Inherent and specific metabolic profiles of major brain tumour cell types, as determined by proton nuclear magnetic resonance spectroscopy 1H MRS, have also been used to define metabolite p
Trang 1A metabolomics perspective of human brain tumours
Julian L Griffin1and Risto A Kauppinen2
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 treatassess-ment 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: jlg40@mole.bio.cam.ac.uk
(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 (1H 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.
Trang 2Metabolite 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 1H MRS metabolite
profiles Using a subgroup of eight metabolites (from a
total number of 30 identifiable ones) quantified by
1H 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 1H 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 + phosphocreatcreat-ine), cholcreat-ine-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,1H 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.
Alanine Increases in hypoxic tissues as a result of increased
glycolysis.
Brain tumors including astrocytomas, metastases, gliomas, meningiomas, and dysembryoplastic neuroepithelial tumors.
CH3& CH2lipids 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.
Trang 3metabolome Most applications use prior
chromatogra-phy with gas chromatograchromatogra-phy (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 1H MR
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 1H 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 inhibiinduc-tion of apoptosis
The metabolite data sets from1H 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
31P MRS
31P 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 1H MRS alone Qualitative inspection of brain tumour 31P MR spectra indicated small differ-ences in spectral appearances between normal brain
Trang 4and gliomas [28] Quantitative analysis of 31P MR
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]
31P MRS has also been used to observe tumour
responses to drug and radiation therapies [29]
1H MRS
Metabolomics in vivo using 1H 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 of1H MRS limits the
num-ber of metabolites detected Despite these factors
1H 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 1H 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 1H MRS signals
apart from Lac and macromolecules [32,33] In case of
infectious lesions1H 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
1H MRS will remain confirmatory for these cases
A wealth of 1H MR spectroscopic data from brain
tumours shows that both tumour types and tumour
grades have characteristic spectral patterns The idea
of looking at the1H 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
1H MR spectra using the entire spectral width to
clas-sify human glial tumours [37] A concept of 1H MRS
profiles was introduced Soon afterwards a concept of
‘1H MRS metabolic phenotype’ was coined by Usenius
et al [38] and Preul et al [39] In these papers
simpli-fied 1H 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 1H 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 1H MRS to classify brain tumours [40–42]
Recently, techniques to decompose the1H 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 1H 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 1H 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 1H 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 1H MRS data for patient man-agement It has been shown that the volume of meta-bolic abnormality in 1H MRSI [46] and presence of
1H MRS lipids in tumour tissue provide prognostic information [47] 1H 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
Trang 5assess response to therapy allowing adjustment of treatment protocol [25]
13C MRS
13C 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 13C 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 1H 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 by31P MRS can aid in assessment of prognosis [27] High CCM detected by 1H 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 1H-decoupled
31P 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
Lac/Lip
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].
Trang 6able 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 1H 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 1H MRS,
but also 31P and 13C 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
13C 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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