F11R Is a Novel Monocyte Prognostic Biomarker for
Winnie W. Pong1, Jason Walker2, Todd Wylie2, Vincent Magrini2, Jingqin Luo3, Ryan J. Emnett1, Jaebok
Choi4, Matthew L. Cooper4, Malachi Griffith2, Obi L. Griffith2, Joshua B. Rubin5, Gregory N. Fuller6, David
Piwnica-Worms7, Xi Feng8, Dolores Hambardzumyan8, John F. DiPersio4, Elaine R. Mardis2, David H.
1 Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America, 2 The Genome Institute, Washington
University School of Medicine, St. Louis, Missouri, United States of America, 3 Division of Biostatistics, Washington University School of Medicine, St. Louis,
Missouri, United States of America, 4 Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United
States of America, 5 Department of Pediatrics, Division of Pediatric Hematology/Oncology, Washington University School of Medicine, St. Louis, Missouri,
United States of America, 6 Department of Pathology, MD Anderson Cancer Center, Houston, Texas, United States of America, 7 BRIGHT Institute and
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, United States of America, 8 Department of Stem Cell Biology
and Regeneration, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America
Objective: Brain tumors (gliomas) contain large populations of infiltrating macrophages and recruited microglia,
which in experimental murine glioma models promote tumor formation and progression. Among the barriers to
understanding the contributions of these stromal elements to high-grade glioma (glioblastoma; GBM) biology is the
relative paucity of tools to characterize infiltrating macrophages and resident microglia. In this study, we leveraged
multiple RNA analysis platforms to identify new monocyte markers relevant to GBM patient outcome.
Methods: High-confidence lists of mouse resident microglia- and bone marrow-derived macrophage-specific
transcripts were generated using converging RNA-seq and microarray technologies and validated using qRT-PCR
and flow cytometry. Expression of select cell surface markers was analyzed in brain-infiltrating macrophages and
resident microglia in an induced GBM mouse model, while allogeneic bone marrow transplantation was performed to
trace the origins of infiltrating and resident macrophages. Glioma tissue microarrays were examined by
immunohistochemistry, and the Gene Expression Omnibus (GEO) database was queried to determine the prognostic
value of identified microglia biomarkers in human GBM.
Results: We generated a unique catalog of differentially-expressed bone marrow-derived monocyte and resident
microglia transcripts, and demonstrated that brain-infiltrating macrophages acquire F11R expression in GBM and
following bone-marrow transplantation. Moreover, mononuclear cell F11R expression positively correlates with
human high-grade glioma and additionally serves as a biomarker for GBM patient survival, regardless of GBM
Significance: These studies establish F11R as a novel monocyte prognostic marker for GBM critical for defining a
subpopulation of stromal cells for future potential therapeutic intervention.
Citation: Pong WW, Walker J, Wylie T, Magrini V, Luo J, et al. (2013) F11R Is a Novel Monocyte Prognostic Biomarker for Malignant Glioma . PLoS ONE
8(10): e77571. doi:10.1371/journal.pone.0077571
Editor: Michael Platten, University Hospital of Heidelberg, Germany
Received May 22, 2013; Accepted September 3, 2013; Published October 11, 2013
Copyright: © 2013 Pong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Cancer Institute (U01-CA160882 to DH and DHG; U01-CA141549 to DHG) and National
Institutes of Health (RC4-NS072916 to DHG). WWP was partly supported by a grant from the W. M. Keck Foundation. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal's policy and have the following potential conflicts; there are no direct conflicts of interest. Dr.
Pong reports grants from the W. M. Keck Foundation during the conduct of the study. Dr. Feng reports personal fees from National Cancer Institute and
Cleveland Clinic, and non-financial support from University of Michigan outside the submitted work. Dr. Mardis reports personal fees from Pacific
Biosciences Inc and from Illumina Inc, and other from Life Technologies, outside the submitted work. Dr. Gutmann reports grants from National Cancer
Institute and from National Institutes of Health during the conduct of the study; personal fees from Biomarin, outside the submitted work; In addition, Dr.
Gutmann has Neurofibromatosis type 1 patents. None of these organizations were involved in the study design; collection, analysis and interpretation of
data; writing of the paper; and/or decision to submit for publication. This does not alter their adherence to all the PLOS ONE policies on sharing data and
* E-mail: email@example.com
PLOS ONE | www.plosone.org1 October 2013 | Volume 8 | Issue 10 | e77571
Survival for adults with the malignant brain tumor,
glioblastoma multiforme (GBM), remains poor despite decades
of advancements in surgery, radiation, and chemotherapy. One
underexplored strategy for treating these cancers is the
targeting of stromal cell types in the tumor microenvironment.
In this regard, microglia and macrophages may serve as
tractable targets for stroma-directed therapy, as they comprise
30-50% of the cells in both benign and malignant gliomas [1,2].
In previous genomic studies, glioma outcome and progression
were shown to correlate with macrophage and microglia gene
expression , while polymorphisms in the microglial CX3CR1
chemokine receptor locus were associated with improved
patient survival . Furthermore, pharmacologic or genetic
inhibition of microglial function reduces tumor growth in
experimental rodent glioma models [2,5–9].
One of the barriers to developing glioma stromal therapies is
the paucity of suitable reagents to characterize the spectrum of
macrophage populations in health and disease. Although
recent reports have established that the tissue origins for
mouse brain (resident) microglia and bone-marrow derived
monocytes (BMDM) are distinct , mouse and human brain
tumors harbor potentially distinct and functionally important
subpopulations of infiltrating monocytes and resident microglia.
To identify new macrophage markers relevant to high-grade
glioma, we sought to discover BMDM- and brain microglia-
specific transcripts to enable an analysis of the role of these
mononuclear cell populations in GBM.
In this study, we leveraged four converging analysis methods
across two complementary platforms to identify a series of
differentially-expressed BMDM and brain microglia transcripts.
Following validation by real-time quantitative RT-PCR and flow
cytometry, we selected two representative differentially-
expressed BMDM and microglia surface markers (SELL and
F11R) to demonstrate that infiltrating BMDM in induced murine
malignant glioma acquire F11R expression, which was verified
using allogeneic bone marrow transplantation. To establish the
clinical relevance of F11R to human GBM, we show that F11R
expression correlates positively with glioma malignancy grade
as well as correlates negatively with patient survival
independent of GBM molecular subtype.
Materials and Methods
All mice were maintained in strict accordance with
recommendations in the Guide for the Care and Use of
Laboratory Animals of the National Institutes of Health and
active animal studies protocols approved by the Animal Studies
Committee at the Washington University School of Medicine
(Protocol Numbers: 20110111 and 20120058) and the
Institutional Animal Care And Use Committee (Protocol
Number: 2010-0268) at the Cleveland Clinic Foundation. All
surgeries were performed under Ketamine (100mg/kg) and
Xylazine (10mg/kg) anesthesia, and all efforts were made to
minimize suffering. Animals were also provided 0.25%
Marcaine (Bupivacaine) in the volume of approximately
0.1mL/25g post-surgery to provide pain relief.
All human samples were collected on a protocol approved by
the Institutional Review Board at the Washington University
School of Medicine (Permit Number: 201103323) to comply
with ethical standards as well as government and institutional
regulations. Tissue microarrays cores were received by the
Tissue Procurement Core Facility and Tumor Bank as de-
identified specimens, and consent was waived.
Experimental mouse models
Ntv-a;Ink4a-Arf-/-;Gli-luc mice that develop high-grade
gliomas following intracranial RCAS-PDGFB injection at 6
weeks of age were imaged by luciferase bioluminescence (BLI)
prior to tissue collection at 3 months of age . Control naïve
mice were age and gender matched and not injected with
RCAS. Whole control brains (from naïve mice not receiving
RCAS injections) and tumor masses from glioma-bearing mice
were isolated and collected for flow cytometry analyses.
Allogeneic bone marrow transplantation (BMT) was
employed to induce graft-versus-host-disease (GVHD).
Briefly, bone marrow from B6.SJL-Ptprca Pepcb/BoyJ mice
(Jackson Laboratory, CD45.1, H-2Kb) were T cell depleted
(TCD) using CD90.2 microbeads and an AutoMACS (Miltenyi
Biotec GmbH, Auburn, CA), and injected intravenously into
BALB/c recipient mice (Jackson Laboratory, CD45.2, H-2Kd)
preconditioned with 925 cGy total body irradiation (TBI) prior to
BMT. Following BMT, delayed lymphocyte infusions (DLI) from
C57BL/6 (CD45.2, H-2Kb) were injected on day 11 post-BMT.
For DLI, mouse T cells (total CD4+ and CD8+ T cells) were
isolated from mouse spleens using Miltenyi microbeads and an
AutoMACS (Miltenyi Biotech, Auburn, CA). Control mice
received BMT only, without DLI. Tissues were collected for flow
cytometry analysis at 1, 2, and 3 weeks post-DLI (n=6 per
treatment per time point). All experiments were performed at
least twice on independently-generated mouse cohorts.
Flow cytometry and fluorescence-activated cell sorting
(FACS). Brain and bone marrow were collected from
anesthetized and Ringer’s solution-perfused mice, and
mononuclear cells were isolated for antibody-mediated flow
cytometry and FACS (Table S1) . For intracellular staining,
surface-stained cells were fixed and permeabilized with the BD
Biosciences). Nonspecific staining and gating was determined
using isotype and fluorescence minus one (FMO) controls.
Forward Scatter (FSC) and Side Scatter (SSC) were used to
determine viable cells, and appropriate controls were employed
for compensation and gating . BMDM were gated on cells
that were CD11b+ CD115+ Ly6G-, while brainstem microglia
(BSM) were CD11b+ CD45low Ly6G-- to generate near pure
(>99.9%) populations of BMDM and BSM cells. FACS samples
were placed into TRIzol (Life Technologies Corporation,
Carlsbad, CA) for RNA extraction without any intervening in
vitro tissue culture adaptation or exposure to growth factors.
RNA protocols. Total RNA was isolated from sorted cell
pellets using TRIzol-chloroform extraction, resuspended in
Ambion Nuclease-free water (Life Technologies), snap frozen,
and stored at -80°C. RNA quality and yield were assayed using
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org2 October 2013 | Volume 8 | Issue 10 | e77571
the Agilent Eukaryotic Total RNA 6000 and the BioRad
Experion, then quantified using the Quant-iT™ RNA assay kit
on a Qubit™ Fluorometer (Life Technologies).
Sequencing and microarray platforms. The Ovation®
RNA-Seq method was employed for cDNA synthesis, and
500ng cDNA were used for Illumina library construction with
the Illumina paired-end LT indexing protocol [15,16]. Each
library was sequenced on the Illumina HiSeq, generating
between 15-22Mbp per lane of 100 basepair paired-end reads.
For microarray analyses, cDNA prepared from total RNA
(NuGEN Ovation WTA Pico V.2, NuGEN Technologies, Inc.,
San Carlos, CA) was used to probe the Mouse Exon 1.0ST
array (Affymetrix, Santa Clara, CA).
RNA-seq and microarray analysis methods. Six mouse
samples were sequenced from independently-generated
biological replicates that included three samples of BSM and
three samples of BMDM (six lanes of Illumina data sequenced
in total). Corresponding RNA-Seq paired-end reads were
processed using the TopHat suite  with Cufflinks [18,19] as
well as ALternative EXpression Analysis by Sequencing
Microarray data were analyzed with Partek® Discovery Suite
software (version 6.6, Partek Inc., St. Louis, MO) and Aroma
(http://www.aroma-project.org/). Additional details are provided
in Methods S1.
A fold-change rank for every gene was generated based on
each independent analysis as well as a mean fold-change rank
across the four independent analyses (Cufflinks, ALEXA-Seq,
Aroma, and Partek), culminating in a final list based on the
mean fold-change rank and significance (<0.05 False
Discovery Rate, FDR).
Quantitative real time polymerase chain reaction
(qPCR). cDNA was synthesized using the Omniscript reverse
transcription kit (Qiagen, Alameda, CA). qPCR was performed
Table 1. Genes of predicted membrane-associated proteins
of brain microglia across a wide range of fold changes are
highlighted relative to Cx3cr1
Symbol Full Name
16456F11r F11 receptor1226.641.55E-12152.41 6.21E-06
808.00 3.90E-12185.89 2.26E-04
12520 Cd81 CD81 antigen98.16 6.95E-1446.11 4.77E-04
Figure 1. Differentially-expressed brainstem microglia (BSM) and bone marrow monocyte (BMDM) transcripts. (A) A heat
map was generated from unsupervised hierarchical clustering analysis that included all genes expressed (FPKM >1 in ≥1/6
samples). (B) Candidate genes selected for further validation for comparison to CX3CR1 and CCR2.
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org3 October 2013 | Volume 8 | Issue 10 | e77571
using the Bio-Rad CFX96 Real-Time System (Bio-Rad
Laboratories Inc., Hercules, CA) with SYBR Green detection
(Life Technologies Corporation, Carlsbad, CA) or TaqMan
probe-based chemistry with pre-designed TaqMan® Gene
Expression Assays (Life Technologies). Primer sequences
were designed with Primer-BLAST
www.ncbi.nlm.nih.gov/tools/primer-blast/) to span exon-exon
junctions and target known splice variants (Table S2). The
ΔΔCT method was used to calculate fold expression changes.
Results were analyzed with two-tailed Student’s T-test in
Graphpad Prism 5 software and displayed as mean ± standard
error of the mean (SEM).
Immunohistochemistry. Tissue microarrays containing
cores from normal brain and tumor samples over a range of
glioma malignancy grades
appropriate antibodies (Table
diaminobenzidine development. Images were acquired on a
Nikon Eclipse E600 microscope (Nikon Corporation, Tokyo,
Japan) equipped with a Leica EC3 optical camera and Leica
Application Suite 2.10 (Leica
Germany). Investigators were blind to clinical grades, and
percentages of only the positive mononuclear cells stained by
Table 2. Genes of predicted membrane-associated proteins
of bone marrow monocytes across a wide range of fold
changes are highlighted relative to Ccr2.
Entrez IDFull Name
17064 CD93 antigen83.622.84E-07 23.031.25E-02
12, member a
38.85 3.26E-0692.36 1.35E-05
16590 kit oncogene42.412.84E-04 2.05 3.39E-02
Table 3. F11r and Sell surface expression of donor
monocyte infiltration in chimeric mice with GVHD.
Week 1Week 2Week 3
%Q1: Sell- , F11r+
21.4 +/- 2.0 62.7 +/- 2.290.0 +/- 1.8
%Q2: Sell+ , F11r+
53.3 +/- 4.2 18.3 +/- 2.6 4.9 +/- 1.4
%Q3: Sell+ , F11r-
25.3 +/- 2.4 19.0 +/- 0.45.1 +/- 0.9
F11r and Sell expression of positively-labeled infiltrating donor monocytes (CD11b
+, CD45.1high cells) over the course of 3 weeks of GVHD demonstrates a shift from
53% F11r+ Sell+ cells to 90% F11r+ only cells.
immunohistochemistry were calculated using the total number
of cells in each image (hematoxylin nuclear staining). The
identities of the F11R+ cell types in the tumors were
independently assessed using cell type-specific antibodies to
verify that only mononuclear cells were included in the analysis
(Figure S1). Data were analyzed with Graphpad Prism 5
software using ANOVA, and outliers were excluded using
Statistical analyses. Statistical analyses were performed
using Graphpad Prism 5 statistical software. Group results are
expressed as mean values ± SEM. Data between two groups
were compared using unpaired two-tailed Student’s T-tests.
Data among multiple groups were compared using Kruskal-
Wallis test followed by Dunn’s Multiple Comparison testing,
with a significance level set at p<0.05.
Survival outcome analyses. The Cancer Genome Atlas
(TCGA)  subtype genes were matched in the NCBI Gene
Expression Omnibus (GEO)  dataset accession GSE16011
 by Entrez ID, and after merging, each GBM sample (159
out of 276 total samples) was assigned to a TCGA subtype
(Classical, Mesenchymal, Neural, or Proneural) based on the
10-nearest neighbor method. Each candidate gene expression
level was associated with a survival outcome using the Cox
proportional hazard model; the resulting hazard ratio (HR), its
associated p-value, and a 95% confidence interval (CI) were
reported. Candidate gene expression levels were dichotomized
by median expression levels (across all samples). Kaplan-
Meier curves were produced and log rank tests employed to
compare survival differences between the low/high-expression
groups. Multivariate Cox models based on gene expression,
one continuous, and the other dichotomized by the median
(High vs. Low), were each used to assess the prognostic ability
of each candidate gene after accounting for TCGA molecular
To identify transcripts that potentially distinguish infiltrating
monocytes from brain microglia, we isolated and examined
gene expression of BMDM (CD11b+ CD45high CD115+ Ly6G-
cells) and brainstem microglia (BSM) (CD11b+ CD45low
CD115low Ly6G- cells) pooled from 6-week-old C57BL/6 male
mice (ten mice/set) by FACS. Despite low RNA yields
(0.4-100ng of total RNA), overall good quality RNA was
obtained (Agilent RNA Integrity Number, RIN=7.6-9.0) , and
the resulting cDNA yields (4.9-5.8µg) were used to perform
parallel Affymetrix Mouse Exon 1.0ST microarray and Illumina
RNA-sequencing (Figure S2, Table S3 and S4).
Complementary analysis pipelines Cufflinks and ALEXA-Seq
for RNA-Seq [18–20,25] and Aroma and Partek for microarray
data [26–28] were employed to analyze each platform. Fold
changes and q-values were calculated for each gene, and a
mean fold-change rank was generated by merging the four
independent analyses. High-confidence gene lists were
subsequently generated that included only differentially-
expressed genes identified to have q-values <0.05 by at least
three of the four analysis methods (Table S5). A global
unsupervised analysis, excluding genes not expressed or not
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org4 October 2013 | Volume 8 | Issue 10 | e77571
Figure 2. Transcript and protein validation of genes differentially expressed between BMDM and BSM. (A) BMDM-enriched
transcripts, including Sell (p=0.0019), Met (p=0.0074), Cd93 (p=0.0104), Kit (p=0.0377), and Clec12a (p=0.0049), were more highly
expressed in independently-generated BMDM (n=5) relative to BSM (n=6) using TaqMan and SYBR Green qPCR. BSM-enriched
transcripts, including Mertk (p=0.0001), F11r (p<0.0001), P2ry13 (p=0.0002), Cadm1 (p=0.0002), and Cd81 (p=0.0001), were more
highly expressed in BSM. (B) Flow cytometry analysis of BMDM (CD11b+ and CD115+ cells; R1, left panel) and BSM (CD11b+
CD45low cells; R1, right panel) verified that SELL (C) and CLEC12A (D) were detected on BMDM, and not on BSM, while F11R (E)
and CD81 (F) were detected on BSM and not BMDM. *, p<0.05; **, p<0.01; ***, p<0.001.
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org5 October 2013 | Volume 8 | Issue 10 | e77571
Figure 3. High-grade murine gliomas contain F11r+ microglia and macrophages. (A) Ntv-a Ink4a-Arf-/-;Gli-luc mice develop
tumors following intracranial RCAS-PDGFB injection (n=4). Sell and F11r expression was examined by flow cytometry in
lymphocytes (R1, CD11b- CD45+ cells), microglia (R2, CD11b+ CD45low cells), and macrophages (R3, CD11b+ CD45high cells) within
the tumor and in control naïve brains (n=4) in four separate experiments. (B) More F11r+ microglia and macrophages were identified
in the gliomas relative to the control brains, most notably in the expansion of the R3 population (34% of positively labeled cells),
which is typically a very small percentage in control brains (<2%). The majority of the labeled macrophages in the glioma are
positive for F11r only (Q1; 94%), with few cells infiltrating the glioma positive for Sell only (Q3; 5%) or double positive for both F11r
and Sell (Q2; 1.4%). (C) Bar graphs illustrate the mean (SEM) percentage and SEM for each immune cell population as well as their
corresponding F11r and Sell surface expressions. White bars = control, black bars = glioma.
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org6 October 2013 | Volume 8 | Issue 10 | e77571
Figure 4. BMDM acquire F11r expression following brain infiltration. (A) GVHD was induced in recipient BALB/cJ mice
following total body irradiation (TBI) and bone marrow transplantation (BMT) from T cell depleted (TCD) B6.SJL-Ptprca Pepcb/BoyJ
donors. Following C57BL/6J mouse donor lymphocyte infusions (DLI), immune cell infiltration was assessed by flow cytometry at 1
week, 2 weeks, and 3 weeks post-DLI (n=6 GVHD and n=6 BMT-only control per time point). (B) Control BMT-only mice do not
exhibit GVHD and lack substantial macrophage (R3) or lymphocyte (R1, and R4 in Figure S4) infiltration. There is a very small
CD11b+, CD45.1high monocyte population that expresses F11r in the control brain (<2%; R3, right panel). (C) Chimeric mice with
GVHD have donor monocyte infiltration (CD11b+, CD45.1high cells; R3) with negligible CD11b+, CD45.1low cells (R2). F11r and Sell
expression (right column) of positively-labeled infiltrating donor monocytes (R3) over the course of 3 weeks of GVHD demonstrates
a shift from F11r+ Sell+ cells to F11r+ only cells. (D) Bar graphs illustrate the mean and SEM for each population of immune system
cells over the course of the three weeks following DLI.
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org7 October 2013 | Volume 8 | Issue 10 | e77571
Figure 5. F11R expression correlates with GBM malignancy grade and survival. (A) Immunohistochemistry demonstrates that
human bone marrow sections (n=3) have few F11R+ cells (arrows), while neurologically-normal post-mortem human frontal cortex
brain sections contain numerous F11R+ mononuclear cells (n=3) (p= 0.0016). Endothelial cell labeling in the upper left quadrant of
the brain section represent a positive control for staining. Scale bar = 50µm. Insets depict representative positively-labeled
mononuclear cells. (B) A representative high-grade glioma, GBM, contains many F11R+ cells. Increased percentages of F11R+
cells (Kruskal-Wallis test/Dunn's Multiple Comparison Test, p<0.0001) are observed in high-grade glioma (AA, anaplastic
astrocytoma, n=23; GBM, n=52) relative to low-grade tumors (PA, pilocytic astrocytoma, n=73) or normal brain (NB, n=23). (C)
Kaplan-Meier curves and log rank test demonstrate that increased F11R expression negatively correlated with patient survival (GEO
database: GSE16011, n=159, p=0.0037). (D) F11R was highly expressed in GSE16011 GBM samples assigned to Classical and
Mesenchymal TCGA subtypes relative to the Proneural subtype (p=3.94E-12).
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org8 October 2013 | Volume 8 | Issue 10 | e77571
variably expressed by Cufflinks analysis, demonstrated clear
separation of BMDM from BSM samples (Figure 1A).
We next prioritized candidate transcripts based on their
expression of protein products potentially amenable to flow
cytometry analysis (cell surface markers), and selected ten
transcripts of predicted membrane-associated proteins (5 BSM
and 5 BMDM; Table 1-2) for real-time quantitative PCR (qPCR)
verification (Figure 1B and Figure 2A). We secondarily
validated two representative transcripts for BSM (F11R and
CD81) and BMDM (SELL and CLEC12A) by flow cytometry
(Figure 2B-F). Since CD81 resides inside the cell membrane of
unstimulated microglia, and CLEC12A is expressed at
relatively low levels in monocytes, we chose F11R and SELL
as representative differentially-expressed flow cytometry
markers to study monocyte infiltration in murine glioblastoma.
Since F11r and Sell are expressed in <2% of the total cells
isolated from bone marrow and brain, respectively, we first
sought to determine whether F11r and Sell would retain their
distinctive expression between infiltrative BMDM and resident
microglia populations in the setting of induced murine
malignant glioma using Ntv-a Ink4a-Arf-/-;Gli-luc mice injected
with RCAS-PDGFB (Figure 3A) . Whereas control naïve
mice do not form gliomas nor contain significant numbers of
CD11b+ CD45high BMDM cells (R3; <2% cells) (Figure S3,
Figure 3B), murine gliomas exhibit increased numbers of
lymphocytes (R1; 27% cells) as well as CD11b+ CD45low cells
(R2, microglia; 39% cells) and CD11b+ CD45high cells (R3,
macrophages; 34% cells). While CD11b+ CD45low microglia
were >99% F11r+, the population of macrophages was not
exclusively Sell+. Surprisingly, the majority of these glioma-
associated CD11b+ CD45high macrophages were also F11r+
(94%, Q1) (Figure 3B). Because fewer than 6% of these
glioma-associated F11r+ cells were dendritic cells, B cells, or
NK cells as determined by flow cytometry using CD3, CD4,
CD11c, CD8a, CD19, CD103, and NK1.1 antibodies (data not
Table 4. Relative contributions of TCGA subtypes to the
survival outcome of GBM patients dichotomized by F11R
Variable Subtype contribution P-valueHR and 95% CI
F11R: High vs. Low
0.0189* 1.57 (1.08~2.29)
Classical vs. Mesenchymal
Neural vs. Mesenchymal
0.6608 1.13 (0.66~1.95)
Proneural vs. Mesenchymal
0.0239* 0.6 (0.38~0.93)
A multivariate Cox model was used to account for the contributions of specific
TCGA subtypes of the GBM samples in the GSE16011 GEO dataset, and hazard
ratios (HR), 95% confidence intervals (CI), and associated p-values were
generated. Patients with Proneural subtype tumors had significantly increased
survival compared to patients with the Mesenchymal subtype when dichotomized
by F11R expression (Hazard Ratio (HR) = 0.6; p=0.0239). However, F11R
expression demonstrates the greatest prognostic value independent of molecular
subtype (multivariate Cox model; HR=1.57, p=0.0189). The full analysis is
available in Table S9.
shown), we hypothesized that Sell+ BMDM acquire F11r
expression following brain infiltration in GBM.
To demonstrate that Sell+ monocytes acquire F11r
expression as a consequence of brain infiltration, we employed
allogeneic BMT to track infiltrating monocytes and resident
microglia by virtue of unique CD45 antigen expression (Figure
4A). In this model, chimeric mice with GVHD have CD45.2-
expressing resident microglia and CD45.1-expressing donor
BMDM and exhibit brain mononuclear infiltration following DLI,
whereas control animals (BMT-only) have negligible monocyte
infiltration (Figure 4B). Resident CD45.2 microglia from both
control and GVHD mice are F11r+ and remain F11r+ throughout
the course of the disease (3 weeks) without increasing CD45.2
surface expression (Figure S4, Table S6) or expressing
CD45.1 (Figure 4C, CD11b+ CD45.1- population below R2). As
observed in induced murine GBM, infiltrating CD45.1+ BMDM
(Figure 4C, Table 3) become F11r+ as a function of time after
entry into the brain: in the first week post-DLI, the majority of
the CD45.1+ infiltrating macrophages (R3) express surface Sell
(53% F11r+ Sell+, 25% F11r- Sell+); however, by the third week
post-DLI, 90% of the positively-labeled R3 cells express only
F11r. No other population of infiltrating cells changed their F11r
and Sell expression (data not shown). Collectively, these data
strongly suggest that F11r is a marker of brain macrophages in
the context of murine GBM, regardless of tissue origin,
prompting us to examine its prognostic value in patients with
In human gliomas, 30-50% of the cells are CD68+ or IBA1+
tissue macrophages; however, we previously showed that the
percentage of CD68+ or IBA1+ cells in these human tumors did
not correlate with glioma malignancy grade . To determine
whether F11R expression was associated with increasing
glioma malignancy grade, we first confirmed the differential
protein expression of F11R in histological sections of human
bone marrow and brain (p=0.0016) (Figure 5A). Second, we
quantified the percent of F11R+ cells (both resident and
infiltrating macrophages) in a series of glioma tissue
microarrays containing all glioma malignancy grades. In
contrast to our prior findings with CD68 and IBA1, we found a
positive correlation between the percent of F11R+ cells and
high-grade glioma (p<0.0001) (Figure 5B).
Because the percent of F11R+ cells was greatest in the high-
grade gliomas, we next asked whether F11R expression had
prognostic value in predicting patient survival. Using GBM data
containing 159 specimens (GSE16011)  dichotomized by
median F11R expression, high F11R expression was
associated with reduced patient survival (HR=1.33, log rank
test p=0.0037) (Figure 5C).
demonstrated that differences in survival relate to specific GBM
molecular subtypes , and that expression of macrophage/
microglia-related genes are associated with the Mesenchymal
subtype . In this regard, we also found that IBA1 (AIF1) and
CD68 expression was increased in Mesenchymal subtype
tumors and was associated with decreased survival (Table S7
and S8). However, using a multivariate Cox model to account
for molecular subtype assignments, survival outcomes using
AIF1 and CD68 were strongly influenced by the molecular
subtype (continuous expression analysis: Subtype overall
Previous studies have
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org9 October 2013 | Volume 8 | Issue 10 | e77571
expression analysis: Subtype overall p=0.0072 and p= 0.0128,
respectively; Table S9). As a result, CD68 was no longer
prognostic for patient survival when we account for these
robust subtype contributions (continuous expression analysis:
HR=1.16 p=0.1222, dichotomous
HR=1.36 p=0.0886). AIF1 retained overall prognostic value
(continuous expression analysis:
dichotomous expression analysis:
however, it is strongly dependent on the survival difference
between the Proneural vs. Mesenchymal subtypes. While
F11R was also more highly expressed in the Mesenchymal
subtypes relative to the Proneural subtype (p = 5.74E-11)
(Figure 5D), F11R expression had the greatest independent
prognostic value regardless
(Dichotomized gene expression HR=1.57, p=0.0189) (Table 4).
In contrast, the expression levels of two frequently-employed
macrophage and microglia markers, CCR2 and CX3CR1, were
not predictive of patient survival in GBM (Table S8 and S9).
Together, these data establish F11R as a novel monocyte
predictor of patient outcome in GBM.
and p= 0.0188, respectively; dichotomized
of molecular subtype
In the current study, we applied converging digital genomic
platforms and analysis methodologies to study microglia and
macrophage infiltration in GBM, yielding several important
findings. First, we have created an enabling resource by
generating a comprehensive catalog of differentially-expressed
monocyte/microglia transcripts that may be used to investigate
macrophage populations in the other CNS disease states. In
addition to identifying known macrophages and microglia
markers, including CCR2, LY6C, SELL, SERPINE2, SPARC,
CCL4, CX3CR1, and TREM2 [30–32], we have also discovered
and validated a number of novel differentially-expressed
microglia and monocyte transcripts (P2RY13, CADM1, MET,
CD93, and KIT). Importantly, we identified several new markers
amenable to flow cytometry analysis not previously reported to
be differentially expressed between microglia and BMDM
(F11R, CD81, and CLEC12A).
Second, we leveraged two representative transcripts to
demonstrate that brain macrophages in murine GBM express
F11R regardless of tissue origin (bone marrow versus brain).
Specifically, CD11b+ CD45low brain macrophages (microglia)
and CD11b+ CD45high infiltrating macrophages (macrophages)
express F11R, rather than the SELL BMDM marker.
Furthermore, using allogeneic bone marrow chimeras, we
establish that BMDM entering the brain in the setting of GVHD
convert from a Sell+ macrophage population to an F11r+
macrophage population as a function of time following CNS
infiltration. While it is possible that Sell+ macrophages are
eliminated and a rare population of F11r+ macrophages
preferentially expanded, we favor a model in which F11R
conversion identifies a subset
macrophages. In either case, the local brain environment is
critical for dictating the profile change of infiltrating monocytes.
The importance of monocyte F11R expression to glioma
biology is further supported by the finding that more
established markers of monocytes (e.g., IBA1, CD68),
infiltrating macrophages (e.g., CCR2), and microglia (e.g.,
CX3CR1) do not provide prognostic information for patients
with GBM by both immunohistological and gene expression
analyses across tumor grade and molecular subtypes.
While it is tempting to postulate that F11R expression
confers new biological properties for brain macrophages, the
function of F11R in monocytes has not been investigated.
Previous studies have shown that F11R is expressed in the
basal processes of endothelial tight junctions  where it
influences epithelial morphology and matrix adhesion [34,35],
as well as immune system trafficking [36–38], and may
additionally act as a leukocyte adhesion molecule to facilitate
leukocyte transendothelial migration under inflammatory
conditions . Further studies will be required to determine
whether F11R+ macrophages in the setting of CNS malignancy
represent a unique subset of monocytes that elaborate specific
chemokines and cytokines critical for GBM pathogenesis and
Third, we show that one of these differentially-expressed
transcripts, F11R, serves as a unique predictive biomarker for
malignant glioma. Our previous studies demonstrated that the
percentage of CD68+ and IBA1+ cells were increased in
gliomas relative to non-neoplastic brain, with both low-grade
and high-grade gliomas harboring equivalent percentages of
macrophages. In contrast, we now show that the percentage of
F11R+ macrophages are increased in high-grade relative to
low-grade glioma and that F11R tumor expression is an
independent predictor of patient outcome, regardless of GBM
molecular subtype. Taken together with reports revealing that
microglia are essential drivers of murine glioma formation and
maintenance [1,2,5,8,9], along with studies in other cancer
types highlighting the predictive value of stromal gene
expression patterns in predicting patient outcome [40,41], the
identification of F11R as a marker of a subset of brain
macrophages may facilitate
microenvironmental factors suitable for future stroma-directed
glioma therapy. Similarly, potential derivative microglia/
macrophage gene signatures may enable clinically-useful
deconvolution of existing complex TCGA datasets for
the detection of critical
Methods S1. Additional details of RNA-seq and microarray
Figure S1. F11R staining of the different F11R+ cell types
in the GBM tissue microarray. Representative mononuclear
cells (A-D), neurons (E-H), and endothelial cells (I-J) are
shown. Black arrows denote positively-stained cells. Neoplastic
GFAP+ astroglial cells did not express F11R (K-L), and a
positive mononuclear cell is shown as an internal control for
positive staining (black arrowhead). Scale bars = 10µm.
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org10 October 2013 | Volume 8 | Issue 10 | e77571
Figure S2. RNA assessment of flow-sorted cells. Three
sets of BMDM and BSM samples were flow sorted for Illumina
RNA-Seq (S1-S6), and two additional independent sets were
generated and submitted for the Affymetrix Mouse Exon 1.0ST
microarray; samples S3 and S6 were shared between the two
platforms. Agilent RNA 6000 Pico results reveal minimal RNA
degradation. Each sample was run in duplicate, except sample
S6, due to the limitation of 11 sample wells.
Figure S3. Microglia and macrophages in murine induced-
glioblastoma. Iba1 immunohistochemistry on fixed frozen
sections shows increased microglia and macrophage infiltration
in tumors from Ntv-a Ink4a-Arf-/-;Gli-luc mice injected with
RCAS-PDGFB (Glioma, n=3) and matched controls (n=3).
Scale bars = 100µm.
Figure S4. CD45.2+ expressing cells in the brains of BMT
and GVHD mice. (A) Control BMT mice without GVHD have a
main CD11b+ CD45.2low cell population representing microglia
(R5), and lack lymphocytes (R4) or macrophages (R6). The
microglia are F11r+ only (>99%). (B) Chimera mice with GVHD
have donor lymphocytes (R4) that are primarily CD11b-
CD45.2+ H-2Kb+ and microglia (R5) that are CD11b+ CD45.2+
(>99%). CD45.2high cells that would be denoted by R6 are not
present. Microglia from GVHD mice are almost exclusively
F11r+ (right panels) throughout the 3 weeks, similar to BMT
control mice. Two independent experiments were conducted,
consisting of GVHD mice (n=6) and BMT-only control mice
(n=6) per time cohort.
Table S1. Antibodies.
Table S2. Mouse qPCR primers.
Table S3. RNA quality assessment from flow-sorted cells.
RNA concentrations (ng/µl) were determined using the
Nanodrop 1000 (Thermo Scientific) and Qubit™ (Life
Technologies). For samples S2, S4, and S5, where Qubit
values were below the detection limits (BL), we used RNA
concentration values determined using Agilent software
(0.7ng/µl, 0.04ng/µl, and 0.6ng/µl, respectively). RNA Quality
Index (RQI) values were determined at the Laboratory for
Clinical Genomics using the BioRad Experian assay. RNA
Integrity Index (RIN) values were determined on the Agilent
RNA Pico BioAnalyzer assay using 1:2 dilutions of S1, S3, and
S6, and No dilutions of S2, S4, and S5.
Table S4. RNA transcriptome metrics from flow-sorted
cells. Despite differences in RNA input, all samples had similar
total sequence input, total reads, and reads mapped, and result
in differentially expressed genes and transcripts between the
bone marrow derived monocyte and brain microglia samples.
Table S5. RNA-seq and microarray platforms were
analyzed two ways each, with Cufflinks and ALEXA-Seq,
and with Aroma and Partek, respectively. The analyses
were merged by filtering for transcripts that were significantly
differentially expressed in three out of the four methods, and
then were ranked by average fold change rank across the four
Table S6. F11r and Sell surface expression of resident
microglia in chimeric mice with GVHD. Chimera mice with
GVHD have microglia that are CD11b+ CD45.2+ (>99%).
Microglia from GVHD mice are almost exclusively F11r+
throughout the 3 weeks, similar to BMT control mice.
Table S7. Gene expression levels in GBM samples
(n=159). The GSE16011 GEO dataset was stratified by TCGA
subtype (Classical, n=33; Mesenchymal, n=58; Neural, n=19;
and Proneural, n=49) based on the 10-nearest neighbor
method. The median levels of expression of each gene were
reported with interquartile range and compared using pairwise
Table S8. Survival outcomes of GBM patients stratified by
TCGA molecular subtypes. Gene expression levels of GBM
samples within the GSE16011 GEO dataset stratified by TCGA
subtype were correlated with survival outcomes using the Cox
proportional hazard model to generate hazard ratios (HR), 95%
confidence intervals (CI), and associated p-values. A log rank
test was used to compare survival differences between the low/
high expression groups (dichotomized by the median
Table S9. Relative contributions of TCGA subtypes to the
survival outcome of GBM patients. Continuous and
dichotomized gene expression (high and low expression
relative to the median) was analyzed using a multivariate Cox
model to account for the relative contributions of specific TCGA
subtypes of the GBM samples in the GSE16011 GEO dataset,
and hazard ratios (HR), 95% confidence intervals (CI), and
associated p-values were generated.
We thank the Washington University/Alvin J. Siteman Cancer
Center Flow Cytometry Core, Tumor Tissue Repository, Center
for Biomedical Informatics, and Multiplex Gene Analysis
Genechip Core facilities. We additionally thank Julie Ritchey,
Amy Ly, Scott M Gianino, Corina Anastasaki, Sonika Dahiya,
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org11 October 2013 | Volume 8 | Issue 10 | e77571
and Patrick J Cimino for technical expertise, and Gregory F.
Wu, Sean D. McGrath, and Edward Dela Ziga for advice.
Conceived and designed the experiments: WWP ERM DHG.
Performed the experiments: WWP RJE JC MLC XF. Analyzed
the data: WWP JW TW VM JL RJE JC MLC MG OLG.
Contributed reagents/materials/analysis tools: VM GNF DH
JFD ERM DHG. Wrote the manuscript: WWP DHG. Jointly
supervised research: WWP VM ERM DHG. Contributed to
experimental design: JW TW VM JC JBR DP-W DH.
Contributed to the preparation of the manuscript: WWP JW TW
VM MG OLG. Edited and approved the manuscript: DHG WWP
TW VM JL RJE JC MLC MG OLG JBR GNF DP-W XF DH JFP
1. Roggendorf W, Strupp S, Paulus W (1996) Distribution and
characterization of microglia/macrophages in human brain tumors. Acta
Neuropathol 92: 288–293. doi:10.1007/s004010050520. PubMed:
2. Simmons GW, Pong WW, Emnett RJ, White CR, Gianino SM et al.
(2011) Neurofibromatosis-1 heterozygosity increases microglia in a
spatially and temporally restricted pattern relevant to mouse optic
glioma formation and growth. J Neuropathol Exp Neurol 70: 51–62. doi:
10.1097/NEN.0b013e3182032d37. PubMed: 21157378.
3. Engler JR, Robinson AE, Smirnov I, Hodgson JG, Berger MS et al.
(2012) Increased microglia/macrophage gene expression in a subset of
adult and pediatric astrocytomas. PLOS ONE 7: e43339. doi:10.1371/
journal.pone.0043339. PubMed: 22937035.
4. Rodero M, Marie Y, Coudert M, Blondet E, Mokhtari K et al. (2008)
Polymorphism in the microglial cell-mobilizing CX3CR1 gene is
associated with survival in patients with glioblastoma. J Clin Oncol 26:
5957–5964. doi:10.1200/JCO.2008.17.2833. PubMed: 19001328.
5. Daginakatte GC, Gutmann DH (2007) Neurofibromatosis-1 (Nf1)
heterozygous brain microglia elaborate paracrine factors that promote
Nf1-deficient astrocyte and glioma growth. Hum Mol Genet 16: 1098–
1112. doi:10.1093/hmg/ddm059. PubMed: 17400655.
6. Levy A, Blacher E, Vaknine H, Lund FE, Stein R et al. (2012) CD38
deficiency in the tumor microenvironment attenuates glioma
progression and modulates features of tumor-associated microglia/
macrophages. Neuro Oncol 14: 1037–1049. doi:10.1093/neuonc/
nos121. PubMed: 22700727.
7. Gabrusiewicz K, Ellert-Miklaszewska A, Lipko M, Sielska M,
Frankowska M et al. (2011) Characteristics of the alternative phenotype
of microglia/macrophages and its modulation in experimental gliomas.
PLOS ONE 6: e23902. doi:10.1371/journal.pone.0023902. PubMed:
8. Markovic DS, Vinnakota K, Van Rooijen N, Kiwit J, Synowitz M et al.
(2011) Minocycline reduces glioma expansion and invasion by
attenuating microglial MT1-MMP expression. Brain Behav Immun 25:
624–628. doi:10.1016/j.bbi.2011.01.015. PubMed: 21324352.
9. Pong WW, Higer SB, Gianino SM, Emnett RJ, Gutmann DH (2013)
Reduced microglial CX3CR1 expression delays neurofibromatosis-1
glioma formation. Ann Neurol 73: 303–308. doi:10.1002/ana.23813.
10. Ginhoux F, Greter M, Leboeuf M, Nandi S, See P et al. (2010) Fate
mapping analysis reveals that adult microglia derive from primitive
macrophages. Science 330: 841–845. doi:10.1126/science.1194637.
11. Hambardzumyan D, Amankulor NM, Helmy KY, Becher OJ, Holland EC
(2009) Modeling Adult Gliomas Using RCAS/t-va Technology. Transl
Oncol 2: 89–95. doi:10.1593/tlo.09100. PubMed: 19412424.
12. Choi J, Ritchey J, Prior JL, Holt M, Shannon WD et al. (2010) In vivo
administration of hypomethylating agents mitigate graft-versus-host
disease without sacrificing graft-versus-leukemia. Blood 116: 129–139.
doi:10.1182/blood-2009-12-257253. PubMed: 20424188.
13. Cardona AE, Huang D, Sasse ME, Ransohoff RM (2006) Isolation of
murine microglial cells for RNA analysis or flow cytometry. Nat Protoc
1: 1947–1951. doi:10.1038/nprot.2006.327. PubMed: 17487181.
14. Baumgarth N, Roederer M (2000) A practical approach to multicolor
flow cytometry for immunophenotyping. J Immunol Methods 243: 77–
97. doi:10.1016/S0022-1759(00)00229-5. PubMed: 10986408.
15. Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD et al. (2009)
Recurring mutations found by sequencing an acute myeloid leukemia
genome. N Engl J Med
NEJMoa0903840. PubMed: 19657110.
16. Govindan R, Ding L, Griffith M, Subramanian J, Dees ND et al. (2012)
Genomic landscape of non-small cell lung cancer in smokers and
never-smokers. Cell 150: 1121–1134. doi:10.1016/j.cell.2012.08.024.
361: 1058–1066. doi:10.1056/
17. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice
junctions with RNA-Seq. Bioinformatics 25: 1105–1111. doi:10.1093/
bioinformatics/btp120. PubMed: 19289445.
18. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L (2011)
Improving RNA-Seq expression estimates by correcting for fragment
bias. Genome Biol 12: R22. doi:10.1186/gb-2011-12-3-r22. PubMed:
19. Roberts A, Pimentel H, Trapnell C, Pachter L (2011) Identification of
novel transcripts in annotated genomes using RNA-Seq. Bioinformatics
27: 2325–2329. doi:10.1093/bioinformatics/btr355. PubMed: 21697122.
20. Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy AS et al. (2010)
Alternative expression analysis by RNA sequencing. Nat Methods 7:
843–847. doi:10.1038/nmeth.1503. PubMed: 20835245.
21. Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y et al. (2010)
Integrated genomic analysis identifies clinically relevant subtypes of
glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR,
and NF1. Cancer Cell 17: 98–110. doi:10.1016/j.ccr.2009.12.020.
22. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus:
NCBI gene expression and hybridization array data repository. Nucleic
Acids Res 30: 207–210. doi:10.1093/nar/30.1.207. PubMed: 11752295.
23. Gravendeel LA, Kouwenhoven MC, Gevaert O, De Rooi JJ, Stubbs AP
et al. (2009) Intrinsic gene expression profiles of gliomas are a better
predictor of survival than histology. Cancer Res 69: 9065–9072. doi:
10.1158/0008-5472.CAN-09-2307. PubMed: 19920198.
24. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M et al. (2006)
The RIN: an RNA integrity number for assigning integrity values to RNA
measurements. BMC Mol Biol 7: 3. doi:10.1186/1471-2199-7-3.
25. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G et al. (2010)
Transcript assembly and quantification by RNA-Seq reveals
unannotated transcripts and
differentiation. Nat Biotechnol 28: 511–515. doi:10.1038/nbt.1621.
26. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ et al.
(2003) Exploration, normalization, and summaries of high density
oligonucleotide array probe level data. Biostatistics 4: 249–264. doi:
10.1093/biostatistics/4.2.249. PubMed: 12925520.
27. Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F (2004)
A Model-Based Background Adjustment for Oligonucleotide Expression
Arrays. J Am Stat
28. Bengtsson H, Simpson K, Bullard J, Hansen K (2008) aroma.affymetrix:
A generic framework in R for analyzing small to very large Affymetrix
data sets in bounded memory. Tech Report #745. Department of
Statistics, University of California, Berkeley: 1–9
29. Cooper LA, Gutman DA, Long Q, Johnson BA, Cholleti SR et al. (2010)
The proneural molecular signature is enriched in oligodendrogliomas
and predicts improved survival among diffuse gliomas. PLOS ONE 5:
e12548. doi:10.1371/journal.pone.0012548. PubMed: 20838435.
30. Gautier EL, Shay T, Miller J, Greter M, Jakubzick C et al. (2012) Gene-
expression profiles and transcriptional regulatory pathways that
underlie the identity and diversity of mouse tissue macrophages. Nat
Immunol 13: 1118–1128. doi:10.1038/ni.2419. PubMed: 23023392.
31. Geissmann F, Auffray C, Palframan R, Wirrig C, Ciocca A et al. (2008)
Blood monocytes: distinct subsets, how they relate to dendritic cells,
and their possible roles in the regulation of T-cell responses. Immunol
Cell Biol 86: 398–408. doi:10.1038/icb.2008.19. PubMed: 18392044.
32. Saederup N, Cardona AE, Croft K, Mizutani M, Cotleur AC et al. (2010)
Selective chemokine receptor usage by central nervous system
myeloid cells in CCR2-red fluorescent protein knock-in mice. PLOS
ONE 5: e13693. doi:10.1371/journal.pone.0013693.
isoform switching during cell
Assoc 99: 909–917. doi:
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org12 October 2013 | Volume 8 | Issue 10 | e77571
33. Williams LA, Martin-Padura I, Dejana E, Hogg N, Simmons DL (1999) Download full-text
Identification and characterisation of human Junctional Adhesion
Molecule (JAM). Mol Immunol
S0161-5890(99)00122-4. PubMed: 10698320.
34. Liu Y, Nusrat A, Schnell FJ, Reaves TA, Walsh S et al. (2000) Human
junction adhesion molecule regulates tight junction resealing in
epithelia. J Cell Sci 113 (13): 2363–2374. PubMed: 10852816.
35. Mandell KJ, Babbin BA, Nusrat A, Parkos CA (2005) Junctional
adhesion molecule 1 regulates epithelial cell morphology through
effects on beta1 integrins and Rap1 activity. J Biol Chem 280: 11665–
11674. doi:10.1074/jbc.M412650200. PubMed: 15677455.
36. Cera MR, Del Prete A, Vecchi A, Corada M, Martin-Padura I et al.
(2004) Increased DC trafficking to lymph nodes and contact
hypersensitivity in junctional adhesion molecule-A-deficient mice. J Clin
Invest 114: 729–738. doi:10.1172/JCI21231. PubMed: 15343392.
37. Murakami M, Francavilla C, Torselli I, Corada M, Maddaluno L et al.
(2010) Inactivation of junctional adhesion molecule-A enhances
antitumoral immune response by promoting dendritic cell and T
36: 1175–1188. doi:10.1016/
10.1158/0008-5472.CAN-09-1703. PubMed: 20160037.
38. Woodfin A, Reichel CA, Khandoga A, Corada M, Voisin M-B et al.
(2007) JAM-A mediates neutrophil transmigration in a stimulus-specific
manner in vivo: evidence for sequential roles for JAM-A and PECAM-1
in neutrophil transmigration. Blood 110: 1848–1856. doi: 10.1182/
39. Stamatovic SM, Sladojevic N, Keep RF, Andjelkovic AV (2012)
Relocalization of junctional adhesion molecule A during inflammatory
stimulation of brain endothelial cells. Mol Cell Biol 32: 3414–3427. doi:
10.1128/MCB.06678-11. PubMed: 22733993.
40. Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M et al. (2008)
Stromal gene expression predicts clinical outcome in breast cancer.
Nat Med 14: 518–527. doi:10.1038/nm1764. PubMed: 18438415.
41. Farmer P, Bonnefoi H, Anderle P, Cameron D, Wirapati P et al. (2009)
A stroma-related gene signature predicts resistance to neoadjuvant
chemotherapy in breast cancer. Nat Med 15: 68–74. doi:10.1038/nm.
1908. PubMed: 19122658.
infiltration. Cancer Res 70: 1759–1765. doi:
Transcriptomics Reveal F11R as a GBM Biomarker
PLOS ONE | www.plosone.org13 October 2013 | Volume 8 | Issue 10 | e77571