223 333345
Tel: +44 1223 764 922
E-mail:
(Received 19 October 2006, revised 7
December 2006, accepted 3 January 2006)
doi:10.1111/j.1742-4658.2007.05676.x
During the past decade or so, a wealth of information about metabolites in
various human brain tumour preparations (cultured cells, tissue specimens,
tumours in vivo) has been accumulated by global profiling tools. Such hol-
istic approaches to cellular biochemistry have been termed metabolomics.
Inherent and specific metabolic profiles of major brain tumour cell types,

as determined by proton nuclear magnetic resonance spectroscopy
(
1
H MRS), have also been used to define metabolite phenotypes in tumours
in vivo. This minireview examines the recent advances in the field of human
brain tumour metabolomics research, including advances in MRS and mass
spectrometry technologies, and data analysis.
Abbreviations
ANN, artificial neural network; Ala, alanine; CCM, choline-containing metabolites; Cre, creatine + phosphocreatine; GABA, c-amino butyric
acid; Gln, glutamine; Glu, glutamic acid; GPC, glycerophosphocholine; GPE, glycerophophoethanolamine; ICA, independent component
analysis; LC, liquid chromatography; Lip, lipids; MRI, magnetic resonance imaging; MRS, nuclear magnetic resonance spectroscopy; MRSI,
magnetic resonance spectroscopic imaging; NAA, N-acetylaspartic acid; PC, phosphocholine; PNET, primitive neuroectodermal tumour; Tau,
taurine.
1132 FEBS Journal 274 (2007) 1132–1139 ª 2007 The Authors Journal compilation ª 2007 FEBS
Metabolite patterns in neural cells
Three major neural cell types, i.e., neurones, glial
and meningeal cells, have strictly distinct functional
properties, a factor that is reflected in their metabolic
specialization. It has become evident that the three
neural cell types not only are distinguishable from each
other by morphological and immunocytochemical char-
acteristics, but also through their
1
H MRS metabolite
profiles. Using a subgroup of eight metabolites (from a
total number of  30 identifiable ones) quantified by
1
H MRS in acid extracts of cultured cells, one can
unambiguously separate the three neural cell types [4].
Similarly, several brain tumour cell types can be identi-

fied by their
1
H MRS metabolite content [5]. It was
observed that neuroblastoma, glioma and meningeoma
cells display low concentrations of normal neural meta-
bolites, such as N-acetylaspartate (NAA), c-amino
butyrate (GABA) and taurine (Tau) [5]. The meta-
bolites bearing greatest value for discrimination
of tumour cell types include total creatine (Cre; creat-
ine + phosphocreatine), choline-containing metabolites
[CCM; including phosphocholine (PC), glycerophos-
phocholine (GPC) and choline], alanine (Ala), Tau and
glutamate (Glu). Indicative to the potential clinical
value of MRS metabolite profiles,
1
H MRS data allow
separation between tumour types and grades [6,7]
(Table 1).
Metabolomics technology
Metabolomics usually consists of two methodologically
distinct parts. First, the analysis uses a global profiling
tool to measure the concentration of the metabolites
while the subsequent data matrix is interrogated by
multivariate statistics or data reduction tools. Sec-
ondly, pattern recognition processes can be separated
into unsupervised and supervised techniques. The for-
mer display the innate variation associated with the
data, while the latter uses prior information to cluster
the data. In addition to pattern recognition processes
[8,9], machine learning approaches have also been

applied to biochemical profiles of tumours [10].
For the analysis of brain tumours MRS and MS
dominate the literature, although in other applications
thin layer chromatography, Fourier transform infrared
and Raman spectroscopy have been used previously
[11,12]. Reflecting the literature, the majority of this
minireview concerns the use of MRS as a metabolic
profiling tool. However, MS approaches will be dis-
cussed briefly first.
Mass spectrometry
Mass spectrometry based approaches are inherently
more sensitive than MRS techniques, providing access
to lower concentration metabolites in the tumour
Table 1. Metabolites that have been commonly identified as perturbed in brain tumours using MRS either for tissue extracts or in vivo.
Metabolite Metabolic function Type of cancer ⁄ tumour
Alanine Increases in hypoxic tissues as a result of increased
glycolysis.
Brain tumors including astrocytomas, metastases,
gliomas, meningiomas, and dysembryoplastic
neuroepithelial tumors.
CH
3
&CH
2
lipids Increases in the relative intensities of lipid peaks
detected by NMR are believed to result from either the
production of cell membrane microdomains or increased
numbers of cytoplasmic vesicles.
Alterations in visible lipids have been linked to many
cellular processes such as proliferation,

inflammation, malignancy, growth arrest, necrosis
and apoptosis.
Choline containing
metabolites (CCMs)
Choline, phosphocholine, phosphatidylcholine and
glycerophosphocholine are major constituents of cell
membranes and increases in these metabolites reflect cell
death (apoptosis and necrosis).
Many tumour types including a range of brain
tumours.
Lactate Lactate is an end product of glycolysis and increases rapidly
during hypoxia and ischaemia, in particular as a result of
poor vascularity and acquired resistance to hypoxia.
Increased rates of lactate production are associated
with a range of tumours.
Myo-inositol In tumours, myo-inositol is involved in osmoregulation
and volume regulation.
Elevated in glioma.
Nucleotides Nucleotides are key intermediates in DNA ⁄ mRNA synthesis
and breakdown. Changes in ATP concentration also indicate
the energetic status of the tumour.
Found to be elevated in glioma during apoptosis.
PUFAs Polyunsaturated fatty acids are constituents of cell
membranes, especially mitochondrial.
Increased in glioma during apoptosis.
J. L. Griffin and R. A. Kauppinen A metabolomics perspective of brain tumours
FEBS Journal 274 (2007) 1132–1139 ª 2007 The Authors Journal compilation ª 2007 FEBS 1133
metabolome. Most applications use prior chromatogra-
phy with gas chromatography (GC) and liquid chro-
matography (LC) to initially separate out, by time,

metabolites in a tissue extract prior to analysis. The
use of MS to monitor the metabolic profiles of brain
tumours significantly predates the use of the term meta-
bolomics. For example, Jellum and colleagues [13]
identified  160 peaks in GC-MS spectra from normal
brain tissue, pituitary tumours and brain tumours, and
then used a pattern recognition approach to classify
tissue into healthy and tumour.
The sensitivity of mass spectrometry based approa-
ches has also been used to monitor trace metabolites
in excised tissue. For example, neurotransmitters in
neuroctomas have been profiled, including acetylcho-
line and the metabolites of catecholamines by HPLC
[14], while Olsen and colleagues [15] have used quadru-
pole-time of flight MS to detect morphine in glioma.
Mass spectrometry has also been shown to be highly
discriminatory for lipid metabolites, including ceramide
metabolites, which vary in neuroblastoma cells during
cell death [16]. MS profiling of lipid metabolites has
also been used to determine which components con-
tribute to resonances that are found in vivo
1
HMR
spectra. Miller and coworkers [17] demonstrated that
the CCM peak detected in brain tumour specimens lar-
gely correlated with choline, PC and GPC, but not
phosphatidylcholine.
Ex vivo monitoring of brain tumour
metabolites
The use of NMR spectroscopy to profile metabolites

in tumour cells and tissues has been applied to a
wide range of human tumours for a number of years,
with the approach being particularly useful at gener-
ating new hypotheses that link characteristics of a
tumour to metabolism. For example, Bhakoo and
colleagues [18] examined the process of immortaliza-
tion in primary rat Schwann cells, noting that an
increase in the PC ⁄ GPC ratio correlated with this
process.
Tissue heterogeneity is a major issue in assessing the
biochemical profile of tumours, particularly during
response to treatments. High resolution magic angle
spinning
1
H MRS is a highly versatile tool in this
respect, examining relatively small amounts of tumour
tissue, and can be used on tissue samples prior to
histopathology. Examining glioblastoma multiforme
removed during surgery, Cheng and colleagues demon-
strated that Lac and mobile lipids (Lip) were correla-
ted with degree of tumour necrosis and the proportion
of PC to choline correlated with the malignancy of the
glioma [19]. This had previously been shown by solu-
tion state multinuclear MRS of glioma extracts [20].
To investigate lipid metabolism within tumours, tan-
dem MS approaches provide a unique insight into
many classes of compounds. Sullards and colleagues
[21] have used this approach to monitor changes in
sphingolipid metabolism in human glioma cell lines in
order to correlate these profiles with either the induc-

tion or inhibition of apoptosis.
The metabolite data sets from
1
H MRS of extracted
human brain tumour biopsy specimens have been used
as inputs for pattern recognition analysis techniques
[22]. Incorporation of principal component analysis as
a means to reduce dimensionality of the MRS data for
neural network analysis provided classification of sam-
ples not only to meningeal and nonmeningeal tumours,
but also grading within gliomas to within one grade
with an accuracy of 62%. It was observed that only
few metabolites in the extracts were discriminatory,
including Cre, glutamine (Gln), Ala and myo-inositol
[22]. This study and many others [7,23,24] have dem-
onstrated metabolite abnormalities in brain tumours
that discriminate them from normal brain tissue.
Human brain tumours in vivo
Human brain tumours form some 2% of all malignan-
cies. Unlike outside the cranium both benign and
malignant tumours can be life threatening due to space
occupying nature. In adults, the majority of primary
brain tumours are derived from glial or meningeal tis-
sues, while secondary tumours contain metastases from
many organs (e.g., breast and lung melanomas) of the
body. Paediatric primary brain tumours also include
tumours from neuronal cells, e.g., neuroblastomas and
retinoblastomas. Despite significant heterogeneity in
metabolism in tumours [25], MRS has provided unique
information about tumour metabolites to be used for

diagnosis, treatment planning, setting prognosis and
monitoring efficacy of treatment procedures. Several
‘metabolonomic’ approaches have been proposed to
help decompose the MRS from human brain tumours.
31
P MRS
31
P MRS can readily distinguish phosphorylated cho-
line metabolites, including PC, PE, glycerophosphoryl
ethanolamine (GPE) and GPC, involved in cell mem-
brane metabolism [26,27], thus providing more
detailed information about tumour activity than avail-
able by
1
H MRS alone. Qualitative inspection of
brain tumour
31
P MR spectra indicated small differ-
ences in spectral appearances between normal brain
A metabolomics perspective of brain tumours J. L. Griffin and R. A. Kauppinen
1134 FEBS Journal 274 (2007) 1132–1139 ª 2007 The Authors Journal compilation ª 2007 FEBS
and gliomas [28]. Quantitative analysis of
31
PMR
spectra revealed that the overall concentrations of
MR detectable phosphates, including phosphodiester
and phosphocreatine, were significantly lower in
tumours than in normal parenchyma [29–31].
31
P MRS has also been used to observe tumour

responses to drug and radiation therapies [29].
1
H MRS
Metabolomics in vivo using
1
H MRS is limited by a
number of technical issues. First, brain tumours are
inherently heterogeneous in terms of their cellularity
and blood supply; secondly, spectral resolution is
much poorer in vivo than in vitro, allowing assignment
of some 10 tumour metabolites; and thirdly, sensitivity
of MRS at commonly used clinical field strengths and
narrow chemical shift scale of
1
H MRS limits the num-
ber of metabolites detected. Despite these factors
1
H MRS and MRS imaging (MRSI) from human
brain tumours are gaining an ever increasing role in
clinical assessment of patients with focal cerebral lesion
of any nature.
One of the key questions to be addressed remains
whether
1
H MRS alone can provide specificity and
sensitivity to identify proliferating lesions from other
common focal brain conditions. Recent studies show
that ischaemic infarctions show no
1
H MRS signals

apart from Lac and macromolecules [32,33]. In case of
infectious lesions
1
H MRS data provide > 90% specif-
icity to separate abscesses and tuberculomas from astr-
ocytic tumours [34]. Modern magnetic resonance
imaging (MRI) techniques provide a large repertoire to
diagnose brain lesions, such as ischaemic stroke, infec-
tions and multiple sclerosis [35] and thus, the role of
1
H MRS will remain confirmatory for these cases.
A wealth of
1
H MR spectroscopic data from brain
tumours shows that both tumour types and tumour
grades have characteristic spectral patterns. The idea
of looking at the
1
H MRS spectrum in a more holistic
manner arose from the work on cultured brain tumour
cells [36]. Hagberg and coworkers proposed a set of
multidimensional statistical methods for single-voxel
1
H MR spectra using the entire spectral width to clas-
sify human glial tumours [37]. A concept of
1
H MRS
profiles was introduced. Soon afterwards a concept of
‘
1

H MRS metabolic phenotype’ was coined by Usenius
et al. [38] and Preul et al. [39]. In these papers simpli-
fied
1
H MR spectra from healthy brain and tumours
comprising of six metabolites (CCM, Cre, NAA, Ala,
Lac and Lip) were used as inputs to artificial neural
network (ANN) analysis to classify the tumour types
and grades. Preul et al. used leaving-one-out linear
discriminant method for
1
H MRSI data sets and dem-
onstrated a phenomenal accuracy of 104 correct out of
105 cases [39]. Usenius and coworkers included non-
suppressed water signal from the spectroscopic volume
as well as an ANN analysis and showed an accuracy
of 82% for classification according to brain tumour
type and grade [38]. Although neural network based
approaches are typically ‘black box’ approaches, ‘reso-
nance profiles’ provided by ANN analyses for tumour
classification closely resemble MR spectral patterns,
aiding the identification of metabolites with key
discriminatory weight for a given histological tissue
type [39]. Subsequent studies have confirmed the good
performance of
1
H MRS to classify brain tumours
[40–42].
Recently, techniques to decompose the
1

H MR spec-
tra into biologically meaningful components have been
introduced. One powerful technique to this end is the
independent component analysis (ICA) [43]. Biological
systems, such as brain tumours, are regarded as linear
combinations of spectra from different tissue (cell)
types within the voxel. Using ICA for
1
H MRSI data
it was observed that spectra from seven distinct histo-
logical brain tumour types can be described by maxi-
mally four ICA components (Fig. 1A, for an example)
[44]. Available ICA algorithms are capable of handling
standard in vivo MRS data which still possess signifi-
cant unavoidable variation in signal-to-noise ratio, line
width and line shape within the data matrix (Fig. 1A).
Using these components images were generated for the
distribution of these IC types within each tumour
(Fig. 1B). This type of information may turn out to be
clinically relevant, as it may show the growth pattern
of tumour in situ, as well as being able to distinguish
high grade gliomas [44].
Impact of
1
H MRS information in clinical manage-
ment of brain tumour patients is increasing [25]. A
concerted European network has introduced a compu-
ter-based decision supporting system for clinical diag-
nosis of brain tumours [45]. The goal of this project is
to develop a fully automated system using

1
H MRS(I)
data acquired with any of the commercial clinical scan-
ners as input for diagnosis of brain tumours [42]. It
has become evident that there are additional relevant
aspects available from
1
H MRS data for patient man-
agement. It has been shown that the volume of meta-
bolic abnormality in
1
H MRSI [46] and presence of
1
H MRS lipids in tumour tissue provide prognostic
information [47].
1
H MRS distribution of CCM, Cre
and Lac ⁄ Lip [47,48] and the presence of specific IC
components above [44] are indicative for brain tumour
invasiveness, which can be used for individual therapy
planning. Furthermore, spectroscopy data are used to
J. L. Griffin and R. A. Kauppinen A metabolomics perspective of brain tumours
FEBS Journal 274 (2007) 1132–1139 ª 2007 The Authors Journal compilation ª 2007 FEBS 1135
assess response to therapy allowing adjustment of
treatment protocol [25].
13
C MRS
13
C MRS is a powerful technique for metabolic assess-
ment of tumours, because both glycolytic and oxida-

tive metabolism of glucose can be estimated in the
same experiment. The switch from oxidative to ‘anabo-
lic’ glucose metabolism (involving glucose carbon
shunting for nucleic acid synthesis) is one of the char-
acteristics of cancer cells [49]. Until now
13
C MRS has
been used only in experimental brain tumours [50,51].
However, the approach provides a wealth of informa-
tion such as the metabolic activity of the lactate pool,
the intracellular location of this pool and the relative
rates of glycolysis and oxidative metabolism in these
tumours [49–51].
Paediatric brain tumours
Brain tumours in paediatric patients are proportionally
much more common malignancies diagnosed in this age
group than those in adults. A large body of paediatric
brain tumours show low degree of malignancy and
therefore respond to therapy, but their anatomical local-
ization, often adjacent to vital structures, makes diagno-
sis challenging. Histologically similar tumour types to
those in adults, such as benign and malignant astrocyto-
mas, and dissimilar ones, such as primitive neuro-
ectodermal tumours (PNET), neuroblastomas and
retinobaslatomas, are found. What has been found
metabolically by
1
H MRS from adult brain tumours
appears to hold also for paediatric cases. It is interesting
to note that paediatric brain tumours, irrespective of

originating cell type, show absence of NAA [27,52,53].
This indicates that only differentiated neural cells are
able to express NAA. Low Cre and high CCM are asso-
ciated with high grade of tumour [27,53,54]. Consistent
with adult brain tumour studies, decline in CCM and
appearance of Lip are signs of response to therapy [53].
A recent study of paediatric brain tumour patients
demonstrated that more detailed biochemical informa-
tion from CCMs by
31
P MRS can aid in assessment of
prognosis [27]. High CCM detected by
1
H MRS in a
variety of paediatric tumour types and grades can be
analysed at the level of individual phosphorylated cho-
line moiety containing compounds by
1
H-decoupled
31
P MRS. It was observed that PC ⁄ GPC and PE ⁄ GPE
ratios are very high in PNET relative to several other
tumours [27]. This pattern of large phosphomonoester
content has been implicated to highly malignant
tumours [26], and thus, multinuclear MRS may be
Cho
(a)
(b)
(c)
(d)

(e)
B
A
C
Cre
Naa
Lac/Lip
2.0 1.0 0 p.p.m.
3.0
Fig. 1. (A)
1
H MRS spectrum of a human glioblastoma (a), a calcula-
ted composite spectrum (b) and three independent components
(IC) (c–e) obtained from the acquired spectrum using the ICA are
shown. Components contain metabolites as follows: IC-c, Lac ⁄ Lip
only; IC-d, Choline containing compounds (Cho), Cre and small NAA
and Lac ⁄ Lip peaks; and IC-e, Cho, Cre and NAA. (B) A topographic
distribution of IC-d and (C) of IC-c from
1
H MRSI data set are
shown superimposed on a Gd-enhanced T1-weighted MR image.
Reproduced with permission from [44].
A metabolomics perspective of brain tumours J. L. Griffin and R. A. Kauppinen
1136 FEBS Journal 274 (2007) 1132–1139 ª 2007 The Authors Journal compilation ª 2007 FEBS
able to provide accurate diagnostic and prognostic
information.
Future directions
Aspirations of molecular medicine MRS is advancing
translation of metabolonomics into clinical manage-
ment of brain tumour patients. In several specialized

centres
1
H MRS(I), by complementing advanced MRI
examinations, are used in diagnosis, therapy planning
and treatment follow-up [25,27,54]. It is envisaged that
the need for invasive diagnostic biopsies will inevitably
decline. This development can be regarded as logic in
the flow of new methods for tumour diagnosis. In the
pursuit morphological analysis using histological meth-
ods has been complemented with, or even replaced by,
immunological analysis of tumour types. This step has
made classification of tumours more accurate and spe-
cific. More recently, gene and protein expression pro-
files have been added to tumour typing. We believe the
metabolomics approach, involving not only
1
H MRS,
but also
31
P and
13
C MRS in vivo, will become a field
in its own right to be used for diagnostic, treatment
planning, and monitoring treatment of these devasta-
ting tumours. The current direction of increasing the
field strength of clinical magnets improves both sensi-
tivity of detecting metabolites and spectral resolution.
New data acquisition methods, including parallel ima-
ging [55] and nuclear hyperpolarization techniques for
13

C of metabolic substrates [56] will speed up MRS
measurements.
Finally, MS will increasingly play a role in ex vivo
cancer metabolomics. One exciting possibility for met-
abolomic based histology is to perform MALDI MS
to produce an image of a tissue section which repre-
sents certain metabolites. This is already being used in
cancer cell proteomics as well as certain metabolomic
experiments [57].
Acknowledgements
Supported by the Royal Society (JLG), the Finnish
Cancer Foundation (RAK) and Academy of Finland
(RAK).
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