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EpendymomaAssociated with a Good Prognosis in Immune Gene and Cell Enrichment Is

AllenE. Waziri, Michael Wang and Nicholas K. ForemanHandler,Wei, Bette K. Kleinschmidt-DeMasters, Michael H.

Andrew M. Donson, Diane K. Birks, Valerie N. Barton, Qi

l.0902811http://www.jimmunol.org/content/early/2009/11/16/jimmuno

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Immune Gene and Cell Enrichment Is Associated with a GoodPrognosis in Ependymoma1

Andrew M. Donson,2*§ Diane K. Birks,‡§ Valerie N. Barton,*§ Qi Wei,*§

Bette K. Kleinschmidt-DeMasters,† Michael H. Handler,‡§ Allen E. Waziri,‡ Michael Wang,*§

and Nicholas K. Foreman*‡§

Approximately 50% of children with ependymoma will suffer from tumor recurrences that will ultimately lead to death. Devel-opment of more effective therapies and patient stratification in ependymoma mandates better prognostication. In this study, tumorgene expression microarray profiles from pediatric ependymoma clinical samples were subject to ontological analyses to identifyoutcome-associated biological factors. Histology was subsequently used to evaluate the results of ontological analyses. Ontologyanalyses revealed that genes associated with nonrecurrent ependymoma were predominantly immune function-related. Addition-ally, increased expression of immune-related genes was correlated with longer time to progression in recurrent ependymoma. Ofthose genes associated with both the nonrecurrent phenotype and that positively correlated with time to progression, 95% wereassociated with immune function. Histological analysis of a subset of these immune function genes revealed that their expressionwas restricted to a subpopulation of tumor-infiltrating cells. Analysis of tumor-infiltrating immune cells showed increased infil-tration of CD4� T cells in the nonrecurrent ependymomas. No genomic sequences for SV40, BK, JC, or Merkel polyomaviruseswere found in nonrecurrent ependymoma. This study reveals that up-regulation of immune function genes is the predominantontology associated with a good prognosis in ependymoma and it provides preliminary evidence of a beneficial host proinflam-matory and/or Ag-specific immune response. The Journal of Immunology, 2009, 183: 7428–7440.

E pendymoma (EPN),3 the third most common brain tumorof children, is treated by surgical resection and radiationtherapy (1, 2). Complete resection, often requiring “sec-

ond-look” surgery, is critical for a favorable outcome (3, 4). Ra-diation therapy is also standard, and omission of this results in ahigher number of tumor recurrences (4, 5). Chemotherapy has sofar shown little or no benefit. Unfortunately, �50% of childrentreated with the standard regimen will suffer from tumor recur-rence, which will ultimately result in death (6). This high failurerate represents one of the most significant problems in pediatricneuro-oncology. Despite unfavorable outcome in more than half ofpediatric EPN patients, little progress has been made in the past 20years either in treatment or identification of robust prognostic fac-tors. The ability to identify up-front those EPN patients whosetumor will recur would allow clinicians to try more aggressivetreatment regimens, better stratify patients on various treatmentprotocols, and spare those children whose tumors are unlikely to

recur from overly aggressive treatments. Identification of prognos-tic markers for EPN may have the added benefit of providing in-sight into the biological mechanisms of tumorigenesis, whichcould be exploited for the development of more effective therapies.

To date, study of candidate prognostic markers for pediatricEPN have largely been confined to histological grading accordingto World Health Organization (WHO) tumor classification criteria(7–11), as well as to molecular markers such as Ki-67 (12, 13),survivin (14, 15), human telomerase reverse transcriptase (16), andnucleolin (4). More recently, global molecular analyses such asarray comparative genomic hybridization (17, 18) and gene ex-pression profiling (17, 19–21) have been employed to discoverprognostic chromosomal aberrations or gene expression signa-tures. These global studies have produced an even wider range ofcandidate prognostic markers, although none to date have identi-fied a biological mechanism of recurrence. Despite these numerousstudies, there remains no predictor of tumor recurrence in EPN thatis robustly reproducible from study to study. The driving hypoth-esis for this study is that gene expression patterns differ betweengood and bad prognosis EPN, the details of which will allow forbetter prognostication and provide insights into the biology of re-currence. To achieve this, tumor gene expression profiling com-bined with gene ontology analysis was used as an unbiased ap-proach to identify sets of functionally related genes that wereassociated with clinical outcome in EPN clinical samples. Usingthis approach, it was found that an up-regulation of immune func-tion-related genes was the predominant ontology associated with acomplete response to therapy.

Materials and MethodsPatient cohort

Surgical tumor samples were obtained from 19 patients who presentedbetween 1997 and 2007 for treatment at The Children’s Hospital (Denver,CO) who were diagnosed with EPN according to WHO guidelines (22). All

*Department of Pediatrics, †Departments of Pathology and Neurology, and ‡Depart-ment of Neurosurgery, University of Colorado Denver, Aurora, CO 80045; and §TheChildren’s Hospital, Denver, CO 80045

Received for publication August 25, 2009. Accepted for publication October 5, 2009.

The costs of publication of this article were defrayed in part by the payment of pagecharges. This article must therefore be hereby marked advertisement in accordancewith 18 U.S.C. Section 1734 solely to indicate this fact.1 This work was supported by the Tanner Seebaum Foundation.2 Address correspondence and reprint requests to Andrew M. Donson, Department ofPediatrics, University of Colorado Denver, Mail Stop 8302, P.O. Box 6511, Aurora,CO 80045. E-mail address: andrew.donson@ucdenver.edu3 Abbreviations used in this paper: EPN, ependymoma; AIF-1, allograft inhibitoryfactor-1; DAVID; Database for Annotation, Visualization, and Integrated Discovery;FDR, false discovery rate; FFPE, formalin-fixed paraffin-embedded; gcRMA, Gene-Chip robust multiarray average; GO, Gene Ontology; GSEA, Gene Set EnrichmentAnalysis; IHC, immunohistochemistry; TIL, tumor-infiltrating lymphocyte; TTP,time to progression; GOTERM, Gene Ontology Project term.

Copyright © 2009 by The American Association of Immunologists, Inc. 0022-1767/09/$2.00

The Journal of Immunology

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patients included in the study were treated uniformly, undergoing completetumor resection followed by radiation therapy. Samples used in this studywere obtained at the time of initial resection and before radiation therapy.Two tumor samples were collected for each patient: one sample was snap-frozen in liquid nitrogen, and one was formalin-fixed paraffin-embedded(FFPE) for routine light microscopy. Outcome data were available for allpatients in this study, which was conducted in compliance with Institu-tional Review Board regulations (COMIRB 95-500 and 05-0149). Patientdetails are described in Table I.

Gene expression microarray analysis

Five micrograms of RNA that had been extracted from tumor was ampli-fied, biotin-labeled (Enzo Biochem), and hybridized to Affymetrix HG-U133 Plus 2 microarray chips. Analysis of gene expression microarray datawas performed using the Bioconductor R programming language (www.bioconductor.org). Microarray data were background corrected and nor-malized using the guanine cytosine robust multiarray average (gcRMA)algorithm (23), resulting in log2 expression values. The Affymetrix HG-U133 Plus 2 microarray contains 54,675 probe sets, including multipleprobe sets for the same gene. To reduce errors associated with multipletesting, a filtered list containing a single probe set for each gene thatpossessed the highest gcRMA expression level across all samples usedwas created (18,624 genes). The microarray data discussed in this pub-lication are Minimum Information About a Microarray Experiment(MIAME) compliant and have been deposited in National Center forBiotechnology Information’s Gene Expression Omnibus (24) and areaccessible through Gene Expression Omnibus series accession no.GSE16155 (www.ncbi.nlm.nih.gov/geo/query/).

Gene ontology analyses

Two computer-based ontology analysis tools were used in this study:GSEA (Gene Set Enrichment Analysis: www.broad.mit.edu/gsea) (25) andDAVID (Database for Annotation, Visualization, and Integrated Discov-ery: http://david.abcc.ncifcrf.gov) (26). Both analyses were used to assessgene lists for enrichment of genes annotated with specific Gene OntologyProject terms (GOTERM; www.geneontology.org) (27). Enrichment is de-fined as more genes than would be expected by chance that are associatedwith a specific phenotype or variable.

Briefly, GSEA takes gene expression profiles that have been assigned aspecific phenotype (e.g., nonrecurrent or recurrent) or a continuous variable(e.g., time to progression) and creates a ranked list of genes based on thestrength of the association with the phenotype or variable being interro-gated. The output is an enrichment score with associated false discoveryrate (FDR) adjusted q values and Student’s t test p values for each GeneOntology term. A Benjamini FDR cutoff of 0.25 was used as recommendedby GSEA.

DAVID is a web-based resource that provides Gene Ontology term en-richment scores for lists of genes that, unlike GSEA, have already been

identified by the user as significantly associated with a particular phenotypeor variable.

Immunohistochemistry (IHC)

IHC was performed on 5-�m FFPE tumor tissue sections. Slides weredeparaffinized and then subjected to optimal Ag retrieval protocols. Sub-sequent steps were performed using the EnVision-HRP kit (Dako) on aDako autostainer according to standard protocol. Incubation with primaryAb was performed for 2 h. The following dilutions of primary Ab wereused, and applied to the sections for 1 h: 1/250 allograft inhibitory factor-1(AIF-1) (01-1974) from Waco Pure Chemicals; 1/50 HLA-DR (LN3) and1/40 CD4 (IF6) from Novocastra; 1/100 CD8 (C8/144B), 1/200 CD20(L26), 1/50 CD45 (2B11 � PD7/26), and 1/100 CD68 (PG-M1) fromDako. Each of these Abs stained a discrete subpopulation of cells that weredistributed throughout the parenchyma of the tumor. Slides were analyzedwith the Olympus BX40 microscope, �40 objective lens. Images werecaptured using an Optronics MicroFire 1600 � 1200 camera and Picture-Frame 2.3 imaging software (Optronics). Infiltrating cell abundancies weremeasured as the mean number of positive staining cells per five fields ofview and differential expression between groups was determined using aStudent’s t test with a p value cutoff of 0.05.

Quantitative PCR for viral sequences

Quantitative PCR was performed for SV40, BK, JC, and Merkel polyoma-viruses (PyV). DNA was extracted from surgical specimens using the Gen-trapure DNA extraction kit (Qiagen). All PCR analyses was performedusing the ABI 7500 sequence dector (Applied Biosystems). TaqMan prim-ers and probes were synthesized by an Applied Biosystems facility. Probeswere dual-labeled at the 5� end with FAM and the 3� end with TAMRA. Asequence homology search was performed to ensure the specificity of eachprimer/probe set. TaqMan PCR amplification data were analyzed with soft-ware provided by the manufacturer. All samples were tested in duplicate.Results were expressed as cycle threshold (Ct), which was proportional tothe starting copy numbers and was defined as the PCR cycle at which thefluorescence signal of the PCR kinetics exceeds the threshold value of therespective analysis.

ResultsPatient demographics, tumor grade, or location do not influencerisk of recurrence

In this study the median follow-up for nonrecurrent EPN patientswas 5 years 3 mo. The median time to progression (TTP) for re-current EPN patients was 2 years. No statistically significant dif-ference was seen between recurrent and nonrecurrent EPN patientswith respect to tumor WHO grade, location, age at diagnosis, orgender. In those patients with recurrent EPN, anaplastic EPN(WHO grade III) had a significantly shorter TTP than did classicEPN (WHO grade II) (9 mo vs 32 mo, respectively; p � 0.012). Ashorter TTP was also seen in supratentorial vs infratentorial tumors(3 mo vs 28 mo, respectively; p � 0.044). No significant correla-tion was observed between TTP and either age at diagnosis orgender in recurrent patients.

Genes associated with nonrecurrent EPN are predominantlyimmune-related

Gene expression microarray profiles generated from surgical spec-imens of EPN at initial presentation were separated into 2 groups:nonrecurrent (n � 9) and recurrent (n � 10). In the first geneontology analysis, GSEA was used to identify enriched biologicalfunction in genes associated with either the nonrecurrent or therecurrent groups, respectively termed “the nonrecurrent pheno-type” and “the recurrent phenotype” (Table II). This revealed that“adaptive immune response” was the most highly enrichedGOTERM in the nonrecurrent phenotype with a FDR of 0.059. Inthe recurrent phenotype the most enriched GOTERM was “gluta-mate signaling pathway”, which did not reach statistical signifi-cance by FDR (0.355).

DAVID was used as an additional measure of gene functionenrichment. Two lists of genes that were associated either withnonrecurrent or recurrent phenotypes were generated before

Table I. Patient cohort demographic and tumor detailsa

PatientID Outcome

TTP(months)

Grade(WHO) Location Gender

Age atDx (years)

80 Non — II IF M 2110 Rec 31 II IF M 2135 Rec 6 III ST F 14195 Rec 24 III IF M 2242 Rec 1 III ST F 4246 Rec 35 II IF M 2285 Non — III ST F 5.5306 Non — II IF M 13318 Non — III IF F 2319 Rec 51 II IF F 6364 Rec 35 II IF M 13388 Non — III ST M 11392 Non — II IF F 1393 Rec 18 II IF F 6416 Non — III ST M 5419 Non — II IF M 3459 Non — III IF F 0.5483 Rec 23 II IF M 7507 Rec 5 III IF M 5

a — denotes that tumor did not recur. Non, nonrecurrent; Rec, recurrent; WHO,World Health Organization tumor grade classification; IF, infratentorial; ST, supra-tentorial; Dx, diagnosis.

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DAVID analysis as input. One hundred twenty-seven of the 18,624genes used in this analysis were overexpressed (�2-fold; p �0.05) in nonrecurrent EPN vs recurrent EPN groups. DAVID dem-onstrated that the GOTERM “immune response” was the mostsignificantly enriched ontology (FDR � 9.4 � 10�9) in the non-recurrent phenotype (Table II). In contrast, the most enrichedGOTERM in the recurrent EPN phenotype (47 genes) was “mul-ticellular organismal process”, which was not statistically signifi-cant by FDR (0.98).

Both GSEA and DAVID identified immune function-relatedgenes as the most enriched ontology in the nonrecurrent EPN phe-notype. By contrast, there was no overlap in gene ontology en-richments identified by GSEA and DAVID in the recurrent EPNphenotype, nor did either approach identify any statistically sig-nificant enrichment by FDR.

A detailed analysis of the genes associated with nonrecurrentEPN phenotype was performed to elaborate the results of thecomputer-based ontological analyses described above. All ofthe 127 genes that were overexpressed in nonrecurrent EPN(�2-fold; p � 0.05) were evaluated for their potential roles inany immune-related process as described in peer-reviewed pub-lications. Fifty-four percent (68 out of127) of these genes haddocumented immune-related functions. This approach identifieda number of immune-related genes beyond those identified byGSEA or DAVID; these genes had erroneously not been as-signed an annotation of immune function by GO.

Of the immune-related genes overexpressed in nonrecurrentEPN, a number of genes that are involved in both innate and adap-tive immune responses were identified (Table III). Key initiatingcomponents of both the classical and lectin complement innateresponse pathways (C1QC and MASP1, respectively) and down-stream complement components C3, C3AR1, and ITGB2 (integrin�2) were identified. Multiple MHC class II alleles were identified

(HLA-DMA, HLA-DMB, HLA-DPB1, HLA-DRB5, and CD74).MHC class II is predominantly expressed on APCs, the most pre-dominant of which in the CNS are thought to be the microglia/macrophage population. A number of other genes that are associ-ated with microglia or macrophages were found to beoverexpressed in the nonrecurrent EPN phenotype. Among thesewas AIF1, which is a specific marker of activated microglia/mac-rophages (28). In the context of adaptive immune function, a num-ber of genes specifically involved in T lymphocyte activity wereassociated with the nonrecurrent phenotype, including TCR � con-stant (TRAC), CD37, FYN binding protein (FYB), hepatitis Avirus cellular receptor 2 (HAVCR2), hematopoietic cell-specificLyn substrate-1 (HCLS1), and linker for activation of T cells fam-ily member 2 (LAT2). Other notable immune function-relatedgenes associated with the nonrecurrent phenotype are Fc receptorsCD64A and B, STAT6, TNF (ligand) superfamily, member 10(TRAIL), and cytochrome b-245, � and � polypeptides (CYBAand CYBB).

In recurrent EPN, genes that positively correlate with longertime to progression are predominantly immune-related

EPN generally recur within 3 years of initial presentation. Ourrecurrent EPN cohort had TTP ranging from 1 to 51 mo. To iden-tify genes associated with TTP in EPN that recurred (n � 10),microarray gene expression data were correlated with TTP as acontinuous variable using a modified version of the GSEA ap-proach described above. GSEA identified “humoral immune re-sponse” as the highest enriched GOTERM in genes that positivelycorrelated with TTP (FDR � 0.0694) (Table IV). In the reverseanalysis, “biological process” was the highest enriched GOTERMin genes that negatively correlated with TTP (FDR � 0.223), al-though these were less statistically significant than was the im-mune gene correlation.

Table II. Ontologic analyses of genes associated with the nonrecurrent and recurrent phenotypes in EPNa

GOTERM Annotation GOTERM ID

Enrichment

q Value p Value

GSEA ontology analysisNonrecurrent phenotype

Adaptive immune response 2250 0.059 0.00614Adaptive immune response 2460 0.084 0.0103Humoral immune response 6959 0.146 0.0294Phagocytosis 6909 0.164 0.0120Immune effector process 2252 0.224 0.00789

Recurrent phenotypeGlutamate signaling pathway 7215 0.355 0.0435Aromatic compound metabolic process 6725 0.450 0.0057Regulation of G protein-coupled receptor

protein signaling pathway8277 0.467 0.0468

Meiosis I 7127 0.468 0.0312Regulation of muscle contraction 6937 0.476 0.0248

DAVID ontology analysisNonrecurrent phenotype

Immune response 6955 9.40 � 10�9 4.91 � 10�12

Immune system process 2376 9.84 � 10�9 5.14 � 10�12

Response to wounding 9611 5.79 � 10�7 3.03 � 10�10

Response to external stimulus 9605 1.49 � 10�6 7.77 � 10�10

Inflammatory response 6954 1.70 � 10�5 8.87 � 10�9

Recurrent phenotypeMulticellular organismal process 32501 0.98 7.23 � 10�4

Anatomical structure development 48856 0.95 0.00113Multicellular organismal development 7275 0.99 0.00244Biological regulation 65007 1.00 0.00477Developmental process 32502 0.99 0.00489

a The top five enriched ontologies for each phenotype ranked according to FDR (q value) are shown. Statistical significanceis defined as FDR � 0.25 and Student’s t test p � 0.05.

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Table III. Immune function related genes overexpressed the nonrecurrent EPN phenotypea

Gene Symbol Gene NameAffymetrixProbeset ID

FoldIncrease P Value

Innate immune responseAPOBEC3G Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G 204205_at 4.89 0.00616IRF7 IFN regulatory factor 7 208436_s_at 3.90 0.0134OLR1 Oxidized low-density lipoprotein (lectin-like) receptor 1 210004_at 4.36 0.0417RNASE6 Ribonuclease, RNase A family, k6 213566_at 2.76 0.0332TREM2 Triggering receptor expressed on myeloid cells 2 219725_at 2.92 0.0363TRIM22 Tripartite motif-containing 22 213293_s_at 3.79 0.0264TRIM34 Tripartite motif-containing 34 221044_s_at 4.06 0.00371TYROBP TYRO protein tyrosine kinase binding protein 204122_at 2.30 0.0183

InflammationFRZB Frizzled-related protein 203697_at 6.89 0.00639PLA2G4C Phospholipase A2, group IVC (cytosolic, calcium-independent) 209785_s_at 2.31 0.0464PROS1 Protein S (�) 207808_s_at 2.58 0.0287

ComplementC1QC Complement component 1, q subcomponent, C chain 225353_s_at 2.46 0.00693C3 Complement component 3 217767_at 4.43 0.0155C3AR1 Complement component 3a receptor 1 209906_at 2.25 0.0301ITGB2 Integrin, � 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 2.35 0.0445MASP1 Mannan-binding lectin serine peptidase 1 (C4/C2-activating component

of Ra-reactive factor)232224_at 2.55 0.0170

SERPING1 Serpin peptidase inhibitor, clade G (C1 inhibitor), member 1(angioedema, hereditary)

200986_at 2.77 0.0368

Macrophage/microgliaAIF1 Allograft inflammatory factor 1 215051_x_at 2.62 0.00730B3GALT4 UDP-Gal:� GlcNAc� 1,3-galactosyltransferase, polypeptide 4 210205_at 4.68 9.30 � 10�5

CD74 CD74 Ag (invariant polypeptide of MHC, class II Ag-associated) 209619_at 2.13 0.0296CLEC7A C-type lectin domain family 7 member A 221698_s_at 2.47 0.0312CSF1R CSF1 receptor 203104_at 2.52 0.0204HLA-DMA MHC, class II, DM� 217478_s_at 3.30 0.0319HLA-DMB MHC, class II, DM� 203932_at 2.18 0.0268HLA-DPB1 MHC, class II, DP� 1 201137_s_at 2.36 0.0251HLA-DRB5 MHC, class II, DR� 5 208306_x_at 2.10 0.0449IFIT3 IFN-induced protein with tetratricopeptide repeats 3 229450_at 3.85 0.0229LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains),

member 1229937_x_at 3.33 0.0389

NAPSB Napsin B aspartic peptidase pseudogene 228055_at 3.98 0.0478SYK Spleen tyrosine kinase 226068_at 2.35 0.0307

Adaptive immune responseT cell

CD37 Leukocyte Ag CD37 204192_at 2.52 0.0218FYB FYN-binding protein (FYB-120/130) 211795_s_at 2.96 0.0272GPR65 G protein-coupled receptor 65 214467_at 2.54 0.0184HAVCR2 Hepatitis A virus cellular receptor 2 235458_at 3.50 0.0391HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 2.62 0.0385LAT2 Linker for activation of T cells family member 2 221581_s_at 2.41 0.00564PTPRJ Protein tyrosine phosphatase receptor J 227396_at 2.53 0.0388TRAC TCR� constant 209670_at 3.29 0.00156

B cellBLNK B cell linker 207655_s_at 3.56 0.00115GALNAC4S B cell RAG-associated protein 203066_at 3.06 0.0205LPXN Leupaxin 216250_s_at 2.18 0.00254MS4A6A CD20-like precursor 223280_x_at 2.82 0.0376

AntibodyFCGR1A Fc fragment of IgG, high-affinity Ia (CD64A) 216950_s_at 6.27 0.00324FCGR1B Fc fragment of IgG, high-affinity Ib (CD64B) 214511_x_at 7.07 0.00551

Cytokines, chemokines, and cytokine signalingCNTNAP1 Contactin-associated protein 1 219400_at 2.77 0.0345IFIT1 IFN-induced protein with tetratricopeptide repeats 1 203153_at 2.71 0.0391RARRES3 Retinoic acid receptor responder (tazarotene induced) 3 204070_at 2.38 0.0276STAT6 Signal transducer and activator of transcription 6, IL-4 induced 201331_s_at 4.72 0.00153TNFSF10 TNF (ligand) superfamily, member 10 202688_at 2.68 0.0470XAF1 Xiap-associated factor-1 228617_at 3.97 0.00365

Oxidative burstALOX5 Arachidonate 5-lipoxygenase 204446_s_at 4.25 0.0382CYBA Cytochrome b-245, � polypeptide 203028_s_at 2.67 0.0219CYBB Cytochrome b-245, � polypeptide 203923_s_at 4.08 0.0297HCK Hemopoietic cell kinase 208018_s_at 3.66 0.0247

Tethering and rolling of immune cellsAPBB1IP Amyloid � (a4) precursor protein-binding, family b, member 1

interacting protein230925_at 2.67 0.0454

(Table continues)

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As an input for DAVID, a list of 395 genes that positively cor-related ( p � 0.05 estimated by two-sided Pearson correlation test)with TTP in recurrent EPN (n � 10) was created using all 18,624genes. Using the same approach, a list of 841 genes that were neg-atively correlated with TTP was also created. Similar to the GSEAresults, DAVID confirmed that immune function-related was the mostenriched ontology in genes that positively correlated with TTP (TableIV). Cell cycle-related ontologies were found to be enriched in genesthat were negatively correlated with TTP (FDR � 2.11 � 10�13),with greater statistical significance than the positive TTP correlate-enriched ontologies (FDR � 7.72 � 10�7).

Detailed analysis of genes that positively correlated with TTPin recurrent EPN was performed to elaborate the results of theabove computer-based ontological analyses. Twenty-eight per-cent (110 out of 395) of the genes positively correlated withTTP in recurrent EPN with statistical significance ( p � 0.05)were related to immune function. The results of this analysis,with genes listed and categorized into subgroups according totheir documented role in specific immune mechanisms, are pro-vided in Table V. As found in the previous analysis, a numberof genes beyond those identified by GSEA or DAVID werefound, due to their not having been assigned an annotation of

Table III. (Continued)

Gene Symbol Gene NameAffymetrixProbeset ID

FoldIncrease P Value

ARHGAP4 Rho GTPase-activating protein 4 204425_at 5.45 0.0150CORO1A Coronin, actin binding protein, 1A 209083_at 2.72 0.0187DOCK2 Dedicator of cytokinesis protein 2 213160_at 2.51 0.0274SELPLG Selectin P ligand 209879_at 3.15 0.0180

Hematopoietic cellsADAM28 ADAM metallopeptidase domain 28 205997_at 3.58 0.0373CD300A CD300A Ag 209933_s_at 3.12 0.0300ENTPD1 Ectonucleoside triphosphate diphosphohydrolase 1 209473_at 2.07 0.0491PTPN6 Protein tyrosine phosphatase, nonreceptor type 6 206687_s_at 2.45 0.0140

Miscellaneous immune-relatedFBLN1 Fibulin 1 202994_s_at 2.41 0.0375GIMAP2 GTPase IMAP family member 2 232024_at 2.33 0.0252LY75 Lymphocyte Ag 75 205668_at 4.28 0.0158PLAC8 Placenta-specific 8 219014_at 2.07 0.0330SIGLEC10 Sialic acid binding Ig-like lectin 10 1552807_a_at 3.55 0.00521

a Genes overexpressed in the nonrecurrent EPN phenotype (�2-fold; p � 0.05) are categorized into specific immune categories according to peer-reviewed publications. Thelevel of overexpression is measured by fold increase and Student’s t test ( p value).

Table IV. Gene ontology analyses of genes positively and negatively correlated with longer time toprogression in EPNa

GOTERM Annotation GOTERM ID

Enrichment

q Value Value

GSEA ontology analysisPositively correlated with TTP

Humoral immune response 6959 0.0694 0.00Extracellular structure organization and biogenesis 43062 0.308 0.0248Synaptogenesis 7416 0.356 0.111Synapse organization and biogenesis 50808 0.372 0.0462Phagocytosis 6909 0.463 0.173

Negatively correlated with TTPBiological process 6270 0.223 0.0402Spliceosome assembly 245 0.224 0.0532Biological process 7093 0.227 0.0226Ribonucleotide metabolic process 9259 0.228 0.0331Sister chromatid segregation 819 0.239 0.00592

DAVID ontology analysisPositively correlated with TTP

Defense response 6952 7.72 � 10�7 4.03 � 10�10

Immune response 6955 7.78 � 10�6 4.07 � 10�9

Immune system process 2376 2.66 � 10�5 1.39 � 10�8

Response to wounding 9611 2.55 � 10�4 1.33 � 10�7

Ag binding 3823 3.14 � 10�4 1.75 � 10�7

Negatively correlated with TTPCell cycle 7049 2.11 � 10�13 6.58 � 10�17

DNA metabolic process 6259 4.67 � 10�12 2.41 � 10�15

Nucleobase, nucleoside, nucleotide, andnucleic acid metabolic process

6139 6.58 � 10�12 3.49 � 10�15

Mitotic cell cycle 278 1.68 � 10�11 8.81 � 10�15

Biopolymer metabolic process 43283 4.69 � 10�11 2.45 � 10�14

a The top five enriched ontologies for positive and negative correlates of longer TTP ranked according to FDR (q value) areshown. Statistical significance is defined as FDR � 0.25.

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Table V. Immune-related genes positively correlated with TTPa

Gene Symbol Gene NameAffymetrixProbeset ID R p Value

Innate immune responseCLEC1 Dendritic cell-associated lectin-1 1561899_at 0.64 0.0443GATA6 GATA binding protein 6 210002_at 0.70 0.0233RARA Retinoic acid receptor, � 203750_s_at 0.74 0.0152SIGLEC1 Sialic acid binding Ig-like lectin 1, sialoadhesin 44673_at 0.79 0.00617

Viral responseMX2 Myxovirus (influenza virus) resistance 2 (mouse) 204994_at 0.84 0.00228OAS2 2�-5�-oligoadenylate synthetase 2, 69/71 kDa 204972_at 0.65 0.0408TMC8 Transmembrane channel-like 8 227353_at 0.75 0.0130

InflammationGPR84 G protein-coupled receptor 84 223767_at 0.69 0.0280NFX1 Nuclear transcription factor, X-box binding 1 1553103_at 0.73 0.0171PSD Pleckstrin and Sec7 domain containing 208102_s_at 0.77 0.00982TPSAB1 Tryptase � 215382_x_at 0.66 0.0371

ComplementC2 Complement component 2 203052_at 0.87 0.00109C3 Complement component 3 217767_at 0.67 0.0351C3AR1 Complement component 3a receptor 1 209906_at 0.69 0.0263C6 Complement component 6 210168_at 0.71 0.0211C7 Complement component 7 202992_at 0.68 0.0312CD53 CD53 Ag 203416_at 0.69 0.0277CD59 CD59 Ag, complement regulatory protein 200985_s_at 0.82 0.00361CR1 Complement component (3b/4b) receptor 1 217552_x_at 0.69 0.0264ITGB2 Integrin, � 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 0.73 0.0165

TLR signalingBTK Bruton agammaglobulinemia tyrosine kinase 205504_at 0.75 0.0124

Macrophage/microgliaAIF1 Allograft inflammatory factor 1 215051_x_at 0.63 0.0494APOB48R Apolipoprotein B48 receptor 220023_at 0.86 0.00139CD36 CD36 Ag (collagen type I receptor, thrombospondin receptor) 206488_s_at 0.76 0.0112COLEC12 Collectin subfamily member 12 221019_s_at 0.77 0.00924CSF2RB CSF2 receptor, �, low-affinity (granulocyte-macrophage) 205159_at 0.73 0.0162FMNL1 Formin-like 1 204789_at 0.67 0.0356HLA-DMB MHC, class II, DM� 203932_at 0.66 0.0389LILRA2 Leukocyte Ig-like receptor, subfamily a (with TM domain),

member 2207857_at 0.76 0.0109

LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIMdomains), member 1

229937_x_at 0.74 0.0135

LILRB2 Leukocyte Ig-like receptor, subfamily b (with TM and ITIMdomains), member 2

210146_x_at 0.68 0.0301

LILRB4 Leukocyte Ig-like receptor, subfamily B (with TM and ITIMdomains), member 4

210152_at 0.82 0.00338

LY86 Lymphocyte Ag 86 205859_at 0.87 0.00122PRDM1 PR domain containing 1, with ZNF domain 228964_at 0.64 0.0457SIRPA Protein tyrosine phosphatase, nonreceptor type substrate 1 202897_at 0.66 0.0362SLC15A1 Solute carrier family 15 (oligopeptide transporter), member 1 211349_at 0.75 0.0134

Adaptive immune responseT cell

APBB1IP Amyloid � (a4) precursor protein-binding, family b, member 1interacting protein

230925_at 0.75 0.0127

FYB FYN binding protein (FYB-120/130) 211795_s_at 0.72 0.0201GPR65 G protein-coupled receptor 65 214467_at 0.74 0.0136HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 0.64 0.0472LAT2 Linker for activation of T cells family member 2 221581_s_at 0.83 0.00299TAGAP T cell activation GTPase-activating protein 229723_at 0.67 0.0325TRDV2 TCR� variable 2 210972_x_at 0.65 0.0428

CD4� T cellHP Haptoglobin 206697_s_at 0.70 0.0229HPR Haptoglobin-related protein 208470_s_at 0.69 0.0260SPDEF SAM pointed domain containing ETS transcription factor 214403_x_at 0.67 0.0357MOG Myelin oligodendrocyte glycoprotein 205989_s_at 0.71 0.0222

B cellCD48 CD48 Ag (B cell membrane protein) 204118_at 0.72 0.0190IGHA2 Ig heavy constant � 2 (A2m marker) 214916_x_at 0.70 0.0232IGHG3 Ig heavy constant � 3 (G3m marker) 211868_x_at 0.65 0.0419IGHM Ig heavy locus 209374_s_at 0.64 0.0455IGJ Ig J polypeptide, linker protein for Ig� and Ig� polypeptides 212592_at 0.65 0.0416IGKC Ig� constant 211644_x_at 0.64 0.0460IGKV1D-13 Ig� variable 1D-13 216207_x_at 0.85 0.00199

(Table continues)

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Table V. (Continued)

Gene Symbol Gene NameAffymetrixProbeset ID R p Value

IGLC2 Ig� constant 1 (Mcg marker) 216984_x_at 0.64 0.0447IGLJ3 Ig� joining 3 211798_x_at 0.72 0.0189IGLL3 Similar to omega protein 215946_x_at 0.79 0.00695IGSF6 Ig superfamily, member 6 206420_at 0.66 0.0377RALY RNA binding protein, autoantigenic (hnRNP-associated with lethal

yellow homolog (mouse))224096_at 0.69 0.0275

AntibodyFCGR1A Fc fragment of IgG, high-affinity Ia 216950_s_at 0.64 0.0469FCGR1B Fc� receptor I B2 214511_x_at 0.64 0.0443FCGR2C Fc fragment of IgG, low-affinity IIc, receptor for (CD32) 211395_x_at 0.67 0.0356

Adhesion moleculesITGAL Integrin, � L (Ag CD11A (p180), lymphocyte function-associated

Ag 1; � polypeptide)213475_s_at 0.69 0.0260

SELE Selectin E (endothelial adhesion molecule 1) 206211_at 0.65 0.0422Cytokines, chemokines, and cytokine signaling

CCL7 Chemokine (C-C motif) ligand 7 208075_s_at 0.65 0.0411CCL11 Chemokine (C-C motif) ligand 11 210133_at 0.65 0.0413CCR5 Chemokine (C-C motif) receptor 5 206991_s_at 0.71 0.0220CX3CR1 Chemokine (C-X3-C motif) receptor 1 205898_at 0.70 0.0234CYSLTR1 Cysteinyl leukotriene receptor 1 230866_at 0.78 0.00813EDA2R Ectodysplasin A2 receptor 221399_at 0.65 0.0435IL1B IL-1, � 39402_at 0.67 0.0322IL4I1 IL-4-induced 1 214935_at 0.69 0.0259IRF8 IFN regulatory factor 8 204057_at 0.82 0.00351KLK7 Kallikrein 7 (chymotryptic, stratum corneum) 239381_at 0.67 0.0331MLCK3 MLCK3 protein 1568925_at 0.64 0.0442SAA2 Serum amyloid A2 208607_s_at 0.67 0.0352TRADD TNFRSF1A-associated via death domain 205641_s_at 0.87 0.00110

Oxidative burstCYBASC3 Cytochrome b, ascorbate dependent 3 224735_at 0.67 0.0346CYBB Cytochrome b-245, � polypeptide 203923_s_at 0.69 0.0266GZMA Granzyme A (granzyme 1, cytotoxic T lymphocyte-associated serine

esterase 3)205488_at 0.64 0.0441

HCK Hemopoietic cell kinase 208018_s_at 0.63 0.0491TPSB2 Tryptase � 2 207134_x_at 0.82 0.00387

Tethering and rolling of lymphocytesABCA1 ATP-binding cassette, subfamily A (ABC1), member 1 203505_at 0.73 0.0168ARHGAP4 Rho GTPase-activating protein 4 204425_at 0.75 0.0118ARHGAP9 Rho GTPase-activating protein 9 224451_x_at 0.67 0.0325CORO1A Coronin, actin-binding protein, 1A 209083_at 0.70 0.0239DOCK2 Dedicator of cytokinesis protein 2 213160_at 0.73 0.0156FPRL2 Formyl peptide receptor-like 2 230422_at 0.66 0.0376SELPLG Selectin P ligand 209879_at 0.82 0.00383ST3GAL1 ST3 �-galactoside �-2,3-sialyltransferase 1 244074_at 0.72 0.0182

Expressed in hematopoietic cellsBCL11B B cell CLL/lymphoma 11B (zinc finger protein) 222895_s_at 0.84 0.00234CD109 CD109 Ag (GOV platelet alloantigens) 226545_at 0.73 0.0163GNA15 Guanine nucleotide binding protein (G protein), � 15 205349_at 0.77 0.00868GIMAP4 GTPase IMAP family member 4 219243_at 0.66 0.0364GMFG Glia maturation factor � 204220_at 0.64 0.0468LCP1 Lymphocyte cytosolic protein 1 (L-plastin) 208885_at 0.80 0.0054MYLC2PL Myosin light chain 2, lymphocyte-specific 221660_at 0.65 0.0405NCKAP1L NCK-associated protein 1-like 209734_at 0.76 0.00996NT5E 5�-nucleotidase, ecto (CD73) 203939_at 0.68 0.0311PCSK5 Proprotein convertase subtilisin/kexin type 5 213652_at 0.70 0.0254PSCD4 Pleckstrin homology, Sec7 and coiled-coil domains 4 219183_s_at 0.83 0.00294SPTB Spectrin, �, erythrocytic (Includes spherocytosis, clinical type I) 214145_s_at 0.85 0.00203

Miscellaneous immune-relatedGVIN1 GTPase, very large IFN inducible 1 220577_at 0.65 0.0399IFI27 Interferon, � -inducible protein 27 202411_at 0.72 0.0194INPP5D Inositol polyphosphate-5-phosphatase, 145 kDa 203332_s_at 0.73 0.0166LENG9 Leukocyte receptor cluster (LRC) member 9 1554589_at 0.66 0.0385LGALS9 Lectin, galactoside-binding, soluble, 9 (galectin 9) 203236_s_at 0.68 0.0316MALL Mal, T cell differentiation protein-like 209373_at 0.79 0.00642SEMA3G Sema domain, Ig domain (Ig), short basic domain, secreted,

(semaphorin) 3G219689_at 0.65 0.0403

SERPINB9 Serpin peptidase inhibitor, clade B (ovalbumin), member 9 209723_at 0.68 0.0293SLA SRC-like adapter 203761_at 0.75 0.0133

a Genes positively correlated with a longer time to progression ( p � 0.05) are categorized into specific immune categories according to peer-reviewed publications. The levelby which listed genes are correlated with TTP is measured as a continuous variable by two-sided Pearson correlation test (R) and estimated p value.

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immune function by GO. As seen in the nonrecurrent pheno-type, genes whose expression positively correlated with TTPincluded a number of molecules critically involved in both in-nate and adaptive immune responses. Some overlap in innateand adaptive immune-related genes was observed between thenonrecurrent phenotype and TTP-positive correlates, analyzedin more detail below. As seen in the nonrecurrent phenotype,multiple components of the complement system (C2, C3,C3AR1, C6, C7, CD53, CD59, CR1, ITGB2) and genes asso-ciated with microglia/macrophages (AIF1, CD36, HLA-DMB,LILRA2, LILRB1, LILRB2, LILRB4) and T cells (FYB,HCLS1, LAT2, TAGAP2, TRDV2) were identified in positivecorrelates of TTP in recurrent EPN. The main difference thatdistinguished TTP-positive correlates from the nonrecurrentphenotype was the presence of a significant number of genescommonly expressed by B cells. These included multiple Iggenes (IGHA2, IGHG3, IGHM, IGJ, IGKC, IGKV1D-13,IGLC2, IGLJ3, IGLL3, and IGSF6).

Overlapping genes between the nonrecurrent EPN phenotypeand positive correlates of time to progression are almostentirely immune function-related

A number of genes were identified that were associated with boththe nonrecurrent EPN phenotype and that were also positively cor-related with TTP, emphasizing their involvement in EPN clinicaloutcome as a whole. Ontological analysis revealed that 95% (19out of 20) of genes associated with both nonrecurrence and longerTTP have roles in innate and adaptive immune functions, the de-tails of which are outlined in Table VI. These genes are involvedin complement activity (C3, C3AR1, and ITGB2), macrophageactivity (AIF1), phagocytosis of Ab-coated cells (FCGR1A,FCGR1B, LILRB1, CYBB), Ag presentation (HLA-DMB), lym-phocyte tethering and rolling (SELPLG, CORO1A, DOCK2,APBB1IP, ARHGAP4), and lymphocyte activation (HCLS1,LAT2, FYB, GPR65, HCK). Only phosphorylase kinase, �-1(PHKG1) had no documented evidence of immune involvement,with its known function being as a key glycogenolytic enzyme.However, the dependence of T-lymphocyte activity on glucosemetabolism suggests a potential role in immune function for thisgene (29). In the reverse analysis of genes that overlapped betweenboth bad prognosis groups, that is, the recurrent EPN phenotypeand TTP negative correlates, only two genes were identified: pro-grammed cell death-6 (PDCD6) and opsin-3 (OPN3), which areknown to have roles in TCR-induced apoptosis and photorecep-tion, respectively.

Immune-related genes associated with a good outcome in EPNare expressed by infiltrating cells within the tumor

It was predicted that up-regulated immune-related genes identifiedby ontological analyses were expressed by tumor-infiltrating im-mune cells within patient tumor samples. To provide some evi-dence for this, histology was used to identify individual cells ex-pressing AIF1 and HLA-DR. These immune-related genesassociated with the nonrecurrent phenotype are known to be ex-pressed by microglia/macrophages (28). IHC of AIF1 andHLA-DR protein expression was performed in FFPE tissue of non-recurrent (n � 9) and recurrent (n � 10) EPN. Protein expressionof AIF1 (Fig. 1A) and HLA-DR (Fig. 1B) was restricted to a sub-population of cells in the parenchyma of the tumor with a cellularmorphology that resembled microglia/macrophages. These data in-dicate that at least a subset of immune function gene transcriptsidentified by microarray analyses are derived from tumor-infiltrat-ing immune cells.

To validate the association of AIF1 and HLA-DR expressionwith outcome, the frequency of positively immunostaining cells inthe parenchyma of nonrecurrent and recurrent EPN was measured.This analysis revealed that AIF1 positive staining cells were sig-nificantly more abundant in nonrecurrent EPN (1.91-fold; p �0.0082) (Fig. 1C). HLA-DR was on average 2-fold more abundantin nonrecurrent EPN but was not significant (2.18-fold; p � 0.082)(Fig. 1D). These data recapitulate the results of gene expressionanalysis that demonstrated overexpression of AIF1 and HLA-DR5B in nonrecurrent EPN compared with recurrent EPN.

Tumor-infiltrating immune cells are present in EPN andassociated with a good outcome

In addition to the microglia/macrophage-associated markers ana-lyzed above, T and B cell-related transcripts were found to beassociated with outcome, suggesting a variety of infiltrating im-mune cell subtypes in EPN. IHC was used to identify CD4� Tcells, CD8� T cells, CD45� leukocytes, microphage/microglia(CD68�), and B cells (CD20) in FFPE tissue in nonrecurrent (n �9) and recurrent (n � 10) EPN. Representative staining of theseimmune cell subpopulations is depicted in Fig. 2. Microglia/mac-rophages and CD45� leukocytes were more abundant than T cellsor B cells across all EPN analyzed.

Frequency analysis of infiltrating cells showed increased num-bers of CD4� T cells (16-fold; p � 0.045), CD8� T cells (1.92-fold; p � 0.34), CD45� leukocytes (1.55-fold; p � 0.16), andmicroglia/macrophages (3.06-fold; p � 0.18) in nonrecurrent EPNcompared with recurrent EPN, although only CD4� T cellsreached statistical significance (Fig. 3). Greater numbers of B cellswere observed in recurrent EPN, although this difference was notstatistically significant (3.92-fold; p � 0.12).

Polyomavirus SV40, BK, JC, and Merkel are not present innonrecurrent EPN

A number of genes associated with the nonrecurrent EPN pheno-type are known to be involved in the immune response to viralinfection, in particular IFN regulatory factor-7 (IRF7), tripartitemotif-containing-22 (TRIM22), and apolipoprotein B mRNA ed-iting enzyme, catalytic polypeptide-like 3G (APOBEC3G) (30–32). Earlier research found SV40-like PyV sequences in �50% ofEPN but did not attempt to correlate viral positivity with clinicaloutcome (33). Based on the fact that the percentage of EPN foundto contain viral sequences in this earlier study matched the per-centage of patients who did not suffer from recurrence, it washypothesized that nonrecurrent EPN samples might contain virus,triggering an increase in viral immune response gene expression.Presence of virus has been shown to predict a favorable outcomein other tumor types, such as head and neck cancer, which sup-ported this hypothesis (34). Nonrecurrent (n � 8) and recurrent(n � 9) EPN specimens were therefore screened for the presenceof SV40, BK, JC, and Merkel PyV DNA sequences using quan-titative PCR. No PyV sequences were found in any of the tumorspecimens tested apart from three of nine recurrent EPN thatshowed weak positivity for SV40. Thus, no association betweenthe nonrecurrent EPN phenotype and the presence of PyV DNAsequences was observed. Despite these data, the possibility cannotbe ruled out that some virus other than those tested is present innonrecurrent EPN.

DiscussionThis study provides early circumstantial evidence that in �50%of EPN patients there is a host antitumor immune responseand/or proinflammatory microenvironment that, when com-bined with standard therapy, results in complete eradication of

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remaining residual tumor cells. Additionally, these data providea novel perspective to the clinical problem of how to identifyup-front those children whose EPN will recur by identifying afunctional role for genes associated with prognosis, rather thansimply listing genes as in previous studies (17, 19 –21). Similarto the results of this study, correlation of lymphoma microarrayprofiles with outcome demonstrated that immune gene expres-sion was the predominant feature that predicted survival (35).The presence of tumor reactive T and B cells and tumor-infil-trating lymphocytes (TIL) in clinical specimens has been cor-related with an improved outcome in a number of tumor types(36, 37). The presence of TIL is a prognostic marker in thesetumors and provides a precedent for the correlation of immunecell infiltration with good clinical outcome in EPN seen in the

present study. Prospective validation of immune-related factorsas an up-front prognostic marker in EPN is clearly warrantedbased on the results of this study.

The results of this study provide preliminary evidence forinvolvement of both the innate and adaptive arms of the im-mune response in host control of EPN. The innate immune sys-tem uses a diversity of pathways to recognize and respond toAgs, including potential cancer-specific Ags. The complementsystem is the major humoral component of the innate immunesystem, and multiple complement system genes were found tobe associated with a good outcome in EPN (C1QC, C2, C3, C6,C7, C3AR1, CR1, CD53, CD59, ITGB2, MASP1, SERPING1).Complement-dependent cytotoxicity is thought to be one of themost important mechanisms of action of therapeutic mAbs

Table VI. Overlapping genes that are associated with both the non-recurrent and long PFS phenotypes in ependymoma showing function, cellulardistribution and key reference(s) pertaining to each of the 20 genes

Gene Symbol Gene NameAffymetrix

ID Function Cellular Distribution

AIF1 Allograft inflammatory factor 1 215051_x_at Activation marker involved with membraneruffling

Macrophages/microglia

APBB1IP Amyloid � (a4) precursor protein-binding, family b, member 1interacting protein

230925_at Facilitates T cell receptor-mediated integrinactivation

T cells

ARHGAP4 Rho GTPase-activating protein 4 204425_at Cell movement Predominantly expressed inhematopoietic cells

C3 Complement component 3 217767_at Plays a central role in the activation ofboth the classical and alternativecomplement system

Widely expressed, includingmacrophages

C3AR1 Complement component 3areceptor 1

209906_at Stimulates chemotaxis, granule enzymerelease, and superoxide anion production

Widely expressed in differentiatedhematopoietic cells

CORO1A Coronin, actin-binding protein, 1A 209083_at Accumulates at the leading edge ofmigrating neutrophils and at the nascentphagosome; cytoskeletal modificationvia actin Arp2/3 complex.

Lymphocytes

CYBB Cytochrome b-245, � polypeptide(chronic granulomatousdisease)

203923_s_at Primary component of the microbicidaloxidase system of phagocytes

Monocytes, macrophages

DOCK2 Dedicator of cytokinesis protein 2 213160_at Hematopoietic cell-specific protein that isindispensable for lymphocytechemotaxis

Specific to leukocytes

FCGR1A Fc fragment of IgG, high-affinityIa

216950_s_at CD64. Involved in phagocytosis of Ab-coated cells; high-affinity receptor to theFc region of � Igs

Predominantly expressed onmonocytes and macrophages.

FCGR1B Fc-gamma receptor I B2 214511_x_at Alternative splice form of CD64 Predominantly expressed onmonocytes and macrophages

FYB FYN binding protein(FYB-120/130)

211795_s_at ADAP; adapter protein of the FYN andLCP2 signaling cascades in T cells

Expressed in hematopoietictissues such as myeloid and Tcells, spleen, and thymus; notexpressed in B cells, nor innonlymphoid tissues

GPR65 G protein-coupled receptor 65 214467_at May have a role in activation-induceddifferentiation or cell death of T cells

In organs and cells involved inhematopoiesis

HCK Hemopoietic cell kinase 208018_s_at Part of a signaling pathway coupling the Fcreceptor to the activation of therespiratory burst; may also contribute toneutrophil migration and may regulatethe degranulation process of neutrophils

Expressed predominantly in cellsof the myeloid and Blymphoid lineages

HCLS1 Hematopoietic cell-specific Lynsubstrate 1

202957_at Role in TCR signaling; substrate of the Agreceptor-coupled tyrosine kinase; plays arole in Ag receptor signaling for bothclonal expansion and deletion inlymphoid cells; cytoskeletal modificationvia actin Arp2/3 complex

Only on cells of hematopoieticorigin

HLA-DMB MHC, class II, DM� 203932_at Ag processing and cross-presentation toCD4� T cells

APCs: macrophages, dendritics,B cells

ITGB2 Integrin, � 2 (complementcomponent 3 receptor 3 and 4subunit)

202803_s_at CD18, part of LFA1 and CR3, receptorsfor ICAMs and C3 (component iC3b),respectively. known to participate in celladhesion as well as cell surface-mediated signaling

Leukocytes

LAT2 Linker for activation of T cellsfamily member 2

221581_s_at Involved FCGR1 (CD64)-mediatedsignaling in myeloid cells; couplesactivation of immune receptors and theirassociated kinases with distalintracellular events

Highly expressed in spleen,peripheral blood lymphocytes,and germinal centers of lymphnodes

LILRB1 Leukocyte Ig-like receptor,subfamily b (with TM andITIM domains), member 1

229937_x_at CD85j; monocyte/macrophage Ig receptor;binds PTPN6 when phosphorylated;binds FCER1A and FCGR1B

Expressed primarily bymonocytes, macrophages, anddendritic cells

PHKG1 Phosphorylase kinase, � 1(muscle)

207312_at Crucial glycogenolytic regulatory enzyme Predominantly in muscle andliver

SELPLG Selectin P ligand 209879_at Critical role in tethering and rolling ofneutrophils and T lymphocytes oninflamed endothelial cells

Myeloid and T cells

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against cancer (38). In animal studies of rituximab-mediatedtumor control, the presence of C1Q was found to be critical foreffective complement-dependent cytotoxicity. C1QC, a key ini-

tiating molecule of the classical, Ab-dependent complementpathway, was associated with the nonrecurrent EPN phenotype,but not with a long TTP in recurrent EPN.

FIGURE 1. Immunohistochemicalstaining of (A) AIF-1 and (B)HLA-DR in FFPE tumor sections ofnonrecurrent EPN with hematoxylincounterstaining (�400). Relativeabundancy of (C) AIF-1 and (D)HLA-DR positive infiltrating cells innonrecurrent (non-rec; n � 9) and re-current (rec; n � 10) EPN. Cells werescored using the mean of the numberof positive staining cells in five fieldsof view and statistical significance byStudent’s t test was defined as p �0.05. Horizontal bars represent themean average of scores.

FIGURE 2. Representative infiltration of (A)CD4� and (B) CD8� T cells in nonrecurrentEPN. C, CD45� leukocytes and (D) CD68� mi-croglia were observed in greater numbers than Tcells across all samples. Immunostaining withhematoxylin counterstain (�400).

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A number of genes specifically associated with activity of mi-croglia/macrophages, the key cellular component of the innate im-mune system, were found to correlate with good outcome in EPN.These included AIF1 (28), multiple MHC class II alleles (HLA-DMA, HLA-DMB, HLA-DPB1, HLA-DRB5 and CD74), and leu-kocyte Ig-like receptor, subfamily b1 (LILRB1). IHC analysis ofAIF1 and HLA-DR demonstrated that these molecules are re-stricted to tumor-infiltrating cells. Based on the morphology ofAIF1 and HLA-DR staining, as compared with macrophage/mi-croglia staining in matched samples, it appears that AIF1 andHLA-DR are being expressed by tumor-infiltrating microglia/mac-rophages as expected. The increased expression of AIF1 in non-recurrent EPN vs recurrent EPN was demonstrated by both mi-croarray analysis (2.62-fold; p � 0.0073) and IHC (1.8-fold; p �0.0082). Consistent with our data, MHC class II expression posi-tively correlates with a favorable outcome in a variety of non-CNStumors such as diffuse large B cell lymphoma and hepatocellularcarcinoma (39, 40).

The association of microglia/macrophage-specific transcriptswith improved outcome in EPN is contrary to a number of reportsof compromised microglia/macrophage activity, including reducedMHC class II expression, in other CNS tumors (41, 42). Further-more, there is growing evidence that tumor-infiltrating macro-

phages promote tumor activity in the brain and elsewhere (43, 44).Note that most studies of tumor-infiltrating microglia/macrophagesin the CNS have been performed in glioblastoma, which has ahighly immunosuppressive tumor microenviroment and a uni-formly dismal outcome. Direct comparison of infiltrating micro-glia/macrophages in good outcome EPN and glioblastoma mayshed light on this disparity.

The up-regulation of numerous adaptive immune response re-lated genes was observed in good prognosis EPN. In previous stud-ies of CNS microglia, innate immune system activation was char-acterized by up-regulation of type-1 IFN and MHC class IIexpression, resulting in cross-presentation of viral epitopes toCD4� T cells (45). Consistent with this, in nonrecurrent EPN weobserved overexpression of IFN-induced genes (e.g., IFIT1,IFIT3), multiple MHC class II genes, genes specifically associatedwith T cell activation (e.g., TRAC, CD37, FYB, HAVCR2,HCLS1), and increased frequency of tumor-infiltrating CD4� Tcells. A number of other examples of specific adaptive immuneresponse activities are implied by EPN outcome-associated tran-scripts. These include the observation that B cell-associated tran-scripts are correlated with delayed recurrence, but are not found inthe nonrecurrent phenotype. Although preliminary, this resultsuggest that an Ab response affords some resistance to tumor

FIGURE 3. Tumor-infiltrating im-mune cells in nonrecurrent (non-rec;n � 9) and recurrent (rec; n � 10)EPN. A, CD45� leukocytes, (B)CD4� T cells, (C) CD8� T cells, (D)CD68� microglia/macrophages, and(E) CD20� B cells were identified inparaffin sections of tumor specimensusing immunohistochemistry. Cellswere scored using the mean of thenumber of positive staining cells infive fields of view and statistical sig-nificance by t test was defined as p �0.05. Horizontal bars represent themean average of scores.

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recurrence, but a T cell-specific response is required for completetumor elimination. The presence of a number of T cell function-related transcripts elaborate specific T cell functions in good out-come EPN. For example, polarization of nonrecurrent EPN infil-trating T cells to the Th1 phenotype is implied by the presence ofHAVCR2 (TIM3) (46). Taken together, these data provide pre-liminary evidence that, beyond the simple presence of an immuneinfiltrate, the phenotype and function of that infiltrate may influ-ence clinical outcome in EPN. This conclusion is consistent withthe report by Galon et al. demonstrating that the type (specificallyTh1), density, and location of immune cells within human colo-rectal tumors predict clinical outcome better than current stagingcriteria (47).

Finally, the interactive role between standard surgery, radiother-apy, and chemotherapy and the host immune system cannot beoverstated. There is increasing evidence that antitumor immuneresponses may contribute to the control of cancer after conven-tional chemotherapy, by modulating the equilibrium between thetumor and the immune system (48, 49). This theory may apply toour findings, whereby in those EPN that harbor a host immuneresponse, surgery and radiation therapy may shift the balance ofthe equilibrium in favor of the host by critically increasing theimmune/tumor cell ratio. This would then result in elimination ofremaining residual tumor by the immune system, resulting in afavorable outcome in the patient. In those patients who do notreceive a complete tumor resection or radiation therapy, the equi-librium remains in favor of the tumor, resulting in the poor out-come that is observed in such scenarios. In those patients thatreceive standard therapy but lack an antitumor immune response,residual tumor continues to grow unhindered despite receivingstandard therapy, resulting in tumor recurrence.

Despite the promising results in animal studies of CNS cancerimmunotherapy, clinical trials using immunotherapy in humanshave had limited success (50–53). This failure suggests thatknowledge of the antitumor immune response in the human CNScannot be extrapolated from animal models as previously assumed.A more rational approach to successfully implementing immuno-therapy would be to design strategies based on data taken fromdirect clinical studies of human host anti-CNS tumor immune re-sponses. This report potentially illustrates just such a response,underscoring the value and potential impact of these findings.

AcknowledgmentsWe thank Patsy Ruegg at IHCtech for assistance with immunohistochem-istry and Liza Litzenberger for photographic expertise.

DisclosuresThe authors have no financial conflicts of interest.

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