Brain metabolomic profiles of lung cancer patients prior to treatment characterized by proton magnetic resonance spectroscopy

Article (PDF Available)inInternational Journal of Clinical and Experimental Medicine 5(2):154-64 · January 2012with32 Reads
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Abstract
Cancer patients without evidence of brain metastases often exhibit constitutional symptoms, cognitive dysfunction and mood changes at the time of clinical diagnosis, i.e. prior to surgical and/or chemotherapy treatment. At present however, there is limited information on brain metabolic and functional status in patients with systemic cancers such as lung cancer prior to initiation of treatment. Therefore, a prospective, observational study was conducted on patients with a clinical diagnosis of lung cancer to assess the cerebral metabolic status before treatment using proton magnetic resonance spectroscopy ((1)HMRS). Together with neurocognitive testing, (1)HMRS was performed in the parietal and occipital cortices of patients diagnosed with a lung mass (N=17) and an age-matched control group (N=15). Glutamate concentrations in the occipital cortex were found to be lower in the patients compared to controls and the concentrations of creatine and phosphocreatine were significantly lower in the parietal cortex of the patients. The lung cancer patients were also characterized by greater fatigue scores (but not depression) prior to treatment when compared to controls. In addition, the serum concentration of interleukin-6 (proinflammatory cytokine) was higher in patients compared to controls; and the concentration of tumor-necrosis factor alpha ([TNF-α]) was positively correlated to the metabolic activity of the lung tumor as defined by the 2-deoxy-2-((18)F)fluoro-D-glucose ((18)FDG) positron emission tomography (PET) derived maximal standardized uptake values (SUV(max)). Finally, multivariate statistical modeling revealed that the concentration of N-acetyl-aspartate [NAA] in the occipital cortex was negatively associated with [TNF-α]. In conclusion, our data demonstrate that the cerebral metabolic status of patients with lung cancer is changed even prior to treatment. In addition, the association between inflammatory cytokines, SUV(max) and [NAA] points towards interactions between the cancer's inherent metabolic activity, systemic subclinical inflammation and brain function.

Figures

Introduction
It is undisputed that cancer patients exhibit con-
stitutional symptoms (e.g. weight loss, loss of
appetite, fatigue and malaise) at the time of
clinical diagnosis [1-3]. However, whether or not
molecular signatures exist for these symptoms
is far less clear. Even less information is avail-
able as to how these symptoms and their mo-
lecular equivalents (if existent) relate to brain
metabolic function and ultimately, cancer out-
comes. The importance of a cancer’s direct ef-
fects on brain function is evidenced by recent
prospective studies showing that as many as
20% of cancer patients can experience cogni-
tive dysfunction and mood changes prior to
treatment [4-6].
Brain imaging techniques such as positron
emission tomography (PET) have documented
significant changes in the resting cerebral meta-
bolic rate of glucose (reflecting brain functional
Int J Clin Exp Med 2012;5(2):154-164
www.ijcem.com
/ISSN:1940-5901/IJCEM1202005
Original Article
Brain metabolomic profiles of lung cancer patients prior to
treatment characterized by proton magnetic resonance
spectroscopy
Helene Benveniste
1
, Shaonan Zhang
2
, Ruth A Reinsel
1
, Haifang Li
3
, Hedok Lee
1
, Mario Rebecchi
1
, William
Moore
3
, Christoffer Johansen
4
, Douglas L Rothman
5
, Thomas V Bilfinger
6
1
Departments of Anesthesiology and Radiology, Stony Brook Medicine, Stony Brook, NY, USA;
2
Department of Ap-
plied Mathematics & Statistics, Stony Brook University, NY, USA;
3
Department of Radiology,
Stony Brook University,
Stony Brook, NY, USA;
4
Department of Psychosocial Cancer Research, Institute of Cancer Epidemiology,
Copenhagen, Denmark;
5
Departments of Diagnostic Radiology and Biomedical Engineering, Yale University School of
Medicine, New Haven, CT, USA;
6
Department of Surgery, Stony Brook Medicine, Stony Brook, NY, USA
Received February 13, 2012; accepted February 25, 2012; Epub April 6, 2012; Published April 30, 2012
Abstract: Cancer patients without evidence of brain metastases often exhibit constitutional symptoms, cognitive dys-
function and mood changes at the time of clinical diagnosis, i.e. prior to surgical and/or chemotherapy treatment. At
present however, there is limited information on brain metabolic and functional status in patients with systemic can-
cers such as lung cancer prior to initiation of treatment. Therefore, a prospective, observational study was conducted
on patients with a clinical diagnosis of lung cancer to assess the cerebral metabolic status before treatment using
proton magnetic resonance spectroscopy (
1
HMRS). Together with neurocognitive testing,
1
HMRS was performed in
the parietal and occipital cortices of patients diagnosed with a lung mass (N=17) and an age-matched control group
(N=15). Glutamate concentrations in the occipital cortex were found to be lower in the patients compared to controls
and the concentrations of creatine and phosphocreatine were significantly lower in the parietal cortex of the patients.
The lung cancer patients were also characterized by greater fatigue scores (but not depression) prior to treatment
when compared to controls. In addition, the serum concentration of interleukin-6 (proinflammatory cytokine) was
higher in patients compared to controls; and the concentration of tumor-necrosis factor alpha ([TNF-α]) was positively
correlated to the metabolic activity of the lung tumor as defined by the 2-deoxy-2-(
18
F)fluoro-D-glucose (
18
FDG) posi-
tron emission tomography (PET) derived maximal standardized uptake values (SUV
max
). Finally, multivariate statistical
modeling revealed that the concentration of N-acetyl-aspartate [NAA] in the occipital cortex was negatively associated
with [TNF-α]. In conclusion, our data demonstrate that the cerebral metabolic status of patients with lung cancer is
changed even prior to treatment. In addition, the association between inflammatory cytokines, SUV
max
and [NAA]
points towards interactions between the cancer’s inherent metabolic activity, systemic subclinical inflammation and
brain function.
Keywords: Lung cancer, proton magnetic resonance spectroscopy, brain, glutamate, proinflammatory cytokines, fa-
tigue
Brain metabolomic profiles of lung cancer patients
155 Int J Clin Exp Med 2012;5(2):154-164
activity) in patients with primary brain cancer or
brain metastases and in breast cancer patients
although after chemotherapy treatment [7-9].
However, there is very limited information in the
literature on brain metabolic and functional
status in patients with systemic cancers such as
breast, prostate or lung cancer prior to treat-
ment. This gap in knowledge is likely related to
the complexity of conducting such studies in
newly diagnosed cancer patients (without evi-
dence of brain metastases), already over-
whelmed by what is perceived as a time sensi-
tive medical emergency with life threatening
implications, requiring diagnostic testing proce-
dures; and the often urgent need for surgical
intervention.
To our knowledge, no imaging studies have
documented brain metabolic status or neuro-
chemical profiles prior to treatment in patients
with lung cancer, although these patients typi-
cally present with significant constitutional
symptoms and often advanced disease. We
therefore conducted a study to investigate the
feasibility of characterizing brain metabolic
status by non-invasive proton magnetic reso-
nance spectroscopy (
1
HMRS) in parallel with
brief assessment of neurocognitive and mood
status as well as systemic inflammatory status
in patients with a clinical diagnosis of lung can-
cer before treatment. Non-invasive
1
HMRS al-
lows for an evaluation of the metabolic status of
the brain in real time by tracking levels of me-
tabolites involved in ‘energetics’ [10]. We hy-
pothesized that patients with malignant lung
cancers would display cerebral metabolic profile
changes in comparison to non-cancer controls.
Materials and methods
Subjects
Eligible patients for this prospective, observa-
tional study included those who were referred to
our surgical oncology clinic with a lung mass.
Age-matched controls were recruited from the
local community. We excluded subjects with
severe psychiatric and neurological illness, vi-
sion or hearing impairment, liver or kidney fail-
ure, addiction to drugs of abuse and those who
had been on any type of chemotherapy 6
months prior to study participation. All partici-
pants were subjected to the Mini-Cog and/or
Mini Mental Status Exam for dementia screen-
ing. A score of 24 or higher on the Mini Mental
Status Exam was required for eligibility. The
verbal IQ of the subjects was also assessed by
the Wechsler Test of Adult Reading. The sub-
jects gave written informed consent and the
study was approved by the local institutional
review board.
Experimental design and data collection
All patients underwent 1) anatomical magnetic
resonance imaging (MRI) followed by
1
HMRS, 2)
neurocognitive testing and 3) blood sampling
for analysis of two cytokines, TNF-α and inter-
leukin-6 (IL-6) prior to treatment; in parallel with
control subjects. Additional data collection for
all subjects included laboratory screening tests,
medical history and physical exam. For the pa-
tients the whole-body [
18
F]fluoro-2-deoxyglucose
positron emission tomography (
18
FDG PET)
scans and corresponding maximum standard-
ized uptake values (SUV
max
) of the lung mass
obtained during the clinical work up were also
acquired for analysis; as well as diagnosis of the
lung mass by pathology attained in conjunction
with surgical resection and/or biopsy.
1
HMRS scanning
MRI procedures for all patients and controls
were conducted on a 3.0T Philips whole body
scanner (Achieva system) equipped with a 12
channel phase array head coil; and included an
initial T1 weighted 3D anatomical scan acquired
with the following parameters: field of view
=240mm, repetition time =8.5 ms, echo time=4
ms, Flip Angle=8°, 1.0 mm slice thickness with
an acquisition matrix of 240×240, yielding a
reconstructed isotropic voxel dimension of
1.00mm
3
.
1
HMRS was performed by using a
point-resolved spectroscopy sequence acquired
in the parietal (15 x 15 x 15 mm
3
) and in the
occipital lobe (15 x 15 x 27 mm
3
) with short
echo time (32ms), repetition time of 2 seconds,
receiver bandwidth=2000Hz, number of
points=2048, and number of excitations = 256.
Both shimming and water suppression routines
were performed with automatic adjustments. A
water unsuppressed scan was used to perform
eddy current correction and to serve as a con-
centration reference for absolute quantification
of metabolite concentrations.
Spectral data analysis
Data analysis of
1
HMRS spectra was performed
Brain metabolomic profiles of lung cancer patients
156 Int J Clin Exp Med 2012;5(2):154-164
using linear combination modeling (LCModel
[11]) with prior knowledge of simulated spectral
signatures for the following brain metabolites:
Alanine, Aspartate, Creatine (Cr), Phospho-
creatine (PCr), γ-aminobutyric acid, Glucose,
Glutamine (Gln), Glutamate (Glu), Glycerophos-
phocholine (GPC), Phosphocholine (PCh), myo-
Inositol, Lactate, N-Acetyl-Aspartate (NAA), N-
acetyl-aspartyl-glutamate, Scyllo-inositol,
Taurine and Guanidoacetate; in addition to lipid
and macromolecules. No baseline correction,
zero-filling or apodization functions were applied
to the data prior to the analysis. Figure 1 shows
a typical processed
1
HMRS spectra from the
parietal and occipital cortices and the respec-
tive voxel positions from a control subject. A
quality analysis of all spectra was performed
and included evaluation of the signal-to-noise
ratio, spectral width (full-width half maximum),
baseline and residual tracings derived from the
LCModel analysis. All spectra with signal-to-
noise ratio <8 and a full-width half maximum
Figure 1. Localized
1
HMRS
spectra processed by
LCModel software from the
occipital cortex (top) and
parietal cortex (bottom) with
the appropriate locations in
parietal and occipital cortex
shown on corresponding T1-
weighted MR images. Label-
ing of the spectral signa-
tures for glutamate (Glu), N-
Acetyl-Asparate (NAA), Glu-
tamate + Glutamine = GLX,
total choline (tCho), total
creatine (tCr), myo-inositol
(mI) and macromolecules
(MM). The raw (black) and
fitted (solid red line) spec-
trum as well as the remain-
ing baseline (stippled red
line) are shown in each of
the spectra.
Brain metabolomic profiles of lung cancer patients
157 Int J Clin Exp Med 2012;5(2):154-164
>0.080 ppm were considered of poor quality
and excluded from data analysis.
Neurocognitive testing
A brief test battery was administered by an ex-
perienced psychologist to all subjects. The do-
mains assessed were the following: Fatigue
(Profile of Mood States (POMS)) [12]; Depres-
sion (Profile of Mood States (POMS)) [12]; Long-
term memory (Hopkins Verbal Learning Test-
Revised); Short term memory (Digit Span) and
Psychomotor Learning (Digit Symbol Coding)
(WAIS-III) [13]; Verbal Fluency (semantic genera-
tion and response inhibition).
Analysis of blood for cytokines
Serum samples were prepared from freshly
drawn blood that had been permitted to clot for
25-30 min. The samples were flash frozen in
liquid N
2
and stored at -80 C until analysis.
(Quantikine and Quantikine HS immunoassay
kits, R&D Systems, Inc, MN) were used to meas-
ure the serum levels of each cytokine in the
samples according to the manufacturer’s in-
structions. The color intensities were read at
450 nm (IL-6) or 490 nm (TNF-α), corrected at
540 nm, or 640 nm, respectively, corrected to
the appropriate background absorbencies and
compared to the corresponding cytokine stan-
dard curve. The cytokine concentration in each
sample, expressed in pg/ml, was calculated
from the standard curve equation derived from
the linear fit to the standards. The assays had
confirmed sensitivities of 1 and 0.2 pg/ml for IL-
6 and TNF-α, respectively.
Statistical analysis
A two-sided independent t-test (Mann-Whitney-U
test without normality) or Fisher’s exact test,
where appropriate were used to examine group
differences in demographic and co-morbidity
parameters at baseline. The neurocognitive and
mood test scores between the two groups were
compared using the non-parametric Mann-
Whitney two-tailed test. Differences in cytokine
concentrations between the two groups; and
metabolite concentrations for each brain region
calculated by LCModel for the two groups were
analyzed by an independent t-test, and statisti-
cal significance was determined using a Type I
error threshold of 0.05. Differences in metabo-
lite concentrations between occipital and parie-
tal cortices within groups were assessed using a
paired, two-sided t-test. A multiple regression
analysis was performed on the metabolic activ-
ity of lung tumors (SUV
max
) and the correspond-
ing levels of proinflammatory cytokines. Finally,
the relation between inflammatory markers and
LCModel quantified metabolites concentrations
was explored using multivariate approach with a
stepwise selection to select the significant me-
tabolites in relation to the concentration of pro-
inflammatory markers. Analysis was conducted
using SAS software and XLSTAT (Version
2011.4.03).
Results
Subjects
A total of 37 subjects were assessed for eligibil-
ity; 17 controls (recruited from the local commu-
nity) and 20 patients referred to the surgical
oncology clinic with a lung mass. Two subjects
in the control group and three in the patient
group were unable to undergo MRI. One of the
patients was found to have two metastases in
the cerebellum. The demographics of the two
groups are listed in Table 1; and show no differ-
ences, in age, gender, educational level, fre-
quency of employment and verbal IQ. Differ-
ences in smoking history were significantly dif-
ferent between the groups. The average body
mass index of the patients was slightly higher
compared to controls at a significance level of
0.048 (Table 1). Table 2 shows comorbidity
data for the two groups and demonstrates that
the frequency of chronic obstructive lung dis-
ease (COPD) and diabetes mellitus was signifi-
cantly higher in the patient group when com-
pared to controls. All of the control subjects and
patients had hematocrits 35%, however the
average hematocrits of the patients was slightly
higher compared to controls (patient hema-
tocrits: 41.8% ± 2.9% versus control hema-
tocrits: 38.9% ± 3.8%, p=0.02) probably secon-
dary to the higher frequency of tobacco use in
the former group.
Pathology of lung tumors
Pathology revealed malignant lesions in 13 pa-
tients and benign lesions in 4 (Table 3). Seventy
-seven percent of the patients with malignant
lung lesions received a histological diagnosis of
adenocarcinoma and 46% were classified as
stage I, 31% stage II and 15% stage III accord-
ing to the International Association for the Study
of Lung Cancer staging system [14, 15]. Only
Brain metabolomic profiles of lung cancer patients
158 Int J Clin Exp Med 2012;5(2):154-164
one of the patients with a malignant lung cancer
had evidence of metastases in the brain. Table
3 also shows the corresponding
18
FDG PET SU-
V
max
characterizing the metabolic activity of the
lung lesion, used to assess potential malignancy
of the lung lesion prior to treatment.
Mood status and cognitive performance
Analysis of POMS fatigue scores were higher in
patients compared to controls (Patients: 7.3 ±
4.2 versus Controls: 3.5 ± 3.3, p=0.009). How-
ever, there were no group differences in POMS
depression scores (p=0.18). Table 4 shows neu-
rocognitive performance in patients and con-
trols and demonstrates no differences in re-
gards to memory, learning and psychomotor
functioning between the two groups.
Brain metabolites
The average signal-to-noise ratio calculated
based on the NAA peak was ~13 and ~20 for
parietal and occipital spectra, respectively; and
the average full-width half maximum ~0.055
ppm. The average Cramer-Rao-Lower-Bounds
(CRLB), reflective of reliability of measurements,
were consistently within acceptable range
(<20%) across subjects for analyzing the con-
centrations of the following metabolites: Gluta-
mate [Glu] (CRLB ~ 14%), N-acetyl-aspartate
[NAA] (CRLB ~ 5%), Glutamate + Glutamine
[Glu+Gln] (CRLB ~ 15%), Creatine + Phospho-
creatine [Cr + PCr] (CRLB ~3%) and the total
choline containing compounds Glycerophospho-
choline and Phosphocholine [GPC + PCh] (CRLB
~5).
In control subjects the concentrations of [Glu],
[NAA], [Cr+PCr] and [Glu+Gln] of the occipital
cortex was similar to that measured in the parie-
tal cortex; however [GPC+PCh] was higher in
parietal compared to occipital cortex (Table 5).
In the patient group [GPC+PCh] was also higher
in the parietal cortex when compared to occipi-
tal cortex; and in addition [NAA] was lower in the
parietal compared to occipital cortex (Table 5).
The quantitative analysis further demonstrated
that the occipital cortex [Glu] was significantly
lower in the patients when compared to controls
at baseline (Patients: 5.99 mM ± 0.78 versus
Controls: 7.00 ± 1.04 mM, p=0.011, Figure 2
and Table 5). This difference in [Glu] was
slightly more significant if patients with benign
lesions were excluded from the patient group
(p=0.003); and also remained significant if the
patient with brain metastases was excluded
(p=0.005). There were no differences in the
Table 1. Basic demographics and social history
Parameter Controls (N=15) Patients (N=17) P-value
Age (Mean ± SD) 61.9 ± 9.0 59.4 ± 11.3 0.49
Gender Male N, (%) 8, (53%) 8, (47%) 1.0
Female N, (%) 7, (47%) 9, (53%)
BMI 26.8 ± 3.1 30.1 ± 5.4 0.048
Smoking his-
tory
Active Smoker N, (%) 3, (20%) 10, (59%)
0.001**
Former Smoker N, (%) 2, (13%) 6, (35%)
Never Smoked N, (%) 10, (67%) 1, (6%)
Employed N, (%) 8, (53.3%) 8, (47%) 1.0
Education years (Mean ± SD) 15.6 ± 3.8 13.7 ± 2.7 0.18
Verbal IQ (estimated by WTAR) 108.9 ± 10.3 101.8 ± 11.9 0.09
Table 2. Co-Morbidity
Disease Controls (N=15) Pathints (N=16) P-value
COPD N, (%) 0, (0%) 6, (35.3%) 0.019*
Hypertension N, (%) 6, (40%) 11 (64.7%) 0.287
Cardiac Disease N, (%) 2, (13.3%) 6, (37.5%) 0.229
Thyroid Disease N, (%) 4, (26.7%) 4, (23.5%) 1.0
Diabetes Mellitus N, (%) 0, (0%) 7, (41.1%) 0.008**
Prior Cancer N, (%) 1, (6.7%) 3, (18.7%) 0.603
Brain metabolomic profiles of lung cancer patients
159 Int J Clin Exp Med 2012;5(2):154-164
concentrations of other occipital metabolites
([Cr+PCr], [NAA] and/or choline containing com-
pounds) between the two groups. Since it has
been documented that [Glu] in the brain is age-
dependent [16] we also performed an one-way
ANCOVA for [Glu] with age as a covariate includ-
ing all subjects. This analysis demonstrated no
age effect (p=0.92) but a significant group dif-
ference in [Glu] (p=0.02). As shown in Table 5,
in the parietal cortex, the concentrations of
[Cr+PCr] were lower in the patients compared to
controls (p=0.035); and [NAA] also trended to
be ~10% lower in the patients (p=0.09).
Proinflammatory markers, SUV
max
of the lung
mass and relation to brain metabolites
The proinflammatory cytokines TNF-α and IL-6
Table 3. Surgical pathology of lung tumors and corresponding SUV
max
values
Patient ID Surgical Pathology Staging
18
FDG SUV
max
P-001 Adenocarcinoma IIIa 9.0
P-011 Aspergilloma N/A 4.9
P-012 Carcinoid tumor N/A 2.7
P-013 Squamous cell carcinoma IIa 6.4
P-017 Squamous cell carcinoma IIb 7.7
P-018 Adenocarcinoma Ia 2.5
P-019 Adenocarcinoma Ib 1.0
P-020 Nodule of chronic inflammation N/A 3.6
P-021 Adenocarcinoma Ia 7.6
P-023* Adenocarcinoma IV 15.7
P-024 Adenocarcinoma IIb 6.0
P-026 Hamartoma N/A 0.0
P-027 Adenocarcinoma Ib 18.8
P-029 Adenocarcinoma IIIa 8.4
P-030 Adenocarcinoma Ia 1.6
P-036 Adenocarcinoma IIb 6.0
P-037 Squamous cell carcinoma Ib 13.6
*P-023 was found to have metastases in the cerebellum
Table 4. Mood status and cognitive performance
Measure Domain Control (N=15) Patient (N=17) P-value
Hopkins Verbal Learning Test-
Revised (% Retained)
Long-Term Memory 92.2 (20.3) 90.0 (19.5) 0.716
Digit Symbol Coding (Age ad-
justed)
Learning & Psychomotor
function
11.7 (3.2) 10.7 (3.2) 0.477
Digit Span (Age adjusted) Working Memory 11.1 (2.8) 10.3 (2.7) 0.434
Verbal Fluency Semantic Generation,
Response Inhibition
36.2 (9.7) 39.2 (12.8) 0.582
Data are presented as mean ± (SD)
Table 5. Neurochemical profile of controls and patients
Metabolite Control Patient Control Patient
Occipital Cortex
(N=12)
Occipital Cortex
(N=13)
Parietal Cortex
(N=11)
Parietal Cortex
(N=13)
[Glu] 7.00 (1.04) 5.99 (0.78)** 7.07 (1.13) 6.72 (1.52)
[NAA] 8.80 (0.72) 8.52 (0.37) 8.51 (0.86) 7.87 (0.97)
#
[GPC + CPh] 1.04 (0.15) 1.09 (0.23)
1.47 (0.24)
1.36 (0.25)
#
[Cr + PCr] 6.89 (0.55) 6.76 (0.54) 6.79 (0.66) 6.28 (0.46)*
[Glu + Gln] 7.40 (2.22) 6.38 (0.92) 7.50 (1.28) 7.17 (1.59)
Data are presented as mean and (SD). **P=0.011 (comparison of occipital [Glu] between controls and patients); *p=0.035
(comparison of parietal [Cr + PCr] between controls and patients);
p < 0.001 (comparison of occipital versus parietal metabolites
of control subjects); #p<0.02 (for comparison of occipital and parietal metabolites of patients)
Brain metabolomic profiles of lung cancer patients
160 Int J Clin Exp Med 2012;5(2):154-164
from the patients with malignancy were com-
pared with the controls without cancer. The av-
erage [TNF-α] of all patients was within normal
range reported in the literature (< 15 pg/ml,
[17]) and not different from controls. The serum
concentration of IL-6 ([IL-6], was very variable
among the patients but the average [IL-6] was
significantly higher compared to control sub-
jects (7.24 ± 6.83 pg/ml versus 2.23 ± 2.68
pg/ml, p=0.038).
A linear regression analysis revealed a trend
(though not statistically significant) towards a
positive relationship between [TNF-α] and SU-
V
max
(R
2
=0.299, p=0.066) and [IL-6] and SUV
max
(R
2
=0.24, p=0.12) suggesting that the greater
the metabolic activity of the lung mass the
greater the inflammatory response.
To examine the potential interaction between
inflammatory markers and brain metabolites we
performed a multiple regression analysis on [IL-
6] and [TNF-α] of all subjects (both patients and
controls) using all metabolites. A stepwise selec-
tion was used to select the significant metabo-
lites in relation to the magnitude of the inflam-
matory response. Interestingly, in the occipital
cortex, after stepwise selection a significant
relation between [NAA] and [TNF-α] was found.
The model derived was as follows: [TNF-α] = 15
- 1.5*[NAA] (p=0.036); indicating that an ele-
vated [TNF-α] will decrease occipital [NAA]
(Figure 3). No statistically significant relation-
ship between inflammatory markers and parie-
tal cortex metabolites were observed.
Figure 2. The scattergram shows occipital [Glu] from
individual subjects in the control and patient groups
and demonstrates that the mean concentration of
[Glu] in the patients is lower compared to controls.
The arrows point to two patients with the highest
levels of [Glu] who were both found to have benign
lung lesions.
Figure 3. A multiple regression
analysis was performed on [TNF
-α] of all subjects (both patients
and controls) using all metabo-
lites in occipital cortex. A step-
wise selection was used to se-
lect the significant metabolites
in relation to the magnitude of
the inflammatory response.
After stepwise selection a sig-
nificant relation between [NAA]
and [TNF-α] was found. The
model derived was as follows:
[TNF-α] = 15 - 1.5*[NAA]
(R
2
=0.193; p=0.036); indicat-
ing that an elevated [TNF-α] will
decrease occipital [NAA]. The
patients and controls are
marked by a red and blue cir-
cle, respectively.
Brain metabolomic profiles of lung cancer patients
161 Int J Clin Exp Med 2012;5(2):154-164
Discussion
Our data provide evidence that the cerebral
metabolic status of lung cancer patients is al-
tered prior to treatment when compared to an
age-matched control group. Specifically we
showed that [Glu] in the occipital cortex of the
lung cancer patients was approximately 10%
lower than controls; even in patients without
evidence of brain metastases. The lung cancer
patients were also characterized by more fa-
tigue and higher levels of IL-6 when compared
to controls.
To our knowledge, the finding of cerebral meta-
bolic status changes in lung cancer patients
prior to treatment is novel and not previously
documented. However, several prospective
studies have documented the effects of chemo-
therapy or hormone treatment on cognition and
revealed significant deterioration in neuropsy-
chological test scores when compared to base-
line performance in women with breast cancer
[18] and in men with prostate cancer [19, 20].
Functional MRI studies have also supported
evidence of cognitive dysfunction by demon-
strating that treatment results in differences of
task-related neural activation patterns in pa-
tients with prostate cancer in comparison to
controls [20]. Further, women treated with ta-
moxifen to reduce the risk of breast cancer dis-
play changes in brain metabolites including cho-
line containing compounds [21]. Measurements
of the cerebral metabolic rate of glucose using
18
FDG PET have also shown that patients with
breast cancer (but no metastases) can display
abnormalities following treatment [22]. Another
recent study demonstrated that 23% of patients
with breast cancer exhibit cognitive impairment
prior to treatment [23] suggesting that having a
diagnosis of cancer affects brain function. In our
patients the documented changes in occipital
[Glu] were not associated with changes in cogni-
tive performance in comparison to controls as
evaluated by the brief battery of neurocognitive
testing, possibly due to the fact that our groups
were well matched at baseline in terms of esti-
mated premorbid IQ (Wechsler Test of Adult
Reading test done at screening). However, the
lung cancer patients were characterized by
higher fatigue scores which have also previously
been demonstrated in cancer patients including
lung cancer [24-27].
Spectroscopy studies using combined proton
and
13
C-labeled precursors have shown that
[Glu] is directly related to neuronal mitochon-
drial metabolism [neuronal tricarboxylic acid
cycle (TCA) cycle rate (V
TCAn
)] in normal brain
[28, 29]. Further, a decrease in V
TCAn
in the eld-
erly has been shown to correlate with decreases
in [Glu] and [NAA], suggesting that mitochondria
lose oxidative capacity with normal aging [10,
30, 31]. If one accepts, that [Glu] represents
brain ‘energy metabolism’ and thereby indirectly
brain function, the decrease in [Glu] observed in
the lung cancer patients suggests that the pres-
ence of lung cancer itself reduces brain function
prior to treatment. However, it is important to
point out that the patients included in this study
were heterogeneous with respect to their final
lung mass diagnosis and included early as well
as more advanced stages of lung cancer. Due to
the small sample size it was not possible to cor-
relate the cerebral metabolomic status specifi-
cally with tumor staging; and since our sample
was dominated by early stage lung cancer pa-
tients it is likely that the overall impact of ‘lung
cancer’ on [Glu] and potentially other brain me-
tabolites may have been underestimated and
might prove more significant in patients with
more advanced disease. In support of this state-
ment, Figure 2 shows that the highest levels of
[Glu] of the patient group belonged to two of the
subjects with non-cancer. Future studies fo-
cused on characterizing a larger group of pa-
tients with lung cancer at various stages of pro-
gression will help address this issue.
It is important to also consider how the patient’s
other comorbidities might have interacted with
metabolism and influenced [Glu]. For example,
in contrast to controls a larger proportion of the
lung cancer patients were diagnosed with COPD
and it is possible therefore that this chronic con-
dition is responsible for the change in [Glu] at
baseline. However, none of the COPD patients
were oxygen dependent and all had normal he-
matocrit and oxygen saturation at baseline. Fur-
ther, a recent
1
HMRS study on oxygen-
dependent and oxygen-independent COPD pa-
tients did not reveal metabolic differences in
the brain when compared to controls [32], sup-
porting our main hypothesis that the metabolic
changes we observed in the cancer patients are
caused by the cancer and not by COPD. None-
theless, to further evaluate our preliminary find-
ings and ascertain their independence of co-
morbidities such as COPD, it will be essential to
enlarge our sample size and also include other
Brain metabolomic profiles of lung cancer patients
162 Int J Clin Exp Med 2012;5(2):154-164
cancer types.
Previous quantitative
1
HMRS studies of the nor-
mal human brain have documented a heteroge-
neous distribution of [NAA] and [GPC+PCh] in
the brain with higher [NAA] in the occipital cor-
tex when compared to the parietal and frontal
cortices; and higher [GPC+PCh] in the parietal
compared to occipital cortex [33]. In contrast,
[Glu] in grey matter is not region-dependent at
least in normal, young human brain [33]. In our
study, the quantitative profile of metabolites of
control subjects also revealed higher concentra-
tions of choline-containing compounds in the
parietal compared to the occipital cortex (Table
5); but we did not observe higher [NAA] in the
occipital compared to the parietal cortex as pre-
viously reported, which might be related to age-
differences of the two control populations [33].
We also did not observe a [Glu] decrease in the
parietal cortex of the patient group in compari-
son to controls, however, [NAA] trended to be
lower in the patients (p=0.09). The relatively
small sample size prohibits further interpreta-
tion and conclusions as to whether the effect of
‘lung mass’ or lung cancer exert a region-
specific or global effect on cerebral metabolic
status.
The measurements of proinflammatory cyto-
kines revealed higher [IL-6] in the patients with
a malignant lung mass compared to controls
which is in agreement with previous reports [17,
34]. The normal range of [IL-6] in human serum
is reported in the literature to be <15-20pg/ml
[34]; and has been shown to increase in pa-
tients with lung cancer, although the increase
varies greatly and is also dependent on tumor
type and stage [17, 34, 35]. In our study the
patient’s average [IL-6] was still within the re-
ported normal range probably because the ma-
jority of the patients were sampled at an early
diagnostic stage (i.e. stage I-II). In the patient
group, [TNF-α] was also within normal range
and no different from controls; which also indi-
cates the early stage of the cancer. When ex-
ploring the potential relation between the meta-
bolic activity of the lung mass as evaluated by
the SUV
max
and inflammation, we found a posi-
tive association (p=0.066) between [TNF-α] and
SUV
max
. This finding indirectly supports previous
data reporting that 1) TNF-α and other proin-
flammatory cytokines are produced locally in
lung cancers [35, 36] and 2) the metabolic ac-
tivity of the lung cancer is related to proliferative
tumor activity [37] and increased tumor cell
glycolysis secondary to local hypoxia [38].
It was intriguing that [TNF-α] was found to be
negatively associated with [NAA] in the occipital
cortex suggesting that ‘inflammation’(regardless
of cancer state) influences cerebral metabolic
status, in agreement with previous reports.
Thus, proinflammatory cytokines can cause cog-
nitive dysfunction [39-41]; and elevations of
cytokines have been found to co-occur with a
reduction of the metabolic rate of glucose utili-
zation in the brain [42-44].
Limitations of the study
The major limitation of the current study is the
small sample size and as such the data pre-
sented are preliminary. The enrollment of pa-
tients for the study was difficult due to the often
urgent need for extensive clinical work-up and
the patient’s emotional stress associated with
the diagnosis and imminent need for surgery.
The ability to perform
1
HMRS in conjunction
with
18
FDG-PET would be ideal for this patient
population since the study protocols could be
carried out with less of a time-burden for the
patient. This approach may be possible in the
future with implementation of a combined MRI-
PET imaging modality the clinical arena. It would
also be important to compare
1
HMRS results
with corresponding regional cerebral metabolic
rate of glucose data in order to obtain more
accurate spatial information. The latter will en-
able a better understanding of the neuronal
networks involved in cerebral effects associated
with cancer.
Acknowledgements
We would like to thank Sunday Campolo, RN
and April Frank, RN for their superb assistance
in recruiting the lung cancer patients from the
Surgical Oncology Clinic. We would also like to
thank all the patients who participated in our
study and who sacrificed their time prior to
treatment for the various study protocol proce-
dures including the neurocognitive test battery
as well as the MRI/
1
HMRS scans. Finally, we
would also like to acknowledge Dr. Sachin Jam-
bawalikar’s expertise in regard to
1
HMRS data
acquisitions as well as Dr. Joseph Conrad for
helping out with data collection. This work was
supported by funding from a Translational Re-
search Opportunity Grant from the School of
Brain metabolomic profiles of lung cancer patients
163 Int J Clin Exp Med 2012;5(2):154-164
Medicine, Stony Brook University.
Address correspondence to: Dr. Helene Benveniste,
Health Sciences Center Level 4-060, Stony Brook
Medicine, Stony Brook, NY 11794-8480 Tel: (631)
444 2358; Fax: (631) 444 2907; E-mail: helene.
benveniste@stonybrookmedicine.edu
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