A cerebrospinal fluid microRNA signature as biomarker for glioblastoma

Abstract

Purpose:
To develop a cerebrospinal fluid (CSF) miRNA diagnostic biomarker for glioblastoma.

Experimental design:
Glioblastoma tissue and matched CSF from the same patient (obtained prior to tumor manipulation) were profiled by TaqMan OpenArray® Human MicroRNA Panel. CSF miRNA profiles from glioblastoma patients and controls were created from three discovery cohorts and confirmed in two validation cohorts.

Results:
miRNA profiles from clinical CSF correlated with those found in glioblastoma tissues. Comparison of CSF miRNA profiles between glioblastoma patients and non-brain tumor patients yielded a tumor "signature" consisting of nine miRNAs. The "signature" correlated with glioblastoma tumor volume (p=0.008). When prospectively applied to cisternal CSF, the sensitivity and specificity of the 'signature' for glioblastoma detection were 67% and 80%, respectively. For lumbar CSF, the sensitivity and specificity of the signature were 28% and 95%, respectively. Comparable results were obtained from analyses of CSF extracellular vesicles (EVs) and crude CSF.

Conclusion:
We report a CSF miRNA signature as a "liquid biopsy" diagnostic platform for glioblastoma.

Full text

Oncotarget1
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www.impactjournals.com/oncotarget/ Oncotarget, Advance Publications 2017
A cerebrospinal uid microRNA signature as biomarker for
glioblastoma
Johnny C. Akers1,*, Wei Hua2,*, Hongying Li3,*, Valya Ramakrishnan1, Zixiao Yang2,
Kai Quan2, Wei Zhu2, Jie Li1, Javier Figueroa1, Brian R. Hirshman1, Brittney Miller1,
David Piccioni4, Florian Ringel5, Ricardo Komotar6, Karen Messer3, Douglas R.
Galasko7, Fred Hochberg1, Ying Mao7,**, Bob S. Carter1,** and Clark C. Chen1,**
1 Center for Theoretical and Applied Neuro-Oncology, University of California, San Diego, CA, USA
2 Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
3 Biostatistics Department, Moores Cancer Center, UC San Diego Health System, La Jolla, CA, USA
4 Department of Neurosurgery, Moores Cancer Center, UC San Diego Health System, La Jolla, CA, USA
5 Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
6 Department of Neurological Surgery, Miller School of Medicine, University of Miami, Miami, FL, USA
7 Department of Neurosciences, University of California, San Diego, CA, USA
8 State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, The Collaborative Innovation Center for Brain
Science, Fudan University, Shanghai, China
* These authors shared responsibility as rst authors
** These authors shared responsibility as senior authors
Correspondence to: Clark C. Chen, email: clarkchen@ucsd.edu
Keywords: extracellular vesicle, CSF, liquid biopsy
Received: April12, 2017 Accepted: May 19, 2017 Published: June 01, 2017
Copyright: Akers et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Purpose: To develop a cerebrospinal uid (CSF) miRNA diagnostic biomarker for
glioblastoma.
Experimental Design: Glioblastoma tissue and matched CSF from the same
patient (obtained prior to tumor manipulation) were proled by TaqMan OpenArray®
Human MicroRNA Panel. CSF miRNA proles from glioblastoma patients and controls
were created from three discovery cohorts and conrmed in two validation cohorts.
Results: miRNA proles from clinical CSF correlated with those found in
glioblastoma tissues. Comparison of CSF miRNA proles between glioblastoma
patients and non-brain tumor patients yielded a tumor “signature” consisting of
nine miRNAs. The “signature” correlated with glioblastoma tumor volume (p=0.008).
When prospectively applied to cisternal CSF, the sensitivity and specicity of the
‘signature’ for glioblastoma detection were 67% and 80%, respectively. For lumbar
CSF, the sensitivity and specicity of the signature were 28% and 95%, respectively.
Comparable results were obtained from analyses of CSF extracellular vesicles (EVs)
and crude CSF.
Conclusion: We report a CSF miRNA signature as a “liquid biopsy” diagnostic
platform for glioblastoma.
INTRODUCTION
Glioblastoma, dened by the World Health
Organization (WHO) glioma classication as grade IV
astrocytoma, is the most common form of primary brain
cancer in adults [1, 2]. Diagnosis of the disease remains
a clinical challenge. First, error in diagnosis occurs in
up to 30% of the instances where clinical decisions are
based solely upon Magnetic Resonance Imaging (MRI)
[3]. As such, diagnosis of the disease requires tissue

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acquired through cranial surgery [4]. However, morbidity
for biopsy surgical resection of glioblastoma involving
eloquent regions of the cerebrum can be as high as 10%
[5], with permanent neurologic injury for a subset of
these patients [6]. The risk is higher for surgical resection
involving eloquent cerebrum [7]. Second, a subset of brain
tumor patients present with co-morbidities that prohibit
consideration for surgery. Analysis of the Surveillance,
Epidemiology, and End Results (SEER) registry suggests
that ~20% of all aicted patients are medically too
ill to be considered for surgery [8]. We propose that
these challenges can be addressed by development of a
minimally invasive “liquid biopsy” platform [9].
CSF is an appealing and accessible bio-uid for
glioblastoma “liquid biopsy”. The bio-uid lies in close
proximity to tumor tissue, often bathing tumor or its
associated microenvironment [10]. The CSF can be
located in the brain or its ventricles, which we termed
“cisternal” CSF, or the lumbar region, which we termed
“lumbar” CSF. CSF in these compartments diers in
chemical compositions [11, 12], suggesting limited CSF
exchange between these two anatomic compartments.
Whether these dierences impact their diagnostic value
for glioblastoma remains an open question.
Extracellular Vesicles (EVs) are cell-secreted
vesicles that range 30-2000 nm in size that mediate
critical biologic functions, including cellular remodeling
and intracellular communication [9]. Cancer cells exhibit
increased secretion of EVs, with secreted EVs containing
genetic contents reective of the cell of origin [9, 13]. In
this context, there is a growing interest in EVs derived
from bio-uids, including CSF [14], as a platform for
disease diagnosis [15].
Here, we examined miRNA proles of the CSF
EVs and “crude” CSF derived from glioblastoma patients.
miRNA is an attractive biomarker platform given its
stability in bio-uids [15], selective over-expression in
glioblastomas [13, 16, 17], and release by tumor cells into
the extracellular environment [18]. Our results support
the utility of CSF miRNA proling as a “liquid biopsy”
platform for glioblastoma diagnosis.
RESULTS
miRNA proling of matched glioblastoma tumor
and CSF EVs in human subjects
We rst investigated whether the miRNA prole
from CSF mirrored that of the matched glioblastoma
specimen within the same subject, using the 15 subjects
with matched CSF and tumor tissue from Cohort 1.
Using a CT cut-o of 35, we found that 200-400 miRNAs
were detected in the glioblastoma specimens (median
313 species; range 238 to 351). Between 30-50% of
these miRNAs were detected in the matched EV CSF
(Figure 1A). However, the average CT value at which
these miRNAs were detected in CSF was increased by
~5 (Supplementary Figure 1), translating to a 30-fold
decrease in abundance. We plotted the level of each
detectable miRNA in CSF (Figure 1B, y-axis) against
its level in the glioblastoma sample (Figure 1B, x-axis)
and found correlation between CSF miRNAs and tumor
miRNAs for all 15 paired samples. These results suggest
that the miRNA content of CSF mirrors that of matched
glioblastoma samples.
Comparison of CSF fractions for number of
miRNA species
For select samples, miRNA proling was performed
for both CSF derived EV and crude CSF. In general, more
miRNA species were detected in the crude CSF relative
to EV. Nearly all miRNAs detected in the EVs were also
present in the crude CSF (Figure 1C).
Identication of a miRNA CSF signature which
can identify glioblastoma
Though all CSF samples were collected using the
same Standard Operating Procedure (SOP), signicant
variation in miRNA proles were found between CSF
derived from the rst three cohorts (cohorts 1, 2, and
3). To account for this variability, we used miRNA
proles derived from all three cohorts in our signature
development. Details of the analysis can be found in
Supplementary Figure 2. In brief, miRNAs with levels
that diered between glioblastoma and non-oncologic
CSF were identied using the criteria of FDR < 0.2
and log(fold-change) > 2 as described. From Cohort 1,
we identied 29 miRNAs. 3 miRNAs were identied
in Cohort 2. In Cohort 3, we identied 110 miRNAs
as dierentially expressed, with miR-21 having the
largest fold change as previously published [16]. Based
on our cross-sample validation criteria, 24 miRNAs
were subsequently selected for signature development
(Supplementary Figure 3). In addition, three dierentially
regulated miRNAs which validated in one (but not two)
independent datasets were added to the candidate set.
miR-548a, stably expressed across the three data sets and
potentially useful as a reference miRNA, was also added
to the panel, yielding a total of 28 candidate miRNAs.
We then used LASSO [19] to develop a classier
from these 28 candidate miRNAs using cross-validated
minimum deviance as the model selection criterion
(Figure 2A). LASSO analysis indicated an optimal
classier consisting of 9 miRNAs, including 5 miRNAs
that were enriched (miR-21, -218, -193b, -331, and

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Figure 1: miRNA analysis of matched glioblastoma tumor and CSF samples. miRNA prole of matched glioblastoma tumor
and CSF EV samples were analyzed using the TaqMan OpenArray platform. A. Venn diagrams indicating the unique and shared detectable
miRNAs between tumor tissue and CSF EVs. B. Correlation between miRNA proles of matched glioblastoma specimens and CSF. For
each patient, CT values of shared miRNAs in tumor specimen were plotted against CT values from CSF EVs. Pearson correlation coecient
was then calculated for each patient. The correlations were highly signicant for all matched pairs of tumor and CSF specimens. C. Venn
diagrams comparing the miRNA prole of crude CSF versus CSF EV. > 95% of miRNA found in CSF EVs were also represented in the
crude CSF.

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-374a) and 4 miRNAs that were depleted (miR-548c,
-520f, 27b, and 130b) in glioblastoma CSF (Figure 2B).
We then determined the optimal score cuto (0.4) below
which we classied a subject as non-glioblastoma and
above which we classied a subject as with a diagnosis
of glioblastoma. Both the signature coecients and the
cuto for classication as glioblastoma were documented
before proceeding to the validation step.
Correlation of the CSF miRNA signature score
with tumor volume
Pre-operative MRI was available for 11 of the
patients in Cohort 1. We created tumor volumes based on
the Agfa CD Viewer and related these to the CSF miRNA
gene signature scores. A positive correlation was observed
Figure 2: Identication of miRNA signature. Dierentially expressed miRNAs between glioblastoma and non-oncologic CSF
samples were selected from miRNA qPCR array based on FDR < 2 and log(fold-change) > 2 and cross-validated using multiple cohorts. A.
28 candidate miRNAs was used to train a classier with LASSO using a using cross-validated minimum deviance as the model selection
criterion, B. yielding a 9 miRNA signature.
Table 1: Patient demographics and samples
Discovery Discovery Discovery Validation Validation
Cohort
Cohort 1
UCSD, Munich, Miami
Cisternal and lumbar CSF
Cohort 2
Huashan,
Lumbar CSF
Cohort 3
UCSD,
Cisternal CSF
Cohort 4
UCSD,
Lumbar
CSF
Cohort 5
UCSD,
Huashan,
Lumbar
CSF
Age, Median (Range) 61
(25-82) 59
(24-83) 56.5
(22-84) 53.5 (29-74) 58
(27-74)
Gender
Female 17 32 13 5 23
Male 22 35 19 17 15
Diagnosis
Glioblastoma 24 40 13 10 18
Normal/non-oncologic 15 27 19 12 20
Collection Method
Cisternal 26 0 32 22 0
Lumbar 13* 67 0 0 38
Tumor tissue yes no no no no
*All 13 lumber CSF samples from Cohort 1 were from the glioblastoma group

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between miRNA signatures and tumor volumes (Figure
3A). Glioblastoma with volumes < 15 cc had lower
miRNA scores than those with > 15cc’s (P < 0.0001,
Figure 3B).
Validation of the CSF miRNA glioblastoma
signature
We tested the performance of the 9-miRNA
signature in a prospective manner. Since most EV
miRNAs are also detected in crude CSF, we opted to
validate our signature using unfractionated crude CSF.
We prospectively collected and proled cisternal CSF
from an additional 22 patients (Cohort 4: 10 glioblastoma
and 12 non-oncologic patients). Using the cuto (0.4)
established during the discovery process, the signature
correctly identied 8/10 subjects with glioblastoma and
8/12 non-oncologic subjects, yielding a sensitivity of 80%
and specicity of 67%. The AUC was 0.75 (95% CI 0.53,
0.97) (Figure 4A).
We also prospectively collected and proled
lumbar CSF from 18 glioblastoma and 20 non-oncologic
patients (Cohort 5). Using the same coecients and
cuto score, the 9 miRNA signature correctly identied
5/18 glioblastoma subjects and 19/20 non-oncologic
subjects, yielding a sensitivity of 28% and specicity of
95%. The AUC was 83% (95% CI: 69%, 96%). (Figure
4B). Notably, few miRNA species were detected in the
lumbar CSF samples. These results suggest that cisternal
and lumbar CSF may dier in miRNA content. Notably,
these validation samples used whole CSF for the miRNA
assay, as described in methods.
13 lumbar glioblastoma CSF samples were collected
as a part of Cohort 1. We had compared the performance
of our miRNA signature in these samples in order to aord
direct comparison to that seen in the cisternal samples.
In the cohort 1 lumbar CSF samples, the 9 miRNA
signature correctly identied glioblastoma subjects in
3/13 glioblastoma samples yielding a sensitivity of 23%.
These results were comparable to those observed in the
validation cohorts, conrming our observation that the
diagnostic utility of the 9-miRNA signature is optimal
when applied to cisternal CSF (Supplementary Figure 4).
Validation of increased miR21 in a mouse
xenograft model of glioblastoma
miR-21 [16] play a pivotal role in our signature. We
wished to determine whether glioblastoma growth induce
accumulation of miR-21 in the CSF and used a murine
xenograft model to achieve this end. We orthotopically
implanted the patient-derived glioblastoma neurosphere
line (JVJ), which expressed high levels of miR-21, into
nude mice. 4 weeks after injection, brain tissue and
murine CSF were collected from tumor bearing mice
and age-matched, mock injected nude mice (Figure 5A).
Both brain tissue and CSF were analyzed by qRT-PCR to
measure the level of miR-21. In all analyzed samples, we
found elevated miR-21 levels in the brain tissues (Figure
5B) and CSFs (Figure 5C) isolated from xenograft bearing
mice relative to control mice. This result suggest that
glioblastoma xenograft growth induce accumulation of
miR-21 in murine CSF.
Figure 3: Correlation of miRNA score with tumor volume. A. The tumor volume of 11 patients in Cohort 3 was plotted against
the CSF miRNA signature score, and the Pearson correlation coecient was calculated. B. Glioblastoma < 15 cc’s in volume showed a
lowered miRNA signature score relative to those with > 15cc’s.

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DISCUSSION
In current clinical practice, CSF sampling is
not routinely performed in glioblastoma patients. The
sensitivity of CSF cytology as a diagnostic tool for
glioblastoma is ~10% [20] and below the threshold for
clinical utility. However, our study suggests potential
utility for CSF miRNA proling as a diagnostic platform
for glioblastoma. The miRNA detectable in human
and nude mouse glioblastoma specimens is detected
in matched CSF, though at a concentration that is
~30 fold lower. miRNA proles of CSF derived from
glioblastoma patients correlated well to the miRNA
proles of the matched tumor specimens. We developed
a nine miRNA CSF signature that discriminated CSF
of glioblastoma patients from those of patients without
history of brain cancer. We validated this signature using
prospectively collected CSF samples after development
and documentation of the original signature. For crude
CSF based assay, the sensitivity and specicity for
glioblastoma detection were 80% and 67%, respectively.
In contrast, for CSF derived from lumbar puncture, the
sensitivity and specicity for glioblastoma detection were
28% and 95%, respectively. It is important to note that
the miRNA reported here dier from those previously
reported to discriminate between types of brain cancer
[21], suggesting that our miRNA signature has limited
utility in discriminating between dierent forms of brain
cancers. These results suggest that distinct miRNA proles
may be required to address dierent clinical needs.
There has been signicant variability in the reported
miRNA proles in CSF derived from glioblastoma patients
[21-23]. We observed this variability in our own study,
where signicant variation in miRNA proles were found
between CSF derived from the three discovery cohorts
(Supplemental Figure 3). A major source of variability is
the CSF collection site (cisternal vs. lumbar). However,
even after correcting for site of collection, this variability
remained. It is worthwhile noting that the CSF samples
were collected in our study through a Standard Operating
Figure 4: Validation of miRNA signature. A. Performance of the 9-miRNA signature using crude cisternal CSF from an independent
collection of prospectively collected samples. B. Performance of the 9-miRNA signature using crude lumbar CSF from an independent
collection of prospectively collected samples.

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Procedure (SOP) and processed identically post-collection.
The variability observed between these cohorts, in the
context of the published literature, suggests that CSF
miRNA proles are likely inuenced by physiologic
factors or perturbation that was not accounted for by the
SOP (e.g. circadian rhythm, fatigue, intake of medicine…
etc). As such, the robustness of the CSF miRNA signatures
are largely a function of the sample size, since larger
sample sizes aord a greater likelihood of minimizing the
undue inuence of any particular perturbation/physiology.
Our study is particularly important in this context, since
our study design is the only one in the literature that
derived the signature through three independent cohorts,
summing to 135 CSF samples. We subsequently validated
our results in another 60 prospectively collected CSF.
The scale of our study as well as the meticulous eort
devoted to validation is notable in the reported literature
of glioblastoma CSF biomarkers.
An important nding in this study is that the miRNA
contents of cisternal and lumbar CSF dier. We found that
less than half of the miRNAs detected in cisternal CSF
were detected in lumbar CSF (Supplementary Figure 5),
likely accounting for the fewer number of dierentially
expressed miRNAs found in cohort 2. This nding
suggests the two CSF compartments do not communicate
suciently for full equilibrium of miRNA contents.
Similar observations have been made for other proteins
and metabolites [11, 24]. For instance, IgG level decreases
progressive as the CSF moves from the site of intracranial
inammation to the lumbar sac [25]. These dierences
bear relevance to CSF based diagnostics and warrant
consideration in future study design. For instance, separate
miRNA signatures may need to be developed for analysis
of clinical lumbar and cisternal CSF samples.
EVs have been touted as platforms for diagnostic
and prognostic biomarker interrogation [26-28]. The
isolation of these EVs from CSF introduces an additional
step during clinical sample processing [29], a step which
incurs increased cost and risk of contamination risk.
The step is necessary if 1) the biomarker of interest is
enriched in EVs or 2) if inhibitory factors prohibitive to
the analytical platform is present in the crude CSF. Our
analysis support neither hypothetical scenarios. When
we compared the miRNA proles of CSF EVs relative to
Figure 5: Direct release of miR-21 from glioblastoma xenograft in vivo. A. 20,000 human glioblastoma stem cells were
intracranially injected into nude mice. 4 weeks later, brain tissues and murine CSFs were collected from tumor bearing mice and age-
matched nude mice without the xenograft injection. B. Human miR-21 levels were elevated in the brain tissue of patient derived glioblastoma
xenograft bearing mice and undetectable in mice without xenograft implant. C. Human miR-21 levels were elevated in the CSF of patient
derived glioblastoma xenograft bearing mice and undetectable in mice without xenograft implant.

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crude CSF, we found that > 95% of miRNAs found in EVs
(including miRNAs in our signature) were represented in
the crude CSF, suggesting that crude CSF may suce for
miRNA proling. Further supporting this hypothesis, the
performance of the 9 miRNA signature was comparable
when applied to CSF EV RNA (Supplementary Figure 6)
or crude CSF RNA (Figure 4).
The literature that examined altered miRNA
regulation in glioblastoma has expanded over the past
decade [30]. It is notable that of the reported miRNA
that are signicantly over- or under-expressed in
clinical glioblastoma specimens [30], only miR-21 was
represented in our miRNA signature. As further validation
of our correlative clinical studies, we showed that murine
CSF miR-21 levels were elevated in murine CSF from
glioblastoma xenograft bearing mice (Figure 5). We did
not observe such increase for other miRNAs previously
reported to be over-expressed in glioblastoma, including
miR-16 [30-32] or miR-10b [17, 30, 33] (data not shown).
Our previous study suggested that > 90% extra-cellular
miR-21 were found in the EV fractions [13]. Together,
these results suggest that glioblastoma harbor biologic
mechanisms that facilitate the exportation of miR-21
through EV secretion. This interesting hypothesis awaits
experimental validation.
While our miRNA signature performed well as a
diagnostic tool in cisternal CSF, opportunities for obtaining
these samples are admittedly limited. Such samples can be
obtained only from patients with an Ommaya reservoir or
a ventriculo-peritoneal shunt. Because these procedures
involve placement of an indwelling catheter that is in
direct communication to cisternal CSF, serial samples
can be safely acquired in this patient population. As such,
clinical testing of the cisternal CSF signature is feasible
in the subpopulation of glioblastoma patients with an
indwelling shunt system. Moreover, serial sampling of
cisternal CSF from this patient population may aord a
minimally invasive platform for tracking glioblastoma
disease burden. We are in the process of collecting and
testing CSF from recurrent glioblastoma patients to further
test the utility of our miRNA signature.
In sum, our study provides a proof-of-principle
study demonstrating the plausibility of CSF miRNA
proling as a “liquid biopsy” platform for glioblastoma
diagnosis and provides the basis of future validation of
this platform.
MATERIALS AND METHODS
Clinical specimen collection and image analysis
Five cohorts of patients totaling 195 subjects
provided CSF for these studies (Table 1). The CSF studies
were approved by IRB boards at University of California
San Diego (UCSD) (Cohorts 1, 3, 4, and 5), Technische
Universität München (TUM)(Cohorts 1), University
of Miami Hospital (UMH)(Cohorts 1), and Huashan
Hospital(Cohorts 2,5). All studies were in conducted in
accordance with the principles expressed at the declaration
at Helsinki. Each patient was consented in writing by a
research coordinator prior to CSF collection. Median
age ranged from 54 to 61 years across cohorts. Overall,
88 subjects were female and 107 were male, 111 had
diagnosis of glioblastoma and 84 had other non-oncologic
conditions. Cisternal and ventricular CSF (grouped
as “cisternal”) was collected on 80 subjects by drain
placement or cisternal aspiration at the time of craniotomy
prior to tumor manipulation. Lumbar CSF was collected
on 115 subjects, through lumbar puncture or lumbar
drain. Collected CSF specimens were ltered (0.8µm
lter), immediately frozen and stored at -80°C. 1 cc of
CSF was utilized as the input for all miRNA analysis. The
UCSD cohort was additionally consented for analysis of
MR images. Volumetric measurements of available pre-
operative MR images were carried out with Agfa CD
Viewer 4.5.1 using the formula Volume = (L × W × H)/2,
where L is the greatest length, W is the greatest width, and
H is the greatest depth or height of the tumor [34]. Patients
that received bevacizumab were excluded from MR image
analysis [35].
Extracellular vesicle (EV) isolation
The EV fraction was isolated by dierential
centrifugation as previously described [13]. CSFs
were diluted 1:1 with 1x PBS (Mediatech) prior to
centrifugation. Samples were centrifuged at 2,000×g for
20 min to remove cellular debris. The supernatant was
further centrifuged at 120,000×g for 2 h in a Type 70 Ti
rotor (Beckman) to pellet the EVs. All centrifugation steps
were performed at 4°C. EV pellets were resuspended in
PBS and stored at -80°C.
miRNA proling
RNA was extracted from each sample using the
miRCURY™ RNA Isolation Kit (Exiqon). Samples
assayed were EV, supernatant and tissue from cohort 1;
EV and supernatant from cohort 2; EV from cohort 3;
and whole CSF from cohorts 4 and 5. Four microliters
of RNA extract (4-20ng/µl) was used as input for
microRNA proling on the TaqMan® OpenArray® Real-
Time PCR System using the manufacturer’s instructions
(Life Technologies). Manufacturer’s cartridges consisted
of 818 TaqMan qPCR assays arranged on 384 well
plates, with primers targeting 754 miRNA species, and
16 replicate wells of one negative and 3 positive RNA
controls. Megaplex™ RT Primers, Human Pool A v2.1 and
Megaplex™ RT Primers, Human Pool B v3.0 were used

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for the reverse transcription step. Megaplex™ PreAmp
Primers, Human Pool A v.2.1 and Megaplex™ PreAmp
Primers, Human Pool B v3.0 were used for the PreAmp
step. The samples within each of the 3 discovery cohorts
were assayed on the same date using the same reagents.
The validation samples (cohorts 4 and 5) were assayed in
two dierent batches on two dierent dates and data was
combined for analysis.
Data normalization, QC, and preprocessing
miRNA species with C
T
value ≥35 were considered
below the detection threshold. In tumor tissue samples,
CT values for the query miRNAs were normalized using
the mean of the positive controls (RNU44, RNU48,
U6-rRNA). For the CSF samples, the positive control
miRNAs were not uniformly expressed at high levels
across samples. For the discovery cohorts global mean
normalization was performed in which normalized
CT values were calculated as the raw CT value minus
the arithmetic mean of all expressed miRNAs in the
sample [36]. For the validation cohorts, the data was
rst normalized within each sample as before using the
global mean normalization. Then the batch eect from
the two assay dates was removed using an empirical
Bayes approach (ComBat) [37] with assay date and two
confounding variables (pathology and CSF collection site)
included in the adjustment model. The batch-corrected
data were then combined for analysis.
Statistical approach to training and validation of
the classier
The classier was trained using the three discovery
cohorts (Cohorts 1, 2, and 3). When both supernatant
and EV miRNA was available within a cohort, we used
the fraction with the higher median number of detected
miRNA species for analysis within that cohort. For
cohorts with low detection rates, we used both fractions
with a Bonferroni correction for the two comparisons.
Dierentially expressed CSF miRNAs between
glioblastoma and non-oncologic subjects were identied
using the limma Bioconductor package [38], with FDR
< 0.2 and log (fold-change) > 2 as criteria. We then
required candidate miRNAs from a given cohort to
replicate as dierentially expressed in at least 2 additional
discovery data sets, including Cohort 1 EV, Cohort 2 EV
+ supernatant, Cohort 3 EV and also including tissue
miRNA data from TCGA [39]. The replication criterion
was a two-sided p-value < 0.05 (from limma, or a t-test for
TCGA) and the same direction of dierential expression;
this test of replication has overall Type I error rate ~1%.
This candidate selection plan was pre-specied and
documented.
Candidate miRNAs were carried forward to a
multivariate model to discriminate glioblastoma from non-
oncologic controls using L1-penalized logistic regression
[19]. The model was trained with Cohort 3 using glmnet
package in R with lambda chosen by cross validation [19].
The signature and optimal cuto score to discriminate
cases from controls in Cohort 3 were documented. The
prediction error of the classier with pre-determined
cuto was then evaluated data from whole CSF, using the
prospectively collected independent validation Cohorts 4
and 5. For correlation analyses, the Pearson correlation
coecient was calculated using Graphpad Prism 6.
Orthotopic xenograft model
Dissociated glioblastoma stem cells JVJ (2x104
cells in 4 μl HBSS) were stereotactically injected into the
brains of nude mice at age 6 weeks old. The coordinates
were: 1.8 mm to the right of bregma and 3 mm deep from
the dura. Aged-matched nude mice were used as controls.
Four weeks after injection, CSF samples were collected
from the cisterna magna as previously described [40].
Mock injection with vehicle control was carried out for
control mice.
Quantitative reverse transcriptase-polymerase
chain reaction (qRT-PCR)
For the detection of tissue miRNA, RNA was
extracted from homogenized mouse brains using Qiagen
miRNeasy Mini Kit. cDNA was synthesized using
TaqMan miRNA Reverse Transcription Kit and miRNA-
specic stem-loop primers (Applied Biosystems),
followed by qPCR using SsoAdvanced™ Universal
Probes Supermix (Bio-Rad) and miRNA specic Taqman
assay on a Bio-Rad CFX96 instrument. For the detection
of miRNA from murine CSF, collected CSF was lysed
directly in buer containing 50mM Tris pH 8, 140mM
NaCl, 1.5mM MgCl2, 0.5% NP40, and 0.1% BSA, then
reverse transcribed using SuperScript® VILO™ cDNA
synthesis kit. The cDNA was pre-amplied for 15 cycles
using Taqman PreAmp Mastermix prior to PCR detection
with miR-21 Taqman assay.
Primer sequences
Taqman miRNA assay for miR-10b, miR-16, and
miR-21 were purchased from ThermoFisher Scientics.
AKNOWLEDGMENTS
We thank Shirley Phillips
and Steven Lockton from
Regulus Therapeutics for performing the miRNA proling
experiments.

Oncotarget10
www.impactjournals.com/oncotarget
CONFLICT OF INTERESTS
The authors declare that there is no conict of
interest.
FUNDING
Grant acknowledgement: 4UH3TR000931-03
awarded to BSC. 1RO1NS097649-01 and BWF
1006774.01 awarded to CCC. International S&T
Cooperation Program of China, 2014DFA31470 awarded
to YM and CCC.
Editorial note
This paper has been accepted based in part on peer-
review conducted by another journal and the authors’
response and revisions as well as expedited peer-review
in Oncotarget.
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