SlideShare a Scribd company logo
Clinical article
Abbreviations  EGFR = epidermal growth factor receptor; GBM = glioblastoma; OS = overall survival; PFS = progression-free survival; TMZ = temozolomide.
SUBMITTED  March 18, 2016.  ACCEPTED  July 25, 2016.
include when citing  Published online October 14, 2016; DOI: 10.3171/2016.7.JNS16609.
*  Drs. Nahed and Dietrich contributed equally to this work.
Bone marrow response as a potential biomarker of
outcomes in glioblastoma patients
*Eugene J. Vaios, AB,1,5
Brian V. Nahed, MD, MSc,1,5
Alona Muzikansky, MA,2
Amir T. Fathi, MD,1,3
and Jorg Dietrich, MD, PhD1,4
1
Harvard Medical School; and Departments of 2
Biostatistics, 3
Hematology-Oncology, 4
Neuro-Oncology, and 5
Neurosurgery,
Massachusetts General Hospital, Boston, Massachusetts
Objective  Glioblastoma (GBM) is a highly aggressive malignancy that requires a multidisciplinary therapeutic ap-
proach of surgery, chemotherapy, and radiation therapy, but therapy is frequently limited by side effects. The most com-
mon adverse effect of chemotherapy with temozolomide (TMZ) is myelosuppression. It remains unclear whether the
degree of bone-marrow suppression might serve as a biomarker for treatment outcome. The aim of the current study
was to investigate whether the degree of bone-marrow toxicity in patients treated with TMZ correlates with overall sur-
vival (OS) and MRI-based time to progression (progression-free survival [PFS]).
Methods  Complete blood counts and clinical and imaging information were collected retrospectively from 86 cases
involving GBM patients who had completed both radiation therapy and at least 6 monthly cycles of chemotherapy with
TMZ.
Results  Using a multivariate Cox proportional hazard model, it was observed that MGMT promoter methylation,
wild-type EGFR, younger patient age at diagnosis, and treatment-induced decreases in white blood cell counts were
associated with improved OS. The 2-year survival rate was 25% and 58% for patients with increases and decreases,
respectively, in white blood cell counts from baseline over 6 months of TMZ treatment. Consistent with the literature, IDH
mutation and MGMT promoter methylation were associated with better PFS and OS. IDH mutation and MGMT promoter
methylation were not correlated with changes in peripheral red blood cell or white blood cell counts.
Conclusions  Decreases in white blood cell counts might serve as a potential biomarker for OS and PFS in malig-
nant glioma patients treated with radiation therapy and TMZ. It remains unclear whether treatment-induced changes in
white blood cell counts correlate with drug-induced antitumor activity or represent an independent factor of the altered
local and systemic tumor environment. Additional studies will be needed to determine dose dependence for chemothera-
py based upon peripheral blood counts.
http://thejns.org/doi/abs/10.3171/2016.7.JNS16609
Key Words  glioblastoma; temozolomide; bone marrow; biomarker; overall survival; oncology
G
lioblastoma (GBM) is the most common and
most aggressive primary brain tumor, with few
therapeutic advances over the last 20 years.1
The
standard of care includes surgery with a goal of maximal
safe resection, followed by a combination of radiation and
chemotherapy.16
Chemoradiation includes radiotherapy 5
days per week over 6 weeks in combination with daily
temozolomide (TMZ), followed by at least 6 monthly
cycles of TMZ administered on 5 consecutive days per
28-day cycle.8,16
Patients may continue on TMZ for up to
12 months at some institutions unless there are intolerable
side effects such as myelosuppression. Despite standard
©AANS, 2016 J Neurosurg  October 14, 2016 1
E. J. Vaios et al.
J Neurosurg  October 14, 20162
treatment, GBM remains incurable, with a median sur-
vival of less than 19 months and only a 30% probability
of survival 2 years after diagnosis.1,19
The genetic heterogeneity of GBM and the emergence
of various resistance mechanisms are proposed limita-
tions of standardized therapies.1,18
However, MGMT pro-
moter methylation and IDH1 mutation have been identi-
fied in genome-wide association studies as robust genetic
markers for improved clinical outcomes in patients treated
with standard chemoradiation.1,18
While IDH status is con-
sidered an independent biomarker for clinical outcomes,
MGMT promoter methylation is associated with improved
response to TMZ and radiation and thus serves as a pre-
dictive biomarker of more favorable treatment outcomes.
This observed relationship between the molecular-genetic
tumor signature and treatment response has elevated the
importance of noninvasive biomarkers that enable real-
time selection and adjustment of personalized therapies.
The unique role of the local and systemic tumor environ-
ment is increasingly recognized in overall tumor biology
and treatment outcome.18,23
The identification of a periph-
eral biomarker for treatment response and overall survival
(OS) would improve patient management and perhaps
alter chemotherapy (e.g., TMZ) dosing protocols, which
currently do not account for individual pharmacogenetics,
pharmacokinetics, or pharmacodynamics.2
The use of circulating blood counts as a marker of
drug activity and clinical outcomes is an emerging area
of investigation. The current understanding of the effect
of chemotherapeutic agents suggests that greater toxicity
to various organ systems, such as the bone marrow, might
reflect increased potency and, conversely, be associated
with a more favorable antitumor profile.11,12
For instance,
in patients with renal cell carcinoma, leukopenia induced
by the tyrosine kinase inhibitor sunitinib was identified
as an independent and prognostic marker for improved
response rate and progression-free survival (PFS).4
This
finding was further supported by work from Zhu et al.,24
suggesting that sunitinib-induced decreases in neutro-
phils, monocytes, and platelets were associated with im-
proved progression and survival outcomes in patients with
hepatocellular carcinoma.
TMZ therapy, in combination with radiotherapy, sig-
nificantly improves OS in GBM patients.15
However, dos-
ing is based solely on patient body surface area and does
not account for variability in resistance mechanisms and
drug metabolism, raising the possibility that some patients
may be dosed subtherapeutically. Inadequate dosing may
partially contribute to the observed resistance to cyto-
toxic treatment and ultimately tumor recurrence. One of
the most common adverse effects of chemotherapy with
TMZ is myelosuppression, including thrombocytopenia
and leukopenia. It remains unclear whether patients who
do not show a notable decrease in blood counts in re-
sponse to TMZ are treated subtherapeutically. Therefore,
we hypothesized that changes in circulating blood counts
may be predictive of clinical outcomes, with alterations in
blood counts indicative of myelosuppression being associ-
ated with improved OS and PFS.
Methods
We conducted a retrospective, chart-review analysis of
clinical and demographic data from patients who previ-
ously underwent surgery and treatment for primary GBM
at the Massachusetts General Hospital between 2007 and
2014. Patient data were obtained from a Massachusetts
General Hospital institutional database. This time interval
allowed enough time for diagnosis and to follow a com-
plete blood count throughout the patient’s disease course.
This study received institutional review board approval
from Massachusetts General Hospital for all activities.
Eligibility
All patients were treated at the Massachusetts General
Hospital and met the following eligibility criteria: newly
diagnosed with GBM (WHO Grade IV) between Janu-
ary 1, 2007, and July 30, 2014; 18 years of age or older at
the time of diagnosis; surgical biopsy/resection after ini-
tial presentation; and treatment with at least 6 cycles of
monthly TMZ. Patients who did not complete 6 months
of TMZ therapy for any reason were excluded from the
study.
Variables
Descriptive information, including age, sex, and steroid
use, was collected. Steroid use was defined as exposure to
steroids (e.g., dexamethasone) at any given time during the
course of chemotherapy. Genetic information including
chromosomal abnormalities, point mutations, and gene
methylation was recorded. This included known prognos-
tic markers for gliomas such as epidermal growth factor
receptor (EGFR) amplification, MGMT promoter methyl-
ation, IDH mutation, and 1p/19q co-deletion. The genetic
characteristics of the sample are reported as the percent-
age of those patients for whom that genetic variable was
tested. Absolute peripheral blood platelet, red blood cell,
white blood cell, eosinophil, basophil, lymphocyte, neu-
trophil, and monocyte count measurements were recorded
at a maximum of 15 discrete time points during the course
of treatment. Time points included before surgery, after
surgery, before chemoradiation, and before each monthly
TMZ treatment cycle. PFS and OS were also assessed.
Statistics
The primary outcome measure was OS, which was de-
fined as the length of time from the date of initial diagno-
sis to time of death or last date known to be alive for those
who were censored. The secondary outcome measure was
PFS, which was defined as the length of time from initial
diagnosis to the time of first progression based on radiol-
ogy report and clinician notes indicating a switch in ther-
apy or last date known to be progression free for censored
patients. The effect of changes in peripheral blood counts
on clinical outcomes was assessed during the interval
between baseline measurement (before chemoradiation)
and Cycle 6 of monthly TMZ, as this time interval had
the greatest sample size and was controlled for the im-
munosuppressive effect of steroid use during surgery and
chemoradiation. Baseline and 6-month blood counts were
Bone marrow response as a potential biomarker
J Neurosurg  October 14, 2016 3
performed closest to the time of chemoradiation or TMZ
initiation, but no earlier than 2 weeks prior. Changes in
blood counts are reported as the percentage change from
baseline to Cycle 6 of monthly TMZ.
Genetic variables known to be strong prognostic mark-
ers were compared with clinical outcomes using logistic
regression or Pearson’s chi-square test. Spearman or Pear-
son correlation coefficients were estimated to measure
the relation between baseline demographic variables and
clinical outcome measures. Wilcoxon rank-sum tests and
Kruskal-Wallis tests by ranks were used to examine dif-
ferences in mean blood count changes between groups
of patients stratified by IDH mutation and MGMT pro-
moter methylation. Univariate and multivariate Cox pro-
portional hazards models were used to evaluate variables
for association with PFS and OS. Variables were cho-
sen for multivariate analysis using the backward selec-
tion method based on statistical significance in univari-
ate analysis. Percentage changes from baseline in white
blood cell and red blood cell counts were included in the
multivariate model as dichotomized variables, indicating
either an increase or a decrease in that blood count from
baseline. IDH mutation was excluded from the multivari-
ate analysis due to a sample size less than 10. A Wilcoxon
rank-sum test was performed to assess the association be-
tween changes in neutrophils and steroid use. Neutrophil
counts at Cycle 6 of TMZ were compared between pa-
tients with and without steroid treatment using a 2-sample
t-test. Changes in neutrophil counts were excluded from
the multivariate analysis as they were confounded by ste-
roid use. Survival probabilities were compared between
patient groups, stratified by the dichotomous white blood
cell variable, using the log-rank test. In subgroup analy-
sis, patients with a decreased peripheral white blood cell
count from baseline were subdivided into groups based on
the degree of change, using either quartiles or the median
decrease. Here too the log-rank test was used to compare
survival curves between these patient subgroups. All re-
ported p values were 2-sided, and statistical significance
was considered as p < 0.05.
Results
Descriptive Data Analysis
In total, 86 patients diagnosed with GBM were in-
cluded in this study. Their median age at diagnosis was 55
years; 32 patients (37%) were women and 54 (63%) were
men. Nineteen patients (22%) were still alive at the time
of data cutoff for analysis. The median OS for the entire
group was 800 days, and the median PFS was 453 days.
Baseline and 6-month peripheral blood counts are listed
in Table 1. Mutation frequencies in the patient cohort are
reported in Table 2.
Univariate and Multivariate Analysis of Biomarker Impact
on OS
By univariate analysis, MGMT promoter methylation
and IDH mutation were associated with better OS and
PFS, consistent with the literature (Table 3 and Supple-
mental Table 1A). EGFR amplification, changes in neutro-
phil counts, changes in peripheral red and white blood cell
parameters, steroid use, and patient age at diagnosis were
also associated with OS (Table 3). As predicted, patients
receiving steroids during TMZ therapy had worse OS, with
a 2-year survival rate of 51% compared with 71% in pa-
tients who were not treated with steroids (p = 0.0051). Ad-
ditionally, changes in neutrophil counts differed between
patients with and without steroid use (p = 0.0032), and
patients receiving steroids had significantly greater neutro-
phil counts at 6 months of TMZ compared with those who
never received steroids (p = 0.0031). Changes in neutrophil
counts were not associated with OS in patients who never
received steroids (p = 0.300). On multivariate analysis,
MGMT promoter methylation, a decreased white blood
cell count from baseline, and wild-type EGFR status (i.e.,
EGFR not amplified) were significantly associated with
improved OS (Table 4). These associations remained sig-
nificant on multivariate analysis that incorporated patient
age and steroid use, despite our observation that steroid use
was associated with increased white blood cells counts (p
= 0.0585). On subgroup analysis, decreases in white blood
cell counts remained associated with improved OS with
hazard ratios of 0.410 (p = 0.053) and 0.109 (p = 0.066) in
patients with and without steroid use.
Association of MGMT and IDH With Alterations in
Circulating Blood Cell Counts
MGMT promoter methylation and IDH1 mutation are
known to be robust prognostic markers for OS in GBM pa-
tients.1,18
Regarding the association of these genetic mark-
ers with alterations in circulating biomarkers that were
significant on univariate analysis, we report that MGMT
promoter methylation was not correlated with changes in
peripheral red blood cell or white blood cell (p = 0.4492)
counts. Similarly, IDH mutation was not correlated with
changes in peripheral red blood cell (p = 0.2198) or white
blood cell (p = 0.3447) counts during the course of treat-
ment.
Association of Changes in White Blood Cell Counts
With OS
The Kaplan-Meier estimated 2-year survival rate for
patients with a decrease in white blood cell counts from
baseline was 58% and that for patients with an increase
relative to baseline was 25% (p = 0.0019; Fig. 1). Patients
with decreases in white blood cell counts had a median
OS of 850 days (95% CI 691–1097 days) compared with
627 days (95% CI 454–745 days) for those with increases
in white blood cell counts from baseline. We found no sig-
nificant differences in OS between subgroups of patients
with decreased white blood cell counts during treatment,
when the data were stratified by quartile or median de-
crease.
Discussion
We here demonstrate that treatment-associated my-
elosuppression, as manifested by a decrease in circulat-
ing white blood cell counts from baseline during adjuvant
E. J. Vaios et al.
J Neurosurg  October 14, 20164
TMZ therapy, might serve as a potential prognostic mark-
er for clinical outcomes in patients with GBM. Our se-
rial assessment of peripheral blood counts and additional
laboratory and radiographic data found that a decrease
in white blood cells from baseline during adjuvant TMZ
therapy predicts significantly improved OS.
Our institutional survival data for patients with de-
creases in white blood cells compares favorably with data
from the original EORTC/NCIC trial, which reported a
median survival of 14.6 months and a 2-year survival rate
of 26.5% for patients receiving radiotherapy plus TMZ.16
The present findings are consistent with findings of pre-
vious studies demonstrating an association of MGMT
promoter methylation and IDH1 mutation with improved
clinical outcomes.3,5,6,17,21
Interestingly, we found that
MGMT promoter methylation and IDH mutation did not
correlate with changes in white blood cell counts, suggest-
ing that these changes may serve as an independent prog-
nostic factor.
Despite these robust findings, our study is limited by
its modest sample size and retrospective nature. Neverthe-
less, our exploratory analysis of hematological parameters
identified that treatment-related changes in white blood
cell counts are associated with OS, with decreases in white
blood cell counts from baseline serving as a biomarker for
improved OS. This association was maintained on mul-
tivariate analysis, even after controlling for other known
prognostic variables, including age at the time of diagno-
sis, steroid use, EGFR amplification status, and MGMT
promoter methylation status. On subgroup analysis, de-
creases in white blood cell counts from baseline main-
tained a strong association with improved OS regardless of
whether a patient used steroids during TMZ therapy. No-
tably, an increase in white blood cell counts from baseline
was also considered an important biomarker for worse OS,
suggesting that changes in white blood cell counts play an
TABLE 2. Summary of patient characteristics
Characteristic Value
Sex
 Male
 Female
54 (63%)
32 (37%)
Age at diagnosis, yrs
  Mean (SD)
 Range
55.37 (12.55)
18.00–80.00
OS, days
  Mean (SD)
 Range
915.09 (476.37)
324.00–2660.00
PFS, days
  Mean (SD)
 Range
622.10 (487.02)
12.00–2660.00
Genetic mutation
  EGFR
 MGMT
 IDH
37 (50.00%)
39 (54.17%)
6 (8.96%)
Steroid use
Deceased
62 (72.09%)
67 (77.91%)
Values represent n (%) unless otherwise indicated.
TABLE 1. Patient blood counts
Hematology Value
Baseline
  Platelets (×109
/L)
  Mean (SD)
  Range
279.28 (97.59)
95.00–589.00
  Red blood cells (×1012
/L)
  Mean (SD)
  Range
4.28 (0.43)
3.27–5.16
  White blood cells (×109
/L)
  Mean (SD)
  Range
8.69 (3.29)
3.27–20.00
  Eosinophils (×109
/L)
  Mean (SD)
  Range
0.11 (0.11)
0.00–0.58
  Basophils (×109
/L)
  Mean (SD)
  Range
0.03 (0.03)
0.00–0.20
  Lymphocytes (×109
/L)
  Mean (SD)
  Range
1.67 (0.73)
0.52–3.93
  Neutrophils (×109
/L)
  Mean (SD)
  Range
6.21 (2.90)
2.16–15.43
  Monocytes (×109
/L)
  Mean (SD)
  Range
0.46 (0.24)
0.14–1.51
6-mo adjuvant TMZ*
  Platelets (×109
/L)
  Mean (SD)
  Range
188.74 (65.76)
80.00–400.00
  Red blood cells (×1012
/L)
  Mean (SD)
  Range
4.13 (0.45)
3.24–5.29
  White blood cells (×109
/L)
  Mean (SD)
  Range
5.68 (2.50)
3.00–14.10
  Eosinophils (×109
/L)
  Mean (SD)
  Range
0.12 (0.10)
0.00–0.45
  Basophils (×109
/L)
  Mean (SD)
  Range
0.02 (0.02)
0.00–0.15
  Lymphocytes (×109
/L)
  Mean (SD)
  Range
0.99 (0.41)
0.35–2.10
  Neutrophils (×109
/L)
  Mean (SD)
  Range
3.97 (2.21)
0.02–11.30
  Monocytes (×109
/L)
  Mean (SD)
  Range
0.40 (0.18)
0.02–1.02
*  Blood counts obtained at the time of the 6th monthly cycle of TMZ treatment.
Bone marrow response as a potential biomarker
J Neurosurg  October 14, 2016 5
important biological role in the tumor microenvironment
independent of chemotherapy.
We were unable to identify an association between
changes in the peripheral platelet count and clinical out-
comes, as described by Williams et al.22
This finding sug-
gests that decreases in white blood cell counts, rather than
general “bone marrow suppression,” might be a predictor
of improved OS. However, we did not observe a statis-
tically significant association between white blood cell
changes and PFS in our multivariate model, when con-
trolling for steroid use and other markers that were sig-
nificant on univariate analysis (Supplemental Data). Our
findings, however, indicate that patient age and MGMT
promoter methylation are important predictors of PFS.
Changes in neutrophils were also significantly associ-
ated with OS and PFS, with neutrophil increases from
baseline predicting worse outcomes. However, given the
association of neutrophil counts with medications (e.g.,
steroid use), infections, and environmental factors, these
findings remain more challenging to interpret. Neutrophil
counts were likely confounded by steroid use, perhaps in-
dicative of more aggressive tumor growth necessitating
steroid administration for the management of edema. Con-
sistent with the literature, our study found that steroid use
was associated with elevated neutrophil counts and worse
outcomes. In patients who did not receive steroids, we did
not observe an association between neutrophil counts and
OS. Therefore, this hematological parameter was excluded
from our multivariate analysis.
The observed relationship between a decrease in white
blood cell counts and clinical outcomes is possibly due to
higher in vivo drug concentrations of TMZ, resulting from
differences in drug metabolism. TMZ activity is dose and
schedule dependent and induces cytotoxicity primarily by
causing O6
-meG lesions, which deplete the repair enzyme
MGMT, leading to double-strand DNA breaks and tumor
cell apoptosis.7,13,14,25
TMZ is administered orally and has
a half-life of 1.8 hours, reaching concentrations in the CSF
that are 30%–40% of plasma concentrations. The clini-
cal efficacy of TMZ and its active metabolite depends on
MGMT activity; the integrity of the mismatch repair sys-
tem, which recognizes O6
-meG lesions in template DNA
strands; and function of the base excision repair system,
which corrects highly lethal N3
-meA lesions via poly
(ADP-ribose) polymerase.9,10,20
There are no guidelines for
dose adjustment based on the tumor genetic signature or
in the context of severe renal or hepatic impairment. Since
TABLE 3. Univariate analysis for OS
Covariate HR 95% CI p Value*
Sex
 Male
 Female
0.685
—
0.418–1.124
—
0.1346
—
Age 1.021 1.000–1.042 0.0470
Genetic mutations
  EGFR
 MGMT
 IDH
1.818
0.353
0.100
1.074–3.078
0.203–0.615
0.014–0.726
0.0261
0.0002
0.0228
Percent change in
 Platelets
  Red blood cells
  White blood cells
 Eosinophils
 Basophils
 Lymphocytes
 Neutrophils
 Monocytes
1.163
0.074
3.133
0.979
0.938
1.330
1.856
1.407
0.443–3.051
0.007–0.787
1.489–6.591
0.895–1.070
0.678–1.296
0.675–2.621
1.218–2.828
0.873–2.270
0.7588
0.0308
0.0026
0.6340
0.6973
0.4102
0.0040
0.1610
Steroids
 Used
  Not used
2.286
—
1.262–4.142 0.0064
—
*  Based on log-rank test.
TABLE 4. Multivariate analysis for OS
Covariate HR 95% CI p Value*
Age 1.064 1.033–1.096 <0.0001
MGMT 0.106 0.043–0.259 <0.0001
EGFR 1.779 0.978–3.247 0.0594
White blood cells
 Increase
 Decrease
3.040
—
1.245–7.407
—
0.0147
—
Red blood cells
 Increase
 Decrease
1.065
—
0.578–1.961
—
0.8390
—
Steroids
 Used
  Not used
1.196
—
0.575–2.494
—
0.6317
—
IDH was excluded due to inadequate sample size.
*  Based on log-rank test.
Fig. 1. Kaplan-Meier analysis of OS in patients stratified by increase
(dotted line) or decrease (solid line) in white blood cell counts from base-
line. A significant survival benefit is noted for patients with a decrease in
white blood cells relative to baseline (log-rank p = 0.0019).
E. J. Vaios et al.
J Neurosurg  October 14, 20166
current dosing guidelines do not factor interpatient differ-
ences in resistance mechanisms or patient-specific drug
metabolism, it is possible that some patients are treated
subtherapeutically.
Given the routine and reliable assessment of periph-
eral blood counts in GBM patients receiving conventional
therapies, white blood cell counts could serve as a valuable
biomarker of treatment response and for predicting clini-
cal outcomes. Future prospective studies should address
whether blood cell counts could serve as a correlate bio-
marker for in vivo TMZ levels and drug activity as well as
a predictor of clinical outcomes, which in turn could help
to optimize dosing and scheduling of chemotherapy by ac-
counting for variability in drug metabolism.
Conclusions
We report a temporal relationship between changes in
peripheral white blood cell counts during adjuvant TMZ
treatment and clinical outcomes. Specifically, depression
of white blood cell counts appears to be an independent
prognostic factor and was associated with improved OS.
This relationship may be a reflection of plasma TMZ levels
and, in time, may serve as a surrogate marker of therapeu-
tic efficacy. These findings warrant further investigation in
prospective studies, including correlations with the degree
of change in white blood cell counts and pharmacokinet-
ics of TMZ in individual patients. It also remains unclear
whether treatment-associated changes in white blood cell
counts correlate with drug-induced antitumor activity or
represent an independent factor of the altered local and
systemic tumor environment.
Acknowledgments
This work was supported by the 2015 Neurosurgical Research
and Education Foundation (NREF) Medical Student Summer
Research Fellowship (Eugene J. Vaios) and the Harvard Medical
School Scholars in Medicine Office (Eugene J. Vaios).
Jorg Dietrich received support from the American Academy of
Neurology, the American Cancer Society, and generous gifts from
the family foundations of Bryan Lockwood, Ronald Tawil, and
Sheila McPhee.
References
  1.	 Bastien JI, McNeill KA, Fine HA: Molecular
characterizations of glioblastoma, targeted therapy, and
clinical results to date. Cancer 121:502–516, 2015
  2.	 Ellingson BM, Wen PY, van den Bent MJ, Cloughesy TF:
Pros and cons of current brain tumor imaging. Neuro Oncol
16 (Suppl 7):vii2–vii11, 2014
  3.	 Esteller M, Garcia-Foncillas J, Andion E, Goodman SN,
Hidalgo OF, Vanaclocha V, et al: Inactivation of the DNA-
repair gene MGMT and the clinical response of gliomas to
alkylating agents. N Engl J Med 343:1350–1354, 2000
  4.	 Fujita T, Wakatabe Y, Matsumoto K, Tabata K, Yoshida K,
Iwamura M: Leukopenia as a biomarker of sunitinib outcome
in advanced renal cell carcinoma. Anticancer Res 34:3781–
3787, 2014
  5.	 Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet
N, Weller M, et al: MGMT gene silencing and benefit from
temozolomide in glioblastoma. N Engl J Med 352:997–1003,
2005
  6.	 Idbaih A, Omuro A, Ducray F, Hoang-Xuan K: Molecular
genetic markers as predictors of response to chemotherapy in
gliomas. Curr Opin Oncol 19:606–611, 2007
  7.	 Kanzawa T, Bedwell J, Kondo Y, Kondo S, Germano IM:
Inhibition of DNA repair for sensitizing resistant glioma cells
to temozolomide. J Neurosurg 99:1047–1052, 2003
  8.	 Lonardi S, Tosoni A, Brandes AA: Adjuvant chemotherapy
in the treatment of high grade gliomas. Cancer Treat Rev
31:79–89, 2005
  9.	 Marchesi F, Turriziani M, Tortorelli G, Avvisati G, Torino
F, De Vecchis L: Triazene compounds: mechanism of action
and related DNA repair systems. Pharmacol Res 56:275–
287, 2007
10.	 Miknyoczki S, Chang H, Grobelny J, Pritchard S, Worrell
C, McGann N, et al: The selective poly(ADP-ribose)
polymerase-1(2) inhibitor, CEP-8983, increases the sensitivity
of chemoresistant tumor cells to temozolomide and
irinotecan but does not potentiate myelotoxicity. Mol Cancer
Ther 6:2290–2302, 2007
11.	 Motzer RJ, Hutson TE, Tomczak P, Michaelson MD,
Bukowski RM, Oudard S, et al: Overall survival and updated
results for sunitinib compared with interferon alfa in patients
with metastatic renal cell carcinoma. J Clin Oncol 27:3584–
3590, 2009
12.	 Motzer RJ, Hutson TE, Tomczak P, Michaelson MD,
Bukowski RM, Rixe O, et al: Sunitinib versus interferon alfa
in metastatic renal-cell carcinoma. N Engl J Med 356:115–
124, 2007
13.	 Newlands ES, Stevens MF, Wedge SR, Wheelhouse RT,
Brock C: Temozolomide: a review of its discovery, chemical
properties, pre-clinical development and clinical trials.
Cancer Treat Rev 23:35–61, 1997
14.	 Roos WP, Batista LF, Naumann SC, Wick W, Weller M,
Menck CF, et al: Apoptosis in malignant glioma cells
triggered by the temozolomide-induced DNA lesion O6
-
methylguanine. Oncogene 26:186–197, 2007
15.	 Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn
MJ, Janzer RC, et al: Effects of radiotherapy with
concomitant and adjuvant temozolomide versus radiotherapy
alone on survival in glioblastoma in a randomised phase III
study: 5-year analysis of the EORTC-NCIC trial. Lancet
Oncol 10:459–466, 2009
16.	 Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher
B, Taphoorn MJ, et al: Radiotherapy plus concomitant and
adjuvant temozolomide for glioblastoma. N Engl J Med
352:987–996, 2005
17.	 Tabatabai G, Stupp R, van den Bent MJ, Hegi ME, Tonn JC,
Wick W, et al: Molecular diagnostics of gliomas: the clinical
perspective. Acta Neuropathol 120:585–592, 2010
18.	 Tanaka S, Louis DN, Curry WT, Batchelor TT, Dietrich J:
Diagnostic and therapeutic avenues for glioblastoma: no
longer a dead end? Nat Rev Clin Oncol 10:14–26, 2013
19.	 Thomas AA, Brennan CW, DeAngelis LM, Omuro AM:
Emerging therapies for glioblastoma. JAMA Neurol
71:1437–1444, 2014
20.	 Villano JL, Seery TE, Bressler LR: Temozolomide in
malignant gliomas: current use and future targets. Cancer
Chemother Pharmacol 64:647–655, 2009
21.	 von Deimling A, Korshunov A, Hartmann C: The next
generation of glioma biomarkers: MGMT methylation,
BRAF fusions and IDH1 mutations. Brain Pathol 21:74–87,
2011
22.	 Williams M, Liu ZW, Woolf D, Hargreaves S, Michalarea
V, Menashy R, et al: Change in platelet levels during
radiotherapy with concurrent and adjuvant temozolomide for
the treatment of glioblastoma: a novel prognostic factor for
survival. J Cancer Res Clin Oncol 138:1683–1688, 2012
23.	 Wilson TA, Karajannis MA, Harter DH: Glioblastoma
multiforme: State of the art and future therapeutics. Surg
Neurol Int 5:64, 2014
Bone marrow response as a potential biomarker
J Neurosurg  October 14, 2016 7
24.	 Zhu AX, Duda DG, Ancukiewicz M, di Tomaso E, Clark
JW, Miksad R, et al: Exploratory analysis of early toxicity
of sunitinib in advanced hepatocellular carcinoma patients:
kinetics and potential biomarker value. Clin Cancer Res
17:918–927, 2011
25.	 Ziegler DS, Kung AL, Kieran MW: Anti-apoptosis
mechanisms in malignant gliomas. J Clin Oncol 26:493–
500, 2008
Disclosures
The authors report no conflict of interest concerning the materi-
als or methods used in this study or the findings specified in this
paper.
Author Contributions
Conception and design: Vaios, Nahed, Dietrich. Acquisition of
data: Vaios. Analysis and interpretation of data: Vaios, Nahed,
Muzikansky, Dietrich. Drafting the article: Vaios. Critically revis-
ing the article: Vaios, Nahed, Fathi, Dietrich. Reviewed submitted
version of manuscript: all authors. Approved the final version of
the manuscript on behalf of all authors: Vaios. Statistical analysis:
Vaios, Muzikansky. Administrative/technical/material support:
Nahed, Dietrich. Study supervision: Nahed, Dietrich.
Supplemental Information
Online-Only Content
Supplemental material is available with the online version of the
article.
Supplemental Tables 1A and B. http://thejns.org/doi/
suppl/0.3171/2016.7.JNS16609.
Correspondence
Eugene John Vaios, Vanderbilt Hall Box 099, 107 Ave. Louis
Pasteur, Boston, MA 02115. email: eugene_vaios@hms.harvard.
edu.

More Related Content

What's hot

oligoblastic AML
oligoblastic AMLoligoblastic AML
oligoblastic AML
spa718
 
Multidisciplinary approach to the management of leukemias aml
Multidisciplinary approach to the management of leukemias    amlMultidisciplinary approach to the management of leukemias    aml
Multidisciplinary approach to the management of leukemias aml
madurai
 
Relapsed AML: Steve Kornblau
Relapsed AML: Steve KornblauRelapsed AML: Steve Kornblau
Relapsed AML: Steve Kornblau
spa718
 
Acute Promyelocytic Leukaemia
Acute Promyelocytic LeukaemiaAcute Promyelocytic Leukaemia
Acute Promyelocytic Leukaemia
Dr. Renesha Islam
 
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
PVI, PeerView Institute for Medical Education
 
8 jason westin
8 jason westin8 jason westin
8 jason westin
spa718
 
oligoblastic AML
oligoblastic AMLoligoblastic AML
oligoblastic AML
spa718
 
Mechanism of resistance to target therapy
Mechanism of resistance to target therapyMechanism of resistance to target therapy
Mechanism of resistance to target therapy
Vito Lorusso
 
Metastatic Castration Resistant Prostate Cancer(mCRPC)
Metastatic Castration Resistant Prostate Cancer(mCRPC)Metastatic Castration Resistant Prostate Cancer(mCRPC)
Metastatic Castration Resistant Prostate Cancer(mCRPC)
Ashfaq9697931281
 
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
Mohsin Maqbool
 
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTSTREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
spa718
 
Role of Stem cell transplant in Chronic Myeloid Leukemia in present era
Role of Stem cell transplant in Chronic Myeloid Leukemia in present eraRole of Stem cell transplant in Chronic Myeloid Leukemia in present era
Role of Stem cell transplant in Chronic Myeloid Leukemia in present era
Alok Gupta
 
AML: improving standard therapy
AML: improving standard therapyAML: improving standard therapy
AML: improving standard therapy
Pritish Chandra Patra
 
T cell
T cellT cell
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal CancerIntegration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
Mohamed Abdulla
 
Cancer Biomarkers
Cancer BiomarkersCancer Biomarkers
Cancer Biomarkers
Ravindra Chhabra
 
thalassemia
thalassemiathalassemia
thalassemia
spa718
 
Recent trends in genomic biomarkers pepgra healthcare
Recent trends in genomic biomarkers   pepgra healthcareRecent trends in genomic biomarkers   pepgra healthcare
Recent trends in genomic biomarkers pepgra healthcare
PEPGRA Healthcare
 
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
H. Jack West
 
Chapter 24.3 metronomic chemotherapy
Chapter 24.3 metronomic chemotherapyChapter 24.3 metronomic chemotherapy
Chapter 24.3 metronomic chemotherapy
Nilesh Kucha
 

What's hot (20)

oligoblastic AML
oligoblastic AMLoligoblastic AML
oligoblastic AML
 
Multidisciplinary approach to the management of leukemias aml
Multidisciplinary approach to the management of leukemias    amlMultidisciplinary approach to the management of leukemias    aml
Multidisciplinary approach to the management of leukemias aml
 
Relapsed AML: Steve Kornblau
Relapsed AML: Steve KornblauRelapsed AML: Steve Kornblau
Relapsed AML: Steve Kornblau
 
Acute Promyelocytic Leukaemia
Acute Promyelocytic LeukaemiaAcute Promyelocytic Leukaemia
Acute Promyelocytic Leukaemia
 
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
Pathology Insights on Innovation in AML: The Rapid Emergence of Precision Dia...
 
8 jason westin
8 jason westin8 jason westin
8 jason westin
 
oligoblastic AML
oligoblastic AMLoligoblastic AML
oligoblastic AML
 
Mechanism of resistance to target therapy
Mechanism of resistance to target therapyMechanism of resistance to target therapy
Mechanism of resistance to target therapy
 
Metastatic Castration Resistant Prostate Cancer(mCRPC)
Metastatic Castration Resistant Prostate Cancer(mCRPC)Metastatic Castration Resistant Prostate Cancer(mCRPC)
Metastatic Castration Resistant Prostate Cancer(mCRPC)
 
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
Effects and outcome of a policy of intermittent Imatinib treatment in elderly...
 
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTSTREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
TREATMENT OF NON-HIDGKIN'S LYMPHOMA IN ELDERLY PATIENTS
 
Role of Stem cell transplant in Chronic Myeloid Leukemia in present era
Role of Stem cell transplant in Chronic Myeloid Leukemia in present eraRole of Stem cell transplant in Chronic Myeloid Leukemia in present era
Role of Stem cell transplant in Chronic Myeloid Leukemia in present era
 
AML: improving standard therapy
AML: improving standard therapyAML: improving standard therapy
AML: improving standard therapy
 
T cell
T cellT cell
T cell
 
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal CancerIntegration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
Integration of NGS in Current & FutureTreatment Algorithm of Colorectal Cancer
 
Cancer Biomarkers
Cancer BiomarkersCancer Biomarkers
Cancer Biomarkers
 
thalassemia
thalassemiathalassemia
thalassemia
 
Recent trends in genomic biomarkers pepgra healthcare
Recent trends in genomic biomarkers   pepgra healthcareRecent trends in genomic biomarkers   pepgra healthcare
Recent trends in genomic biomarkers pepgra healthcare
 
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
Acquired Resistance to Targeted Therapy in EGFR and ALK-Positive Lung Cancer:...
 
Chapter 24.3 metronomic chemotherapy
Chapter 24.3 metronomic chemotherapyChapter 24.3 metronomic chemotherapy
Chapter 24.3 metronomic chemotherapy
 

Similar to JNS

Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
JohnJulie1
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
EditorSara
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
EditorSara
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
NainaAnon
 
Optimizing Chemotherapy
Optimizing Chemotherapy Optimizing Chemotherapy
METASTATC COLORECTAL CANCER IN 2017
METASTATC COLORECTAL CANCER IN 2017METASTATC COLORECTAL CANCER IN 2017
METASTATC COLORECTAL CANCER IN 2017
Mohamed Abdulla
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
daranisaha
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
eshaasini
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
semualkaira
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
semualkaira
 
Immuotherapy 2
Immuotherapy 2Immuotherapy 2
Immuotherapy 2
drmcbansal
 
A Glimpse at Precision Medicine in AML.
A Glimpse at Precision Medicine in AML.A Glimpse at Precision Medicine in AML.
A Glimpse at Precision Medicine in AML.
MarwaGamaleldin1
 
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
CrimsonpublishersCancer
 
LOW GRADE GLIOMA controversies in management
LOW GRADE GLIOMA controversies in managementLOW GRADE GLIOMA controversies in management
LOW GRADE GLIOMA controversies in management
Dr Praveen kumar tripathi
 
Targeted h& n
Targeted h& nTargeted h& n
Targeted h& n
dmtudtud
 
Oncol Lett Vol12 No6 Pg5043
Oncol Lett Vol12 No6 Pg5043Oncol Lett Vol12 No6 Pg5043
Oncol Lett Vol12 No6 Pg5043
Lenka Kellermann
 
Gluck-Clin Breast Cancer-2016
Gluck-Clin Breast Cancer-2016Gluck-Clin Breast Cancer-2016
Gluck-Clin Breast Cancer-2016
Greg Tardie, PhD
 
Sequencing in management of Multiple sclerosis
Sequencing in management of Multiple sclerosisSequencing in management of Multiple sclerosis
Sequencing in management of Multiple sclerosis
Amr Hassan
 
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
CristinaGeorgianaZah
 
Methotrexate in treatment of rheumatoid arthritis
Methotrexate in treatment of rheumatoid arthritisMethotrexate in treatment of rheumatoid arthritis
Methotrexate in treatment of rheumatoid arthritis
Nusrat Fatemee
 

Similar to JNS (20)

Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Optimizing Chemotherapy
Optimizing Chemotherapy Optimizing Chemotherapy
Optimizing Chemotherapy
 
METASTATC COLORECTAL CANCER IN 2017
METASTATC COLORECTAL CANCER IN 2017METASTATC COLORECTAL CANCER IN 2017
METASTATC COLORECTAL CANCER IN 2017
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
Clinic Correlation and Prognostic Value of P4HB and GRP78 Expression in Gastr...
 
Immuotherapy 2
Immuotherapy 2Immuotherapy 2
Immuotherapy 2
 
A Glimpse at Precision Medicine in AML.
A Glimpse at Precision Medicine in AML.A Glimpse at Precision Medicine in AML.
A Glimpse at Precision Medicine in AML.
 
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology_C...
 
LOW GRADE GLIOMA controversies in management
LOW GRADE GLIOMA controversies in managementLOW GRADE GLIOMA controversies in management
LOW GRADE GLIOMA controversies in management
 
Targeted h& n
Targeted h& nTargeted h& n
Targeted h& n
 
Oncol Lett Vol12 No6 Pg5043
Oncol Lett Vol12 No6 Pg5043Oncol Lett Vol12 No6 Pg5043
Oncol Lett Vol12 No6 Pg5043
 
Gluck-Clin Breast Cancer-2016
Gluck-Clin Breast Cancer-2016Gluck-Clin Breast Cancer-2016
Gluck-Clin Breast Cancer-2016
 
Sequencing in management of Multiple sclerosis
Sequencing in management of Multiple sclerosisSequencing in management of Multiple sclerosis
Sequencing in management of Multiple sclerosis
 
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
Total Body Irradiation or Chemotherapy Conditioning in Childhood ALL: A Multi...
 
Methotrexate in treatment of rheumatoid arthritis
Methotrexate in treatment of rheumatoid arthritisMethotrexate in treatment of rheumatoid arthritis
Methotrexate in treatment of rheumatoid arthritis
 

JNS

  • 1. Clinical article Abbreviations  EGFR = epidermal growth factor receptor; GBM = glioblastoma; OS = overall survival; PFS = progression-free survival; TMZ = temozolomide. SUBMITTED  March 18, 2016.  ACCEPTED  July 25, 2016. include when citing  Published online October 14, 2016; DOI: 10.3171/2016.7.JNS16609. *  Drs. Nahed and Dietrich contributed equally to this work. Bone marrow response as a potential biomarker of outcomes in glioblastoma patients *Eugene J. Vaios, AB,1,5 Brian V. Nahed, MD, MSc,1,5 Alona Muzikansky, MA,2 Amir T. Fathi, MD,1,3 and Jorg Dietrich, MD, PhD1,4 1 Harvard Medical School; and Departments of 2 Biostatistics, 3 Hematology-Oncology, 4 Neuro-Oncology, and 5 Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts Objective  Glioblastoma (GBM) is a highly aggressive malignancy that requires a multidisciplinary therapeutic ap- proach of surgery, chemotherapy, and radiation therapy, but therapy is frequently limited by side effects. The most com- mon adverse effect of chemotherapy with temozolomide (TMZ) is myelosuppression. It remains unclear whether the degree of bone-marrow suppression might serve as a biomarker for treatment outcome. The aim of the current study was to investigate whether the degree of bone-marrow toxicity in patients treated with TMZ correlates with overall sur- vival (OS) and MRI-based time to progression (progression-free survival [PFS]). Methods  Complete blood counts and clinical and imaging information were collected retrospectively from 86 cases involving GBM patients who had completed both radiation therapy and at least 6 monthly cycles of chemotherapy with TMZ. Results  Using a multivariate Cox proportional hazard model, it was observed that MGMT promoter methylation, wild-type EGFR, younger patient age at diagnosis, and treatment-induced decreases in white blood cell counts were associated with improved OS. The 2-year survival rate was 25% and 58% for patients with increases and decreases, respectively, in white blood cell counts from baseline over 6 months of TMZ treatment. Consistent with the literature, IDH mutation and MGMT promoter methylation were associated with better PFS and OS. IDH mutation and MGMT promoter methylation were not correlated with changes in peripheral red blood cell or white blood cell counts. Conclusions  Decreases in white blood cell counts might serve as a potential biomarker for OS and PFS in malig- nant glioma patients treated with radiation therapy and TMZ. It remains unclear whether treatment-induced changes in white blood cell counts correlate with drug-induced antitumor activity or represent an independent factor of the altered local and systemic tumor environment. Additional studies will be needed to determine dose dependence for chemothera- py based upon peripheral blood counts. http://thejns.org/doi/abs/10.3171/2016.7.JNS16609 Key Words  glioblastoma; temozolomide; bone marrow; biomarker; overall survival; oncology G lioblastoma (GBM) is the most common and most aggressive primary brain tumor, with few therapeutic advances over the last 20 years.1 The standard of care includes surgery with a goal of maximal safe resection, followed by a combination of radiation and chemotherapy.16 Chemoradiation includes radiotherapy 5 days per week over 6 weeks in combination with daily temozolomide (TMZ), followed by at least 6 monthly cycles of TMZ administered on 5 consecutive days per 28-day cycle.8,16 Patients may continue on TMZ for up to 12 months at some institutions unless there are intolerable side effects such as myelosuppression. Despite standard ©AANS, 2016 J Neurosurg  October 14, 2016 1
  • 2. E. J. Vaios et al. J Neurosurg  October 14, 20162 treatment, GBM remains incurable, with a median sur- vival of less than 19 months and only a 30% probability of survival 2 years after diagnosis.1,19 The genetic heterogeneity of GBM and the emergence of various resistance mechanisms are proposed limita- tions of standardized therapies.1,18 However, MGMT pro- moter methylation and IDH1 mutation have been identi- fied in genome-wide association studies as robust genetic markers for improved clinical outcomes in patients treated with standard chemoradiation.1,18 While IDH status is con- sidered an independent biomarker for clinical outcomes, MGMT promoter methylation is associated with improved response to TMZ and radiation and thus serves as a pre- dictive biomarker of more favorable treatment outcomes. This observed relationship between the molecular-genetic tumor signature and treatment response has elevated the importance of noninvasive biomarkers that enable real- time selection and adjustment of personalized therapies. The unique role of the local and systemic tumor environ- ment is increasingly recognized in overall tumor biology and treatment outcome.18,23 The identification of a periph- eral biomarker for treatment response and overall survival (OS) would improve patient management and perhaps alter chemotherapy (e.g., TMZ) dosing protocols, which currently do not account for individual pharmacogenetics, pharmacokinetics, or pharmacodynamics.2 The use of circulating blood counts as a marker of drug activity and clinical outcomes is an emerging area of investigation. The current understanding of the effect of chemotherapeutic agents suggests that greater toxicity to various organ systems, such as the bone marrow, might reflect increased potency and, conversely, be associated with a more favorable antitumor profile.11,12 For instance, in patients with renal cell carcinoma, leukopenia induced by the tyrosine kinase inhibitor sunitinib was identified as an independent and prognostic marker for improved response rate and progression-free survival (PFS).4 This finding was further supported by work from Zhu et al.,24 suggesting that sunitinib-induced decreases in neutro- phils, monocytes, and platelets were associated with im- proved progression and survival outcomes in patients with hepatocellular carcinoma. TMZ therapy, in combination with radiotherapy, sig- nificantly improves OS in GBM patients.15 However, dos- ing is based solely on patient body surface area and does not account for variability in resistance mechanisms and drug metabolism, raising the possibility that some patients may be dosed subtherapeutically. Inadequate dosing may partially contribute to the observed resistance to cyto- toxic treatment and ultimately tumor recurrence. One of the most common adverse effects of chemotherapy with TMZ is myelosuppression, including thrombocytopenia and leukopenia. It remains unclear whether patients who do not show a notable decrease in blood counts in re- sponse to TMZ are treated subtherapeutically. Therefore, we hypothesized that changes in circulating blood counts may be predictive of clinical outcomes, with alterations in blood counts indicative of myelosuppression being associ- ated with improved OS and PFS. Methods We conducted a retrospective, chart-review analysis of clinical and demographic data from patients who previ- ously underwent surgery and treatment for primary GBM at the Massachusetts General Hospital between 2007 and 2014. Patient data were obtained from a Massachusetts General Hospital institutional database. This time interval allowed enough time for diagnosis and to follow a com- plete blood count throughout the patient’s disease course. This study received institutional review board approval from Massachusetts General Hospital for all activities. Eligibility All patients were treated at the Massachusetts General Hospital and met the following eligibility criteria: newly diagnosed with GBM (WHO Grade IV) between Janu- ary 1, 2007, and July 30, 2014; 18 years of age or older at the time of diagnosis; surgical biopsy/resection after ini- tial presentation; and treatment with at least 6 cycles of monthly TMZ. Patients who did not complete 6 months of TMZ therapy for any reason were excluded from the study. Variables Descriptive information, including age, sex, and steroid use, was collected. Steroid use was defined as exposure to steroids (e.g., dexamethasone) at any given time during the course of chemotherapy. Genetic information including chromosomal abnormalities, point mutations, and gene methylation was recorded. This included known prognos- tic markers for gliomas such as epidermal growth factor receptor (EGFR) amplification, MGMT promoter methyl- ation, IDH mutation, and 1p/19q co-deletion. The genetic characteristics of the sample are reported as the percent- age of those patients for whom that genetic variable was tested. Absolute peripheral blood platelet, red blood cell, white blood cell, eosinophil, basophil, lymphocyte, neu- trophil, and monocyte count measurements were recorded at a maximum of 15 discrete time points during the course of treatment. Time points included before surgery, after surgery, before chemoradiation, and before each monthly TMZ treatment cycle. PFS and OS were also assessed. Statistics The primary outcome measure was OS, which was de- fined as the length of time from the date of initial diagno- sis to time of death or last date known to be alive for those who were censored. The secondary outcome measure was PFS, which was defined as the length of time from initial diagnosis to the time of first progression based on radiol- ogy report and clinician notes indicating a switch in ther- apy or last date known to be progression free for censored patients. The effect of changes in peripheral blood counts on clinical outcomes was assessed during the interval between baseline measurement (before chemoradiation) and Cycle 6 of monthly TMZ, as this time interval had the greatest sample size and was controlled for the im- munosuppressive effect of steroid use during surgery and chemoradiation. Baseline and 6-month blood counts were
  • 3. Bone marrow response as a potential biomarker J Neurosurg  October 14, 2016 3 performed closest to the time of chemoradiation or TMZ initiation, but no earlier than 2 weeks prior. Changes in blood counts are reported as the percentage change from baseline to Cycle 6 of monthly TMZ. Genetic variables known to be strong prognostic mark- ers were compared with clinical outcomes using logistic regression or Pearson’s chi-square test. Spearman or Pear- son correlation coefficients were estimated to measure the relation between baseline demographic variables and clinical outcome measures. Wilcoxon rank-sum tests and Kruskal-Wallis tests by ranks were used to examine dif- ferences in mean blood count changes between groups of patients stratified by IDH mutation and MGMT pro- moter methylation. Univariate and multivariate Cox pro- portional hazards models were used to evaluate variables for association with PFS and OS. Variables were cho- sen for multivariate analysis using the backward selec- tion method based on statistical significance in univari- ate analysis. Percentage changes from baseline in white blood cell and red blood cell counts were included in the multivariate model as dichotomized variables, indicating either an increase or a decrease in that blood count from baseline. IDH mutation was excluded from the multivari- ate analysis due to a sample size less than 10. A Wilcoxon rank-sum test was performed to assess the association be- tween changes in neutrophils and steroid use. Neutrophil counts at Cycle 6 of TMZ were compared between pa- tients with and without steroid treatment using a 2-sample t-test. Changes in neutrophil counts were excluded from the multivariate analysis as they were confounded by ste- roid use. Survival probabilities were compared between patient groups, stratified by the dichotomous white blood cell variable, using the log-rank test. In subgroup analy- sis, patients with a decreased peripheral white blood cell count from baseline were subdivided into groups based on the degree of change, using either quartiles or the median decrease. Here too the log-rank test was used to compare survival curves between these patient subgroups. All re- ported p values were 2-sided, and statistical significance was considered as p < 0.05. Results Descriptive Data Analysis In total, 86 patients diagnosed with GBM were in- cluded in this study. Their median age at diagnosis was 55 years; 32 patients (37%) were women and 54 (63%) were men. Nineteen patients (22%) were still alive at the time of data cutoff for analysis. The median OS for the entire group was 800 days, and the median PFS was 453 days. Baseline and 6-month peripheral blood counts are listed in Table 1. Mutation frequencies in the patient cohort are reported in Table 2. Univariate and Multivariate Analysis of Biomarker Impact on OS By univariate analysis, MGMT promoter methylation and IDH mutation were associated with better OS and PFS, consistent with the literature (Table 3 and Supple- mental Table 1A). EGFR amplification, changes in neutro- phil counts, changes in peripheral red and white blood cell parameters, steroid use, and patient age at diagnosis were also associated with OS (Table 3). As predicted, patients receiving steroids during TMZ therapy had worse OS, with a 2-year survival rate of 51% compared with 71% in pa- tients who were not treated with steroids (p = 0.0051). Ad- ditionally, changes in neutrophil counts differed between patients with and without steroid use (p = 0.0032), and patients receiving steroids had significantly greater neutro- phil counts at 6 months of TMZ compared with those who never received steroids (p = 0.0031). Changes in neutrophil counts were not associated with OS in patients who never received steroids (p = 0.300). On multivariate analysis, MGMT promoter methylation, a decreased white blood cell count from baseline, and wild-type EGFR status (i.e., EGFR not amplified) were significantly associated with improved OS (Table 4). These associations remained sig- nificant on multivariate analysis that incorporated patient age and steroid use, despite our observation that steroid use was associated with increased white blood cells counts (p = 0.0585). On subgroup analysis, decreases in white blood cell counts remained associated with improved OS with hazard ratios of 0.410 (p = 0.053) and 0.109 (p = 0.066) in patients with and without steroid use. Association of MGMT and IDH With Alterations in Circulating Blood Cell Counts MGMT promoter methylation and IDH1 mutation are known to be robust prognostic markers for OS in GBM pa- tients.1,18 Regarding the association of these genetic mark- ers with alterations in circulating biomarkers that were significant on univariate analysis, we report that MGMT promoter methylation was not correlated with changes in peripheral red blood cell or white blood cell (p = 0.4492) counts. Similarly, IDH mutation was not correlated with changes in peripheral red blood cell (p = 0.2198) or white blood cell (p = 0.3447) counts during the course of treat- ment. Association of Changes in White Blood Cell Counts With OS The Kaplan-Meier estimated 2-year survival rate for patients with a decrease in white blood cell counts from baseline was 58% and that for patients with an increase relative to baseline was 25% (p = 0.0019; Fig. 1). Patients with decreases in white blood cell counts had a median OS of 850 days (95% CI 691–1097 days) compared with 627 days (95% CI 454–745 days) for those with increases in white blood cell counts from baseline. We found no sig- nificant differences in OS between subgroups of patients with decreased white blood cell counts during treatment, when the data were stratified by quartile or median de- crease. Discussion We here demonstrate that treatment-associated my- elosuppression, as manifested by a decrease in circulat- ing white blood cell counts from baseline during adjuvant
  • 4. E. J. Vaios et al. J Neurosurg  October 14, 20164 TMZ therapy, might serve as a potential prognostic mark- er for clinical outcomes in patients with GBM. Our se- rial assessment of peripheral blood counts and additional laboratory and radiographic data found that a decrease in white blood cells from baseline during adjuvant TMZ therapy predicts significantly improved OS. Our institutional survival data for patients with de- creases in white blood cells compares favorably with data from the original EORTC/NCIC trial, which reported a median survival of 14.6 months and a 2-year survival rate of 26.5% for patients receiving radiotherapy plus TMZ.16 The present findings are consistent with findings of pre- vious studies demonstrating an association of MGMT promoter methylation and IDH1 mutation with improved clinical outcomes.3,5,6,17,21 Interestingly, we found that MGMT promoter methylation and IDH mutation did not correlate with changes in white blood cell counts, suggest- ing that these changes may serve as an independent prog- nostic factor. Despite these robust findings, our study is limited by its modest sample size and retrospective nature. Neverthe- less, our exploratory analysis of hematological parameters identified that treatment-related changes in white blood cell counts are associated with OS, with decreases in white blood cell counts from baseline serving as a biomarker for improved OS. This association was maintained on mul- tivariate analysis, even after controlling for other known prognostic variables, including age at the time of diagno- sis, steroid use, EGFR amplification status, and MGMT promoter methylation status. On subgroup analysis, de- creases in white blood cell counts from baseline main- tained a strong association with improved OS regardless of whether a patient used steroids during TMZ therapy. No- tably, an increase in white blood cell counts from baseline was also considered an important biomarker for worse OS, suggesting that changes in white blood cell counts play an TABLE 2. Summary of patient characteristics Characteristic Value Sex  Male  Female 54 (63%) 32 (37%) Age at diagnosis, yrs   Mean (SD)  Range 55.37 (12.55) 18.00–80.00 OS, days   Mean (SD)  Range 915.09 (476.37) 324.00–2660.00 PFS, days   Mean (SD)  Range 622.10 (487.02) 12.00–2660.00 Genetic mutation   EGFR  MGMT  IDH 37 (50.00%) 39 (54.17%) 6 (8.96%) Steroid use Deceased 62 (72.09%) 67 (77.91%) Values represent n (%) unless otherwise indicated. TABLE 1. Patient blood counts Hematology Value Baseline   Platelets (×109 /L)   Mean (SD)   Range 279.28 (97.59) 95.00–589.00   Red blood cells (×1012 /L)   Mean (SD)   Range 4.28 (0.43) 3.27–5.16   White blood cells (×109 /L)   Mean (SD)   Range 8.69 (3.29) 3.27–20.00   Eosinophils (×109 /L)   Mean (SD)   Range 0.11 (0.11) 0.00–0.58   Basophils (×109 /L)   Mean (SD)   Range 0.03 (0.03) 0.00–0.20   Lymphocytes (×109 /L)   Mean (SD)   Range 1.67 (0.73) 0.52–3.93   Neutrophils (×109 /L)   Mean (SD)   Range 6.21 (2.90) 2.16–15.43   Monocytes (×109 /L)   Mean (SD)   Range 0.46 (0.24) 0.14–1.51 6-mo adjuvant TMZ*   Platelets (×109 /L)   Mean (SD)   Range 188.74 (65.76) 80.00–400.00   Red blood cells (×1012 /L)   Mean (SD)   Range 4.13 (0.45) 3.24–5.29   White blood cells (×109 /L)   Mean (SD)   Range 5.68 (2.50) 3.00–14.10   Eosinophils (×109 /L)   Mean (SD)   Range 0.12 (0.10) 0.00–0.45   Basophils (×109 /L)   Mean (SD)   Range 0.02 (0.02) 0.00–0.15   Lymphocytes (×109 /L)   Mean (SD)   Range 0.99 (0.41) 0.35–2.10   Neutrophils (×109 /L)   Mean (SD)   Range 3.97 (2.21) 0.02–11.30   Monocytes (×109 /L)   Mean (SD)   Range 0.40 (0.18) 0.02–1.02 *  Blood counts obtained at the time of the 6th monthly cycle of TMZ treatment.
  • 5. Bone marrow response as a potential biomarker J Neurosurg  October 14, 2016 5 important biological role in the tumor microenvironment independent of chemotherapy. We were unable to identify an association between changes in the peripheral platelet count and clinical out- comes, as described by Williams et al.22 This finding sug- gests that decreases in white blood cell counts, rather than general “bone marrow suppression,” might be a predictor of improved OS. However, we did not observe a statis- tically significant association between white blood cell changes and PFS in our multivariate model, when con- trolling for steroid use and other markers that were sig- nificant on univariate analysis (Supplemental Data). Our findings, however, indicate that patient age and MGMT promoter methylation are important predictors of PFS. Changes in neutrophils were also significantly associ- ated with OS and PFS, with neutrophil increases from baseline predicting worse outcomes. However, given the association of neutrophil counts with medications (e.g., steroid use), infections, and environmental factors, these findings remain more challenging to interpret. Neutrophil counts were likely confounded by steroid use, perhaps in- dicative of more aggressive tumor growth necessitating steroid administration for the management of edema. Con- sistent with the literature, our study found that steroid use was associated with elevated neutrophil counts and worse outcomes. In patients who did not receive steroids, we did not observe an association between neutrophil counts and OS. Therefore, this hematological parameter was excluded from our multivariate analysis. The observed relationship between a decrease in white blood cell counts and clinical outcomes is possibly due to higher in vivo drug concentrations of TMZ, resulting from differences in drug metabolism. TMZ activity is dose and schedule dependent and induces cytotoxicity primarily by causing O6 -meG lesions, which deplete the repair enzyme MGMT, leading to double-strand DNA breaks and tumor cell apoptosis.7,13,14,25 TMZ is administered orally and has a half-life of 1.8 hours, reaching concentrations in the CSF that are 30%–40% of plasma concentrations. The clini- cal efficacy of TMZ and its active metabolite depends on MGMT activity; the integrity of the mismatch repair sys- tem, which recognizes O6 -meG lesions in template DNA strands; and function of the base excision repair system, which corrects highly lethal N3 -meA lesions via poly (ADP-ribose) polymerase.9,10,20 There are no guidelines for dose adjustment based on the tumor genetic signature or in the context of severe renal or hepatic impairment. Since TABLE 3. Univariate analysis for OS Covariate HR 95% CI p Value* Sex  Male  Female 0.685 — 0.418–1.124 — 0.1346 — Age 1.021 1.000–1.042 0.0470 Genetic mutations   EGFR  MGMT  IDH 1.818 0.353 0.100 1.074–3.078 0.203–0.615 0.014–0.726 0.0261 0.0002 0.0228 Percent change in  Platelets   Red blood cells   White blood cells  Eosinophils  Basophils  Lymphocytes  Neutrophils  Monocytes 1.163 0.074 3.133 0.979 0.938 1.330 1.856 1.407 0.443–3.051 0.007–0.787 1.489–6.591 0.895–1.070 0.678–1.296 0.675–2.621 1.218–2.828 0.873–2.270 0.7588 0.0308 0.0026 0.6340 0.6973 0.4102 0.0040 0.1610 Steroids  Used   Not used 2.286 — 1.262–4.142 0.0064 — *  Based on log-rank test. TABLE 4. Multivariate analysis for OS Covariate HR 95% CI p Value* Age 1.064 1.033–1.096 <0.0001 MGMT 0.106 0.043–0.259 <0.0001 EGFR 1.779 0.978–3.247 0.0594 White blood cells  Increase  Decrease 3.040 — 1.245–7.407 — 0.0147 — Red blood cells  Increase  Decrease 1.065 — 0.578–1.961 — 0.8390 — Steroids  Used   Not used 1.196 — 0.575–2.494 — 0.6317 — IDH was excluded due to inadequate sample size. *  Based on log-rank test. Fig. 1. Kaplan-Meier analysis of OS in patients stratified by increase (dotted line) or decrease (solid line) in white blood cell counts from base- line. A significant survival benefit is noted for patients with a decrease in white blood cells relative to baseline (log-rank p = 0.0019).
  • 6. E. J. Vaios et al. J Neurosurg  October 14, 20166 current dosing guidelines do not factor interpatient differ- ences in resistance mechanisms or patient-specific drug metabolism, it is possible that some patients are treated subtherapeutically. Given the routine and reliable assessment of periph- eral blood counts in GBM patients receiving conventional therapies, white blood cell counts could serve as a valuable biomarker of treatment response and for predicting clini- cal outcomes. Future prospective studies should address whether blood cell counts could serve as a correlate bio- marker for in vivo TMZ levels and drug activity as well as a predictor of clinical outcomes, which in turn could help to optimize dosing and scheduling of chemotherapy by ac- counting for variability in drug metabolism. Conclusions We report a temporal relationship between changes in peripheral white blood cell counts during adjuvant TMZ treatment and clinical outcomes. Specifically, depression of white blood cell counts appears to be an independent prognostic factor and was associated with improved OS. This relationship may be a reflection of plasma TMZ levels and, in time, may serve as a surrogate marker of therapeu- tic efficacy. These findings warrant further investigation in prospective studies, including correlations with the degree of change in white blood cell counts and pharmacokinet- ics of TMZ in individual patients. It also remains unclear whether treatment-associated changes in white blood cell counts correlate with drug-induced antitumor activity or represent an independent factor of the altered local and systemic tumor environment. Acknowledgments This work was supported by the 2015 Neurosurgical Research and Education Foundation (NREF) Medical Student Summer Research Fellowship (Eugene J. Vaios) and the Harvard Medical School Scholars in Medicine Office (Eugene J. Vaios). Jorg Dietrich received support from the American Academy of Neurology, the American Cancer Society, and generous gifts from the family foundations of Bryan Lockwood, Ronald Tawil, and Sheila McPhee. References   1. Bastien JI, McNeill KA, Fine HA: Molecular characterizations of glioblastoma, targeted therapy, and clinical results to date. Cancer 121:502–516, 2015   2. Ellingson BM, Wen PY, van den Bent MJ, Cloughesy TF: Pros and cons of current brain tumor imaging. Neuro Oncol 16 (Suppl 7):vii2–vii11, 2014   3. Esteller M, Garcia-Foncillas J, Andion E, Goodman SN, Hidalgo OF, Vanaclocha V, et al: Inactivation of the DNA- repair gene MGMT and the clinical response of gliomas to alkylating agents. N Engl J Med 343:1350–1354, 2000   4. Fujita T, Wakatabe Y, Matsumoto K, Tabata K, Yoshida K, Iwamura M: Leukopenia as a biomarker of sunitinib outcome in advanced renal cell carcinoma. Anticancer Res 34:3781– 3787, 2014   5. Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al: MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003, 2005   6. Idbaih A, Omuro A, Ducray F, Hoang-Xuan K: Molecular genetic markers as predictors of response to chemotherapy in gliomas. Curr Opin Oncol 19:606–611, 2007   7. Kanzawa T, Bedwell J, Kondo Y, Kondo S, Germano IM: Inhibition of DNA repair for sensitizing resistant glioma cells to temozolomide. J Neurosurg 99:1047–1052, 2003   8. Lonardi S, Tosoni A, Brandes AA: Adjuvant chemotherapy in the treatment of high grade gliomas. Cancer Treat Rev 31:79–89, 2005   9. Marchesi F, Turriziani M, Tortorelli G, Avvisati G, Torino F, De Vecchis L: Triazene compounds: mechanism of action and related DNA repair systems. Pharmacol Res 56:275– 287, 2007 10. Miknyoczki S, Chang H, Grobelny J, Pritchard S, Worrell C, McGann N, et al: The selective poly(ADP-ribose) polymerase-1(2) inhibitor, CEP-8983, increases the sensitivity of chemoresistant tumor cells to temozolomide and irinotecan but does not potentiate myelotoxicity. Mol Cancer Ther 6:2290–2302, 2007 11. Motzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Oudard S, et al: Overall survival and updated results for sunitinib compared with interferon alfa in patients with metastatic renal cell carcinoma. J Clin Oncol 27:3584– 3590, 2009 12. Motzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Rixe O, et al: Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med 356:115– 124, 2007 13. Newlands ES, Stevens MF, Wedge SR, Wheelhouse RT, Brock C: Temozolomide: a review of its discovery, chemical properties, pre-clinical development and clinical trials. Cancer Treat Rev 23:35–61, 1997 14. Roos WP, Batista LF, Naumann SC, Wick W, Weller M, Menck CF, et al: Apoptosis in malignant glioma cells triggered by the temozolomide-induced DNA lesion O6 - methylguanine. Oncogene 26:186–197, 2007 15. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al: Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10:459–466, 2009 16. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996, 2005 17. Tabatabai G, Stupp R, van den Bent MJ, Hegi ME, Tonn JC, Wick W, et al: Molecular diagnostics of gliomas: the clinical perspective. Acta Neuropathol 120:585–592, 2010 18. Tanaka S, Louis DN, Curry WT, Batchelor TT, Dietrich J: Diagnostic and therapeutic avenues for glioblastoma: no longer a dead end? Nat Rev Clin Oncol 10:14–26, 2013 19. Thomas AA, Brennan CW, DeAngelis LM, Omuro AM: Emerging therapies for glioblastoma. JAMA Neurol 71:1437–1444, 2014 20. Villano JL, Seery TE, Bressler LR: Temozolomide in malignant gliomas: current use and future targets. Cancer Chemother Pharmacol 64:647–655, 2009 21. von Deimling A, Korshunov A, Hartmann C: The next generation of glioma biomarkers: MGMT methylation, BRAF fusions and IDH1 mutations. Brain Pathol 21:74–87, 2011 22. Williams M, Liu ZW, Woolf D, Hargreaves S, Michalarea V, Menashy R, et al: Change in platelet levels during radiotherapy with concurrent and adjuvant temozolomide for the treatment of glioblastoma: a novel prognostic factor for survival. J Cancer Res Clin Oncol 138:1683–1688, 2012 23. Wilson TA, Karajannis MA, Harter DH: Glioblastoma multiforme: State of the art and future therapeutics. Surg Neurol Int 5:64, 2014
  • 7. Bone marrow response as a potential biomarker J Neurosurg  October 14, 2016 7 24. Zhu AX, Duda DG, Ancukiewicz M, di Tomaso E, Clark JW, Miksad R, et al: Exploratory analysis of early toxicity of sunitinib in advanced hepatocellular carcinoma patients: kinetics and potential biomarker value. Clin Cancer Res 17:918–927, 2011 25. Ziegler DS, Kung AL, Kieran MW: Anti-apoptosis mechanisms in malignant gliomas. J Clin Oncol 26:493– 500, 2008 Disclosures The authors report no conflict of interest concerning the materi- als or methods used in this study or the findings specified in this paper. Author Contributions Conception and design: Vaios, Nahed, Dietrich. Acquisition of data: Vaios. Analysis and interpretation of data: Vaios, Nahed, Muzikansky, Dietrich. Drafting the article: Vaios. Critically revis- ing the article: Vaios, Nahed, Fathi, Dietrich. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Vaios. Statistical analysis: Vaios, Muzikansky. Administrative/technical/material support: Nahed, Dietrich. Study supervision: Nahed, Dietrich. Supplemental Information Online-Only Content Supplemental material is available with the online version of the article. Supplemental Tables 1A and B. http://thejns.org/doi/ suppl/0.3171/2016.7.JNS16609. Correspondence Eugene John Vaios, Vanderbilt Hall Box 099, 107 Ave. Louis Pasteur, Boston, MA 02115. email: eugene_vaios@hms.harvard. edu.