Approximately 50% of children with ependymoma will suffer from tumor recurrences that will ultimately lead to death. Development of more effective therapies and patient stratification in ependymoma mandates better prognostication. In this study, tumor gene expression microarray profiles from pediatric ependymoma clinical samples were subject to ontological analyses to identify outcome-associated biological factors. Histology was subsequently used to evaluate the results of ontological analyses. Ontology analyses revealed that genes associated with nonrecurrent ependymoma were predominantly immune function-related. Additionally, increased expression of immune-related genes was correlated with longer time to progression in recurrent ependymoma. Of those genes associated with both the nonrecurrent phenotype and that positively correlated with time to progression, 95% were associated with immune function. Histological analysis of a subset of these immune function genes revealed that their expression was restricted to a subpopulation of tumor-infiltrating cells. Analysis of tumor-infiltrating immune cells showed increased infiltration of CD4+ T cells in the nonrecurrent ependymomas. No genomic sequences for SV40, BK, JC, or Merkel polyomaviruses were found in nonrecurrent ependymoma. This study reveals that up-regulation of immune function genes is the predominant ontology associated with a good prognosis in ependymoma and it provides preliminary evidence of a beneficial host proinflammatory and/or Ag-specific immune response.

Ependymoma (EPN),3 the third most common brain tumor of children, is treated by surgical resection and radiation therapy (1, 2). Complete resection, often requiring “second-look” surgery, is critical for a favorable outcome (3, 4). Radiation therapy is also standard, and omission of this results in a higher number of tumor recurrences (4, 5). Chemotherapy has so far shown little or no benefit. Unfortunately, >50% of children treated with the standard regimen will suffer from tumor recurrence, which will ultimately result in death (6). This high failure rate represents one of the most significant problems in pediatric neuro-oncology. Despite unfavorable outcome in more than half of pediatric EPN patients, little progress has been made in the past 20 years either in treatment or identification of robust prognostic factors. The ability to identify up-front those EPN patients whose tumor will recur would allow clinicians to try more aggressive treatment regimens, better stratify patients on various treatment protocols, and spare those children whose tumors are unlikely to recur from overly aggressive treatments. Identification of prognostic markers for EPN may have the added benefit of providing insight into the biological mechanisms of tumorigenesis, which could be exploited for the development of more effective therapies.

To date, study of candidate prognostic markers for pediatric EPN have largely been confined to histological grading according to World Health Organization (WHO) tumor classification criteria (7, 8, 9, 10, 11), as well as to molecular markers such as Ki-67 (12, 13), survivin (14, 15), human telomerase reverse transcriptase (16), and nucleolin (4). More recently, global molecular analyses such as array comparative genomic hybridization (17, 18) and gene expression profiling (17, 19, 20, 21) have been employed to discover prognostic chromosomal aberrations or gene expression signatures. These global studies have produced an even wider range of candidate prognostic markers, although none to date have identified a biological mechanism of recurrence. Despite these numerous studies, there remains no predictor of tumor recurrence in EPN that is robustly reproducible from study to study. The driving hypothesis for this study is that gene expression patterns differ between good and bad prognosis EPN, the details of which will allow for better prognostication and provide insights into the biology of recurrence. To achieve this, tumor gene expression profiling combined with gene ontology analysis was used as an unbiased approach to identify sets of functionally related genes that were associated with clinical outcome in EPN clinical samples. Using this approach, it was found that an up-regulation of immune function-related genes was the predominant ontology associated with a complete response to therapy.

Surgical tumor samples were obtained from 19 patients who presented between 1997 and 2007 for treatment at The Children’s Hospital (Denver, CO) who were diagnosed with EPN according to WHO guidelines (22). All patients included in the study were treated uniformly, undergoing complete tumor resection followed by radiation therapy. Samples used in this study were 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 all patients in this study, which was conducted in compliance with Institutional Review Board regulations (COMIRB 95-500 and 05-0149). Patient details are described in Table I.

Table I.

Patient cohort demographic and tumor detailsa

Patient IDOutcomeTTP (months)Grade (WHO)LocationGenderAge at Dx (years)
80 Non — II IF 
110 Rec 31 II IF 
135 Rec III ST 14 
195 Rec 24 III IF 
242 Rec III ST 
246 Rec 35 II IF 
285 Non — III ST 5.5 
306 Non — II IF 13 
318 Non — III IF 
319 Rec 51 II IF 
364 Rec 35 II IF 13 
388 Non — III ST 11 
392 Non — II IF 
393 Rec 18 II IF 
416 Non — III ST 
419 Non — II IF 
459 Non — III IF 0.5 
483 Rec 23 II IF 
507 Rec III IF 
Patient IDOutcomeTTP (months)Grade (WHO)LocationGenderAge at Dx (years)
80 Non — II IF 
110 Rec 31 II IF 
135 Rec III ST 14 
195 Rec 24 III IF 
242 Rec III ST 
246 Rec 35 II IF 
285 Non — III ST 5.5 
306 Non — II IF 13 
318 Non — III IF 
319 Rec 51 II IF 
364 Rec 35 II IF 13 
388 Non — III ST 11 
392 Non — II IF 
393 Rec 18 II IF 
416 Non — III ST 
419 Non — II IF 
459 Non — III IF 0.5 
483 Rec 23 II IF 
507 Rec III IF 
a

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

Five micrograms of RNA that had been extracted from tumor was amplified, biotin-labeled (Enzo Biochem), and hybridized to Affymetrix HG-U133 Plus 2 microarray chips. Analysis of gene expression microarray data was performed using the Bioconductor R programming language (www.bioconductor.org). Microarray data were background corrected and normalized 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 multiple probe sets for the same gene. To reduce errors associated with multiple testing, a filtered list containing a single probe set for each gene that possessed the highest gcRMA expression level across all samples used was created (18,624 genes). The microarray data discussed in this publication are Minimum Information About a Microarray Experiment (MIAME) compliant and have been deposited in National Center for Biotechnology Information’s Gene Expression Omnibus (24) and are accessible through Gene Expression Omnibus series accession no. GSE16155 (www.ncbi.nlm.nih.gov/geo/query/).

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

Briefly, GSEA takes gene expression profiles that have been assigned a specific phenotype (e.g., nonrecurrent or recurrent) or a continuous variable (e.g., time to progression) and creates a ranked list of genes based on the strength of the association with the phenotype or variable being interrogated. The output is an enrichment score with associated false discovery rate (FDR) adjusted q values and Student’s t test p values for each Gene Ontology term. A Benjamini FDR cutoff of 0.25 was used as recommended by GSEA.

DAVID is a web-based resource that provides Gene Ontology term enrichment scores for lists of genes that, unlike GSEA, have already been identified by the user as significantly associated with a particular phenotype or variable.

IHC was performed on 5-μm FFPE tumor tissue sections. Slides were deparaffinized and then subjected to optimal Ag retrieval protocols. Subsequent steps were performed using the EnVision-HRP kit (Dako) on a Dako autostainer according to standard protocol. Incubation with primary Ab was performed for 2 h. The following dilutions of primary Ab were used, 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) and 1/40 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) from Dako. Each of these Abs stained a discrete subpopulation of cells that were distributed throughout the parenchyma of the tumor. Slides were analyzed with the Olympus BX40 microscope, ×40 objective lens. Images were captured using an Optronics MicroFire 1600 × 1200 camera and PictureFrame 2.3 imaging software (Optronics). Infiltrating cell abundancies were measured as the mean number of positive staining cells per five fields of view and differential expression between groups was determined using a Student’s t test with a p value cutoff of 0.05.

Quantitative PCR was performed for SV40, BK, JC, and Merkel polyomaviruses (PyV). DNA was extracted from surgical specimens using the Gentrapure DNA extraction kit (Qiagen). All PCR analyses was performed using the ABI 7500 sequence dector (Applied Biosystems). TaqMan primers and probes were synthesized by an Applied Biosystems facility. Probes were dual-labeled at the 5′ end with FAM and the 3′ end with TAMRA. A sequence homology search was performed to ensure the specificity of each primer/probe set. TaqMan PCR amplification data were analyzed with software provided by the manufacturer. All samples were tested in duplicate. Results were expressed as cycle threshold (Ct), which was proportional to the starting copy numbers and was defined as the PCR cycle at which the fluorescence signal of the PCR kinetics exceeds the threshold value of the respective analysis.

In this study the median follow-up for nonrecurrent EPN patients was 5 years 3 mo. The median time to progression (TTP) for recurrent EPN patients was 2 years. No statistically significant difference was seen between recurrent and nonrecurrent EPN patients with respect to tumor WHO grade, location, age at diagnosis, or gender. In those patients with recurrent EPN, anaplastic EPN (WHO grade III) had a significantly shorter TTP than did classic EPN (WHO grade II) (9 mo vs 32 mo, respectively; p = 0.012). A shorter TTP was also seen in supratentorial vs infratentorial tumors (3 mo vs 28 mo, respectively; p = 0.044). No significant correlation was observed between TTP and either age at diagnosis or gender in recurrent patients.

Gene expression microarray profiles generated from surgical specimens of EPN at initial presentation were separated into 2 groups: nonrecurrent (n = 9) and recurrent (n = 10). In the first gene ontology analysis, GSEA was used to identify enriched biological function in genes associated with either the nonrecurrent or the recurrent groups, respectively termed “the nonrecurrent phenotype” and “the recurrent phenotype” (Table II). This revealed that “adaptive immune response” was the most highly enriched GOTERM in the nonrecurrent phenotype with a FDR of 0.059. In the recurrent phenotype the most enriched GOTERM was “glutamate signaling pathway”, which did not reach statistical significance by FDR (0.355).

Table II.

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

GOTERM AnnotationGOTERM IDEnrichment
q Valuep Value
GSEA ontology analysis 
 Nonrecurrent phenotype 
  Adaptive immune response 2250 0.059 0.00614 
  Adaptive immune response 2460 0.084 0.0103 
  Humoral immune response 6959 0.146 0.0294 
  Phagocytosis 6909 0.164 0.0120 
  Immune effector process 2252 0.224 0.00789 
 Recurrent phenotype 
  Glutamate signaling pathway 7215 0.355 0.0435 
  Aromatic compound metabolic process 6725 0.450 0.0057 
  Regulation of G protein-coupled receptor protein signaling pathway 8277 0.467 0.0468 
  Meiosis I 7127 0.468 0.0312 
  Regulation of muscle contraction 6937 0.476 0.0248 
DAVID ontology analysis 
 Nonrecurrent 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 phenotype 
  Multicellular organismal process 32501 0.98 7.23 × 10−4 
  Anatomical structure development 48856 0.95 0.00113 
  Multicellular organismal development 7275 0.99 0.00244 
  Biological regulation 65007 1.00 0.00477 
  Developmental process 32502 0.99 0.00489 
GOTERM AnnotationGOTERM IDEnrichment
q Valuep Value
GSEA ontology analysis 
 Nonrecurrent phenotype 
  Adaptive immune response 2250 0.059 0.00614 
  Adaptive immune response 2460 0.084 0.0103 
  Humoral immune response 6959 0.146 0.0294 
  Phagocytosis 6909 0.164 0.0120 
  Immune effector process 2252 0.224 0.00789 
 Recurrent phenotype 
  Glutamate signaling pathway 7215 0.355 0.0435 
  Aromatic compound metabolic process 6725 0.450 0.0057 
  Regulation of G protein-coupled receptor protein signaling pathway 8277 0.467 0.0468 
  Meiosis I 7127 0.468 0.0312 
  Regulation of muscle contraction 6937 0.476 0.0248 
DAVID ontology analysis 
 Nonrecurrent 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 phenotype 
  Multicellular organismal process 32501 0.98 7.23 × 10−4 
  Anatomical structure development 48856 0.95 0.00113 
  Multicellular organismal development 7275 0.99 0.00244 
  Biological regulation 65007 1.00 0.00477 
  Developmental process 32502 0.99 0.00489 
a

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

DAVID was used as an additional measure of gene function enrichment. Two lists of genes that were associated either with nonrecurrent or recurrent phenotypes were generated before DAVID analysis as input. One hundred twenty-seven of the 18,624 genes used in this analysis were overexpressed (<2-fold; p < 0.05) in nonrecurrent EPN vs recurrent EPN groups. DAVID demonstrated that the GOTERM “immune response” was the most significantly enriched ontology (FDR = 9.4 × 10−9) in the nonrecurrent phenotype (Table II). In contrast, the most enriched GOTERM in the recurrent EPN phenotype (47 genes) was “multicellular organismal process”, which was not statistically significant by FDR (0.98).

Both GSEA and DAVID identified immune function-related genes as the most enriched ontology in the nonrecurrent EPN phenotype. By contrast, there was no overlap in gene ontology enrichments identified by GSEA and DAVID in the recurrent EPN phenotype, nor did either approach identify any statistically significant enrichment by FDR.

A detailed analysis of the genes associated with nonrecurrent EPN phenotype was performed to elaborate the results of the computer-based ontological analyses described above. All of the 127 genes that were overexpressed in nonrecurrent EPN (>2-fold; p < 0.05) were evaluated for their potential roles in any immune-related process as described in peer-reviewed publications. Fifty-four percent (68 out of127) of these genes had documented immune-related functions. This approach identified a number of immune-related genes beyond those identified by GSEA or DAVID; these genes had erroneously not been assigned an annotation of immune function by GO.

Of the immune-related genes overexpressed in nonrecurrent EPN, a number of genes that are involved in both innate and adaptive immune responses were identified (Table III). Key initiating components of both the classical and lectin complement innate response pathways (C1QC and MASP1, respectively) and downstream complement components C3, C3AR1, and ITGB2 (integrin β2) 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 predominant of which in the CNS are thought to be the microglia/macrophage population. A number of other genes that are associated with microglia or macrophages were found to be overexpressed in the nonrecurrent EPN phenotype. Among these was AIF1, which is a specific marker of activated microglia/macrophages (28). In the context of adaptive immune function, a number of genes specifically involved in T lymphocyte activity were associated with the nonrecurrent phenotype, including TCR α constant (TRAC), CD37, FYN binding protein (FYB), hepatitis A virus cellular receptor 2 (HAVCR2), hematopoietic cell-specific Lyn substrate-1 (HCLS1), and linker for activation of T cells family member 2 (LAT2). Other notable immune function-related genes associated with the nonrecurrent phenotype are Fc receptors CD64A and B, STAT6, TNF (ligand) superfamily, member 10 (TRAIL), and cytochrome b-245, α and β polypeptides (CYBA and CYBB).

EPN generally recur within 3 years of initial presentation. Our recurrent EPN cohort had TTP ranging from 1 to 51 mo. To identify genes associated with TTP in EPN that recurred (n = 10), microarray gene expression data were correlated with TTP as a continuous variable using a modified version of the GSEA approach described above. GSEA identified “humoral immune response” as the highest enriched GOTERM in genes that positively correlated with TTP (FDR = 0.0694) (Table IV). In the reverse analysis, “biological process” was the highest enriched GOTERM in genes that negatively correlated with TTP (FDR = 0.223), although these were less statistically significant than was the immune gene correlation.

Table IV.

Gene ontology analyses of genes positively and negatively correlated with longer time to progression in EPNa

GOTERM AnnotationGOTERM IDEnrichment
q ValueValue
GSEA ontology analysis 
 Positively correlated with TTP 
  Humoral immune response 6959 0.0694 0.00 
  Extracellular structure organization and biogenesis 43062 0.308 0.0248 
  Synaptogenesis 7416 0.356 0.111 
  Synapse organization and biogenesis 50808 0.372 0.0462 
  Phagocytosis 6909 0.463 0.173 
 Negatively correlated with TTP 
  Biological process 6270 0.223 0.0402 
  Spliceosome assembly 245 0.224 0.0532 
  Biological process 7093 0.227 0.0226 
  Ribonucleotide metabolic process 9259 0.228 0.0331 
  Sister chromatid segregation 819 0.239 0.00592 
DAVID ontology analysis 
 Positively 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 TTP 
  Cell 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, and nucleic 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 
GOTERM AnnotationGOTERM IDEnrichment
q ValueValue
GSEA ontology analysis 
 Positively correlated with TTP 
  Humoral immune response 6959 0.0694 0.00 
  Extracellular structure organization and biogenesis 43062 0.308 0.0248 
  Synaptogenesis 7416 0.356 0.111 
  Synapse organization and biogenesis 50808 0.372 0.0462 
  Phagocytosis 6909 0.463 0.173 
 Negatively correlated with TTP 
  Biological process 6270 0.223 0.0402 
  Spliceosome assembly 245 0.224 0.0532 
  Biological process 7093 0.227 0.0226 
  Ribonucleotide metabolic process 9259 0.228 0.0331 
  Sister chromatid segregation 819 0.239 0.00592 
DAVID ontology analysis 
 Positively 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 TTP 
  Cell 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, and nucleic 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) are shown. Statistical significance is defined as FDR < 0.25.

As an input for DAVID, a list of 395 genes that positively correlated (p < 0.05 estimated by two-sided Pearson correlation test) with TTP in recurrent EPN (n = 10) was created using all 18,624 genes. Using the same approach, a list of 841 genes that were negatively correlated with TTP was also created. Similar to the GSEA results, DAVID confirmed that immune function-related was the most enriched ontology in genes that positively correlated with TTP (Table IV). Cell cycle-related ontologies were found to be enriched in genes that 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 TTP in recurrent EPN was performed to elaborate the results of the above computer-based ontological analyses. Twenty-eight percent (110 out of 395) of the genes positively correlated with TTP 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 to their documented role in specific immune mechanisms, are provided in Table V. As found in the previous analysis, a number of genes beyond those identified by GSEA or DAVID were found, due to their not having been assigned an annotation of immune function by GO. As seen in the nonrecurrent phenotype, genes whose expression positively correlated with TTP included a number of molecules critically involved in both innate and adaptive immune responses. Some overlap in innate and adaptive immune-related genes was observed between the nonrecurrent phenotype and TTP-positive correlates, analyzed in 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 associated with microglia/macrophages (AIF1, CD36, HLA-DMB, LILRA2, LILRB1, LILRB2, LILRB4) and T cells (FYB, HCLS1, LAT2, TAGAP2, TRDV2) were identified in positive correlates of TTP in recurrent EPN. The main difference that distinguished TTP-positive correlates from the nonrecurrent phenotype was the presence of a significant number of genes commonly expressed by B cells. These included multiple Ig genes (IGHA2, IGHG3, IGHM, IGJ, IGKC, IGKV1D-13, IGLC2, IGLJ3, IGLL3, and IGSF6).

A number of genes were identified that were associated with both the nonrecurrent EPN phenotype and that were also positively correlated with TTP, emphasizing their involvement in EPN clinical outcome as a whole. Ontological analysis revealed that 95% (19 out of 20) of genes associated with both nonrecurrence and longer TTP have roles in innate and adaptive immune functions, the details of which are outlined in Table VI. These genes are involved in complement activity (C3, C3AR1, and ITGB2), macrophage activity (AIF1), phagocytosis of Ab-coated cells (FCGR1A, FCGR1B, LILRB1, CYBB), Ag presentation (HLA-DMB), lymphocyte tethering and rolling (SELPLG, CORO1A, DOCK2, APBB1IP, ARHGAP4), and lymphocyte activation (HCLS1, LAT2, FYB, GPR65, HCK). Only phosphorylase kinase, γ-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 glucose metabolism suggests a potential role in immune function for this gene (29). In the reverse analysis of genes that overlapped between both bad prognosis groups, that is, the recurrent EPN phenotype and TTP negative correlates, only two genes were identified: programmed cell death-6 (PDCD6) and opsin-3 (OPN3), which are known to have roles in TCR-induced apoptosis and photoreception, respectively.

Table VI.

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

Gene SymbolGene NameAffymetrix IDFunctionCellular Distribution
AIF1 Allograft inflammatory factor 1 215051_x_at Activation marker involved with membrane ruffling Macrophages/microglia 
APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at Facilitates T cell receptor-mediated integrin activation T cells 
ARHGAP4 Rho GTPase-activating protein 4 204425_at Cell movement Predominantly expressed in hematopoietic cells 
C3 Complement component 3 217767_at Plays a central role in the activation of both the classical and alternative complement system Widely expressed, including macrophages 
C3AR1 Complement component 3a receptor 1 209906_at Stimulates chemotaxis, granule enzyme release, and superoxide anion production Widely expressed in differentiated hematopoietic cells 
CORO1A Coronin, actin-binding protein, 1A 209083_at Accumulates at the leading edge of migrating neutrophils and at the nascent phagosome; cytoskeletal modification via actin Arp2/3 complex. Lymphocytes 
CYBB Cytochrome b-245, β polypeptide (chronic granulomatous disease) 203923_s_at Primary component of the microbicidal oxidase system of phagocytes Monocytes, macrophages 
DOCK2 Dedicator of cytokinesis protein 2 213160_at Hematopoietic cell-specific protein that is indispensable for lymphocyte chemotaxis Specific to leukocytes 
FCGR1A Fc fragment of IgG, high-affinity Ia 216950_s_at CD64. Involved in phagocytosis of Ab-coated cells; high-affinity receptor to the Fc region of γ Igs Predominantly expressed on monocytes and macrophages. 
FCGR1B Fc-gamma receptor I B2 214511_x_at Alternative splice form of CD64 Predominantly expressed on monocytes and macrophages 
FYB FYN binding protein (FYB-120/130) 211795_s_at ADAP; adapter protein of the FYN and LCP2 signaling cascades in T cells Expressed in hematopoietic tissues such as myeloid and T cells, spleen, and thymus; not expressed in B cells, nor in nonlymphoid tissues 
GPR65 G protein-coupled receptor 65 214467_at May have a role in activation-induced differentiation or cell death of T cells In organs and cells involved in hematopoiesis 
HCK Hemopoietic cell kinase 208018_s_at Part of a signaling pathway coupling the Fc receptor to the activation of the respiratory burst; may also contribute to neutrophil migration and may regulate the degranulation process of neutrophils Expressed predominantly in cells of the myeloid and B lymphoid lineages 
HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at Role in TCR signaling; substrate of the Ag receptor-coupled tyrosine kinase; plays a role in Ag receptor signaling for both clonal expansion and deletion in lymphoid cells; cytoskeletal modification via actin Arp2/3 complex Only on cells of hematopoietic origin 
HLA-DMB MHC, class II, DMβ 203932_at Ag processing and cross-presentation to CD4+ T cells APCs: macrophages, dendritics, B cells 
ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at CD18, part of LFA1 and CR3, receptors for ICAMs and C3 (component iC3b), respectively. known to participate in cell adhesion as well as cell surface-mediated signaling Leukocytes 
LAT2 Linker for activation of T cells family member 2 221581_s_at Involved FCGR1 (CD64)-mediated signaling in myeloid cells; couples activation of immune receptors and their associated kinases with distal intracellular events Highly expressed in spleen, peripheral blood lymphocytes, and germinal centers of lymph nodes 
LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at CD85j; monocyte/macrophage Ig receptor; binds PTPN6 when phosphorylated; binds FCER1A and FCGR1B Expressed primarily by monocytes, macrophages, and dendritic cells 
PHKG1 Phosphorylase kinase, γ 1 (muscle) 207312_at Crucial glycogenolytic regulatory enzyme Predominantly in muscle and liver 
SELPLG Selectin P ligand 209879_at Critical role in tethering and rolling of neutrophils and T lymphocytes on inflamed endothelial cells Myeloid and T cells 
Gene SymbolGene NameAffymetrix IDFunctionCellular Distribution
AIF1 Allograft inflammatory factor 1 215051_x_at Activation marker involved with membrane ruffling Macrophages/microglia 
APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at Facilitates T cell receptor-mediated integrin activation T cells 
ARHGAP4 Rho GTPase-activating protein 4 204425_at Cell movement Predominantly expressed in hematopoietic cells 
C3 Complement component 3 217767_at Plays a central role in the activation of both the classical and alternative complement system Widely expressed, including macrophages 
C3AR1 Complement component 3a receptor 1 209906_at Stimulates chemotaxis, granule enzyme release, and superoxide anion production Widely expressed in differentiated hematopoietic cells 
CORO1A Coronin, actin-binding protein, 1A 209083_at Accumulates at the leading edge of migrating neutrophils and at the nascent phagosome; cytoskeletal modification via actin Arp2/3 complex. Lymphocytes 
CYBB Cytochrome b-245, β polypeptide (chronic granulomatous disease) 203923_s_at Primary component of the microbicidal oxidase system of phagocytes Monocytes, macrophages 
DOCK2 Dedicator of cytokinesis protein 2 213160_at Hematopoietic cell-specific protein that is indispensable for lymphocyte chemotaxis Specific to leukocytes 
FCGR1A Fc fragment of IgG, high-affinity Ia 216950_s_at CD64. Involved in phagocytosis of Ab-coated cells; high-affinity receptor to the Fc region of γ Igs Predominantly expressed on monocytes and macrophages. 
FCGR1B Fc-gamma receptor I B2 214511_x_at Alternative splice form of CD64 Predominantly expressed on monocytes and macrophages 
FYB FYN binding protein (FYB-120/130) 211795_s_at ADAP; adapter protein of the FYN and LCP2 signaling cascades in T cells Expressed in hematopoietic tissues such as myeloid and T cells, spleen, and thymus; not expressed in B cells, nor in nonlymphoid tissues 
GPR65 G protein-coupled receptor 65 214467_at May have a role in activation-induced differentiation or cell death of T cells In organs and cells involved in hematopoiesis 
HCK Hemopoietic cell kinase 208018_s_at Part of a signaling pathway coupling the Fc receptor to the activation of the respiratory burst; may also contribute to neutrophil migration and may regulate the degranulation process of neutrophils Expressed predominantly in cells of the myeloid and B lymphoid lineages 
HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at Role in TCR signaling; substrate of the Ag receptor-coupled tyrosine kinase; plays a role in Ag receptor signaling for both clonal expansion and deletion in lymphoid cells; cytoskeletal modification via actin Arp2/3 complex Only on cells of hematopoietic origin 
HLA-DMB MHC, class II, DMβ 203932_at Ag processing and cross-presentation to CD4+ T cells APCs: macrophages, dendritics, B cells 
ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at CD18, part of LFA1 and CR3, receptors for ICAMs and C3 (component iC3b), respectively. known to participate in cell adhesion as well as cell surface-mediated signaling Leukocytes 
LAT2 Linker for activation of T cells family member 2 221581_s_at Involved FCGR1 (CD64)-mediated signaling in myeloid cells; couples activation of immune receptors and their associated kinases with distal intracellular events Highly expressed in spleen, peripheral blood lymphocytes, and germinal centers of lymph nodes 
LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at CD85j; monocyte/macrophage Ig receptor; binds PTPN6 when phosphorylated; binds FCER1A and FCGR1B Expressed primarily by monocytes, macrophages, and dendritic cells 
PHKG1 Phosphorylase kinase, γ 1 (muscle) 207312_at Crucial glycogenolytic regulatory enzyme Predominantly in muscle and liver 
SELPLG Selectin P ligand 209879_at Critical role in tethering and rolling of neutrophils and T lymphocytes on inflamed endothelial cells Myeloid and T cells 

It was predicted that up-regulated immune-related genes identified by ontological analyses were expressed by tumor-infiltrating immune cells within patient tumor samples. To provide some evidence for this, histology was used to identify individual cells expressing AIF1 and HLA-DR. These immune-related genes associated with the nonrecurrent phenotype are known to be expressed by microglia/macrophages (28). IHC of AIF1 and HLA-DR protein expression was performed in FFPE tissue of nonrecurrent (n = 9) and recurrent (n = 10) EPN. Protein expression of AIF1 (Fig. 1,A) and HLA-DR (Fig. 1 B) was restricted to a subpopulation of cells in the parenchyma of the tumor with a cellular morphology that resembled microglia/macrophages. These data indicate that at least a subset of immune function gene transcripts identified by microarray analyses are derived from tumor-infiltrating immune cells.

FIGURE 1.

Immunohistochemical staining of (A) AIF-1 and (B) HLA-DR in FFPE tumor sections of nonrecurrent EPN with hematoxylin counterstaining (×400). Relative abundancy of (C) AIF-1 and (D) HLA-DR positive infiltrating cells in nonrecurrent (non-rec; n = 9) and recurrent (rec; n = 10) EPN. Cells were scored using the mean of the number of positive staining cells in five fields of view and statistical significance by Student’s t test was defined as p < 0.05. Horizontal bars represent the mean average of scores.

FIGURE 1.

Immunohistochemical staining of (A) AIF-1 and (B) HLA-DR in FFPE tumor sections of nonrecurrent EPN with hematoxylin counterstaining (×400). Relative abundancy of (C) AIF-1 and (D) HLA-DR positive infiltrating cells in nonrecurrent (non-rec; n = 9) and recurrent (rec; n = 10) EPN. Cells were scored using the mean of the number of positive staining cells in five fields of view and statistical significance by Student’s t test was defined as p < 0.05. Horizontal bars represent the mean average of scores.

Close modal

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

In addition to the microglia/macrophage-associated markers analyzed above, T and B cell-related transcripts were found to be associated with outcome, suggesting a variety of infiltrating immune cell subtypes in EPN. IHC was used to identify CD4+ T cells, 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 these immune cell subpopulations is depicted in Fig. 2. Microglia/macrophages and CD45+ leukocytes were more abundant than T cells or B cells across all EPN analyzed.

FIGURE 2.

Representative infiltration of (A) CD4+ and (B) CD8+ T cells in nonrecurrent EPN. C, CD45+ leukocytes and (D) CD68+ microglia were observed in greater numbers than T cells across all samples. Immunostaining with hematoxylin counterstain (×400).

FIGURE 2.

Representative infiltration of (A) CD4+ and (B) CD8+ T cells in nonrecurrent EPN. C, CD45+ leukocytes and (D) CD68+ microglia were observed in greater numbers than T cells across all samples. Immunostaining with hematoxylin counterstain (×400).

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Frequency analysis of infiltrating cells showed increased numbers 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), and microglia/macrophages (3.06-fold; p = 0.18) in nonrecurrent EPN compared with recurrent EPN, although only CD4+ T cells reached statistical significance (Fig. 3). Greater numbers of B cells were observed in recurrent EPN, although this difference was not statistically significant (3.92-fold; p = 0.12).

FIGURE 3.

Tumor-infiltrating immune 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 in paraffin sections of tumor specimens using immunohistochemistry. Cells were scored using the mean of the number of positive staining cells in five fields of view and statistical significance by t test was defined as p < 0.05. Horizontal bars represent the mean average of scores.

FIGURE 3.

Tumor-infiltrating immune 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 in paraffin sections of tumor specimens using immunohistochemistry. Cells were scored using the mean of the number of positive staining cells in five fields of view and statistical significance by t test was defined as p < 0.05. Horizontal bars represent the mean average of scores.

Close modal

A number of genes associated with the nonrecurrent EPN phenotype are known to be involved in the immune response to viral infection, in particular IFN regulatory factor-7 (IRF7), tripartite motif-containing-22 (TRIM22), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G (APOBEC3G) (30, 31, 32). Earlier research found SV40-like PyV sequences in ∼50% of EPN but did not attempt to correlate viral positivity with clinical outcome (33). Based on the fact that the percentage of EPN found to contain viral sequences in this earlier study matched the percentage of patients who did not suffer from recurrence, it was hypothesized 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 outcome in other tumor types, such as head and neck cancer, which supported this hypothesis (34). Nonrecurrent (n = 8) and recurrent (n = 9) EPN specimens were therefore screened for the presence of SV40, BK, JC, and Merkel PyV DNA sequences using quantitative PCR. No PyV sequences were found in any of the tumor specimens tested apart from three of nine recurrent EPN that showed weak positivity for SV40. Thus, no association between the nonrecurrent EPN phenotype and the presence of PyV DNA sequences was observed. Despite these data, the possibility cannot be ruled out that some virus other than those tested is present in nonrecurrent EPN.

This study provides early circumstantial evidence that in ∼50% of EPN patients there is a host antitumor immune response and/or proinflammatory microenvironment that, when combined with standard therapy, results in complete eradication of remaining residual tumor cells. Additionally, these data provide a novel perspective to the clinical problem of how to identify up-front those children whose EPN will recur by identifying a functional role for genes associated with prognosis, rather than simply listing genes as in previous studies (17, 19, 20, 21). Similar to the results of this study, correlation of lymphoma microarray profiles with outcome demonstrated that immune gene expression was the predominant feature that predicted survival (35). The presence of tumor reactive T and B cells and tumor-infiltrating lymphocytes (TIL) in clinical specimens has been correlated with an improved outcome in a number of tumor types (36, 37). The presence of TIL is a prognostic marker in these tumors and provides a precedent for the correlation of immune cell infiltration with good clinical outcome in EPN seen in the present study. Prospective validation of immune-related factors as an up-front prognostic marker in EPN is clearly warranted based on the results of this study.

The results of this study provide preliminary evidence for involvement of both the innate and adaptive arms of the immune response in host control of EPN. The innate immune system uses a diversity of pathways to recognize and respond to Ags, including potential cancer-specific Ags. The complement system is the major humoral component of the innate immune system, and multiple complement system genes were found to be 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 the most important mechanisms of action of therapeutic mAbs against cancer (38). In animal studies of rituximab-mediated tumor control, the presence of C1Q was found to be critical for effective complement-dependent cytotoxicity. C1QC, a key initiating molecule of the classical, Ab-dependent complement pathway, was associated with the nonrecurrent EPN phenotype, but not with a long TTP in recurrent EPN.

A number of genes specifically associated with activity of microglia/macrophages, the key cellular component of the innate immune 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 leukocyte Ig-like receptor, subfamily b1 (LILRB1). IHC analysis of AIF1 and HLA-DR demonstrated that these molecules are restricted to tumor-infiltrating cells. Based on the morphology of AIF1 and HLA-DR staining, as compared with macrophage/microglia staining in matched samples, it appears that AIF1 and HLA-DR are being expressed by tumor-infiltrating microglia/macrophages as expected. The increased expression of AIF1 in nonrecurrent EPN vs recurrent EPN was demonstrated by both microarray analysis (2.62-fold; p = 0.0073) and IHC (1.8-fold; p = 0.0082). Consistent with our data, MHC class II expression positively correlates with a favorable outcome in a variety of non-CNS tumors such as diffuse large B cell lymphoma and hepatocellular carcinoma (39, 40).

The association of microglia/macrophage-specific transcripts with improved outcome in EPN is contrary to a number of reports of compromised microglia/macrophage activity, including reduced MHC class II expression, in other CNS tumors (41, 42). Furthermore, there is growing evidence that tumor-infiltrating macrophages promote tumor activity in the brain and elsewhere (43, 44). Note that most studies of tumor-infiltrating microglia/macrophages in the CNS have been performed in glioblastoma, which has a highly immunosuppressive tumor microenviroment and a uniformly dismal outcome. Direct comparison of infiltrating microglia/macrophages in good outcome EPN and glioblastoma may shed light on this disparity.

The up-regulation of numerous adaptive immune response related genes was observed in good prognosis EPN. In previous studies of CNS microglia, innate immune system activation was characterized by up-regulation of type-1 IFN and MHC class II expression, resulting in cross-presentation of viral epitopes to CD4+ T cells (45). Consistent with this, in nonrecurrent EPN we observed overexpression of IFN-induced genes (e.g., IFIT1, IFIT3), multiple MHC class II genes, genes specifically associated with T cell activation (e.g., TRAC, CD37, FYB, HAVCR2, HCLS1), and increased frequency of tumor-infiltrating CD4+ T cells. A number of other examples of specific adaptive immune response activities are implied by EPN outcome-associated transcripts. These include the observation that B cell-associated transcripts are correlated with delayed recurrence, but are not found in the nonrecurrent phenotype. Although preliminary, this result suggest that an Ab response affords some resistance to tumor recurrence, but a T cell-specific response is required for complete tumor elimination. The presence of a number of T cell function-related transcripts elaborate specific T cell functions in good outcome EPN. For example, polarization of nonrecurrent EPN infiltrating T cells to the Th1 phenotype is implied by the presence of HAVCR2 (TIM3) (46). Taken together, these data provide preliminary evidence that, beyond the simple presence of an immune infiltrate, the phenotype and function of that infiltrate may influence clinical outcome in EPN. This conclusion is consistent with the report by Galon et al. demonstrating that the type (specifically Th1), density, and location of immune cells within human colorectal tumors predict clinical outcome better than current staging criteria (47).

Finally, the interactive role between standard surgery, radiotherapy, and chemotherapy and the host immune system cannot be overstated. There is increasing evidence that antitumor immune responses may contribute to the control of cancer after conventional chemotherapy, by modulating the equilibrium between the tumor and the immune system (48, 49). This theory may apply to our findings, whereby in those EPN that harbor a host immune response, surgery and radiation therapy may shift the balance of the equilibrium in favor of the host by critically increasing the immune/tumor cell ratio. This would then result in elimination of remaining residual tumor by the immune system, resulting in a favorable outcome in the patient. In those patients who do not receive a complete tumor resection or radiation therapy, the equilibrium remains in favor of the tumor, resulting in the poor outcome that is observed in such scenarios. In those patients that receive standard therapy but lack an antitumor immune response, residual tumor continues to grow unhindered despite receiving standard therapy, resulting in tumor recurrence.

Despite the promising results in animal studies of CNS cancer immunotherapy, clinical trials using immunotherapy in humans have had limited success (50, 51, 52, 53). This failure suggests that knowledge of the antitumor immune response in the human CNS cannot be extrapolated from animal models as previously assumed. A more rational approach to successfully implementing immunotherapy would be to design strategies based on data taken from direct clinical studies of human host anti-CNS tumor immune responses. This report potentially illustrates just such a response, underscoring the value and potential impact of these findings.

We thank Patsy Ruegg at IHCtech for assistance with immunohistochemistry and Liza Litzenberger for photographic expertise.

The authors have no financial conflicts of interest.

Table III.

Immune function related genes overexpressed the nonrecurrent EPN phenotypea

Gene SymbolGene NameAffymetrix Probeset IDFold IncreaseP Value
Innate immune response 
  APOBEC3G Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G 204205_at 4.89 0.00616 
  IRF7 IFN regulatory factor 7 208436_s_at 3.90 0.0134 
  OLR1 Oxidized low-density lipoprotein (lectin-like) receptor 1 210004_at 4.36 0.0417 
  RNASE6 Ribonuclease, RNase A family, k6 213566_at 2.76 0.0332 
  TREM2 Triggering receptor expressed on myeloid cells 2 219725_at 2.92 0.0363 
  TRIM22 Tripartite motif-containing 22 213293_s_at 3.79 0.0264 
  TRIM34 Tripartite motif-containing 34 221044_s_at 4.06 0.00371 
  TYROBP TYRO protein tyrosine kinase binding protein 204122_at 2.30 0.0183 
 Inflammation 
  FRZB Frizzled-related protein 203697_at 6.89 0.00639 
  PLA2G4C Phospholipase A2, group IVC (cytosolic, calcium-independent) 209785_s_at 2.31 0.0464 
  PROS1 Protein S (α) 207808_s_at 2.58 0.0287 
 Complement 
  C1QC Complement component 1, q subcomponent, C chain 225353_s_at 2.46 0.00693 
  C3 Complement component 3 217767_at 4.43 0.0155 
  C3AR1 Complement component 3a receptor 1 209906_at 2.25 0.0301 
  ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 2.35 0.0445 
  MASP1 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/microglia 
  AIF1 Allograft inflammatory factor 1 215051_x_at 2.62 0.00730 
  B3GALT4 UDP-Gal:β GlcNAcβ 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 0.0296 
  CLEC7A C-type lectin domain family 7 member A 221698_s_at 2.47 0.0312 
  CSF1R CSF1 receptor 203104_at 2.52 0.0204 
  HLA-DMA MHC, class II, DMα 217478_s_at 3.30 0.0319 
  HLA-DMB MHC, class II, DMβ 203932_at 2.18 0.0268 
  HLA-DPB1 MHC, class II, DPβ 1 201137_s_at 2.36 0.0251 
  HLA-DRB5 MHC, class II, DRβ 5 208306_x_at 2.10 0.0449 
  IFIT3 IFN-induced protein with tetratricopeptide repeats 3 229450_at 3.85 0.0229 
  LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at 3.33 0.0389 
  NAPSB Napsin B aspartic peptidase pseudogene 228055_at 3.98 0.0478 
  SYK Spleen tyrosine kinase 226068_at 2.35 0.0307 
Adaptive immune response 
 T cell 
  CD37 Leukocyte Ag CD37 204192_at 2.52 0.0218 
  FYB FYN-binding protein (FYB-120/130) 211795_s_at 2.96 0.0272 
  GPR65 G protein-coupled receptor 65 214467_at 2.54 0.0184 
  HAVCR2 Hepatitis A virus cellular receptor 2 235458_at 3.50 0.0391 
  HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 2.62 0.0385 
  LAT2 Linker for activation of T cells family member 2 221581_s_at 2.41 0.00564 
  PTPRJ Protein tyrosine phosphatase receptor J 227396_at 2.53 0.0388 
  TRAC TCRα constant 209670_at 3.29 0.00156 
 B cell 
  BLNK B cell linker 207655_s_at 3.56 0.00115 
  GALNAC4S B cell RAG-associated protein 203066_at 3.06 0.0205 
  LPXN Leupaxin 216250_s_at 2.18 0.00254 
  MS4A6A CD20-like precursor 223280_x_at 2.82 0.0376 
 Antibody 
  FCGR1A Fc fragment of IgG, high-affinity Ia (CD64A) 216950_s_at 6.27 0.00324 
  FCGR1B Fc fragment of IgG, high-affinity Ib (CD64B) 214511_x_at 7.07 0.00551 
Cytokines, chemokines, and cytokine signaling 
 CNTNAP1 Contactin-associated protein 1 219400_at 2.77 0.0345 
 IFIT1 IFN-induced protein with tetratricopeptide repeats 1 203153_at 2.71 0.0391 
 RARRES3 Retinoic acid receptor responder (tazarotene induced) 3 204070_at 2.38 0.0276 
 STAT6 Signal transducer and activator of transcription 6, IL-4 induced 201331_s_at 4.72 0.00153 
 TNFSF10 TNF (ligand) superfamily, member 10 202688_at 2.68 0.0470 
 XAF1 Xiap-associated factor-1 228617_at 3.97 0.00365 
Oxidative burst 
 ALOX5 Arachidonate 5-lipoxygenase 204446_s_at 4.25 0.0382 
 CYBA Cytochrome b-245, α polypeptide 203028_s_at 2.67 0.0219 
 CYBB Cytochrome b-245, β polypeptide 203923_s_at 4.08 0.0297 
 HCK Hemopoietic cell kinase 208018_s_at 3.66 0.0247 
Tethering and rolling of immune cells 
 APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at 2.67 0.0454 
(Table continues
Gene SymbolGene NameAffymetrix Probeset IDFold IncreaseP Value
Innate immune response 
  APOBEC3G Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G 204205_at 4.89 0.00616 
  IRF7 IFN regulatory factor 7 208436_s_at 3.90 0.0134 
  OLR1 Oxidized low-density lipoprotein (lectin-like) receptor 1 210004_at 4.36 0.0417 
  RNASE6 Ribonuclease, RNase A family, k6 213566_at 2.76 0.0332 
  TREM2 Triggering receptor expressed on myeloid cells 2 219725_at 2.92 0.0363 
  TRIM22 Tripartite motif-containing 22 213293_s_at 3.79 0.0264 
  TRIM34 Tripartite motif-containing 34 221044_s_at 4.06 0.00371 
  TYROBP TYRO protein tyrosine kinase binding protein 204122_at 2.30 0.0183 
 Inflammation 
  FRZB Frizzled-related protein 203697_at 6.89 0.00639 
  PLA2G4C Phospholipase A2, group IVC (cytosolic, calcium-independent) 209785_s_at 2.31 0.0464 
  PROS1 Protein S (α) 207808_s_at 2.58 0.0287 
 Complement 
  C1QC Complement component 1, q subcomponent, C chain 225353_s_at 2.46 0.00693 
  C3 Complement component 3 217767_at 4.43 0.0155 
  C3AR1 Complement component 3a receptor 1 209906_at 2.25 0.0301 
  ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 2.35 0.0445 
  MASP1 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/microglia 
  AIF1 Allograft inflammatory factor 1 215051_x_at 2.62 0.00730 
  B3GALT4 UDP-Gal:β GlcNAcβ 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 0.0296 
  CLEC7A C-type lectin domain family 7 member A 221698_s_at 2.47 0.0312 
  CSF1R CSF1 receptor 203104_at 2.52 0.0204 
  HLA-DMA MHC, class II, DMα 217478_s_at 3.30 0.0319 
  HLA-DMB MHC, class II, DMβ 203932_at 2.18 0.0268 
  HLA-DPB1 MHC, class II, DPβ 1 201137_s_at 2.36 0.0251 
  HLA-DRB5 MHC, class II, DRβ 5 208306_x_at 2.10 0.0449 
  IFIT3 IFN-induced protein with tetratricopeptide repeats 3 229450_at 3.85 0.0229 
  LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at 3.33 0.0389 
  NAPSB Napsin B aspartic peptidase pseudogene 228055_at 3.98 0.0478 
  SYK Spleen tyrosine kinase 226068_at 2.35 0.0307 
Adaptive immune response 
 T cell 
  CD37 Leukocyte Ag CD37 204192_at 2.52 0.0218 
  FYB FYN-binding protein (FYB-120/130) 211795_s_at 2.96 0.0272 
  GPR65 G protein-coupled receptor 65 214467_at 2.54 0.0184 
  HAVCR2 Hepatitis A virus cellular receptor 2 235458_at 3.50 0.0391 
  HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 2.62 0.0385 
  LAT2 Linker for activation of T cells family member 2 221581_s_at 2.41 0.00564 
  PTPRJ Protein tyrosine phosphatase receptor J 227396_at 2.53 0.0388 
  TRAC TCRα constant 209670_at 3.29 0.00156 
 B cell 
  BLNK B cell linker 207655_s_at 3.56 0.00115 
  GALNAC4S B cell RAG-associated protein 203066_at 3.06 0.0205 
  LPXN Leupaxin 216250_s_at 2.18 0.00254 
  MS4A6A CD20-like precursor 223280_x_at 2.82 0.0376 
 Antibody 
  FCGR1A Fc fragment of IgG, high-affinity Ia (CD64A) 216950_s_at 6.27 0.00324 
  FCGR1B Fc fragment of IgG, high-affinity Ib (CD64B) 214511_x_at 7.07 0.00551 
Cytokines, chemokines, and cytokine signaling 
 CNTNAP1 Contactin-associated protein 1 219400_at 2.77 0.0345 
 IFIT1 IFN-induced protein with tetratricopeptide repeats 1 203153_at 2.71 0.0391 
 RARRES3 Retinoic acid receptor responder (tazarotene induced) 3 204070_at 2.38 0.0276 
 STAT6 Signal transducer and activator of transcription 6, IL-4 induced 201331_s_at 4.72 0.00153 
 TNFSF10 TNF (ligand) superfamily, member 10 202688_at 2.68 0.0470 
 XAF1 Xiap-associated factor-1 228617_at 3.97 0.00365 
Oxidative burst 
 ALOX5 Arachidonate 5-lipoxygenase 204446_s_at 4.25 0.0382 
 CYBA Cytochrome b-245, α polypeptide 203028_s_at 2.67 0.0219 
 CYBB Cytochrome b-245, β polypeptide 203923_s_at 4.08 0.0297 
 HCK Hemopoietic cell kinase 208018_s_at 3.66 0.0247 
Tethering and rolling of immune cells 
 APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at 2.67 0.0454 
(Table continues
Table 3A.

(Continued)

Gene SymbolGene NameAffymetrix Probeset IDFold IncreaseP Value
 ARHGAP4 Rho GTPase-activating protein 4 204425_at 5.45 0.0150 
 CORO1A Coronin, actin binding protein, 1A 209083_at 2.72 0.0187 
 DOCK2 Dedicator of cytokinesis protein 2 213160_at 2.51 0.0274 
 SELPLG Selectin P ligand 209879_at 3.15 0.0180 
Hematopoietic cells 
 ADAM28 ADAM metallopeptidase domain 28 205997_at 3.58 0.0373 
 CD300A CD300A Ag 209933_s_at 3.12 0.0300 
 ENTPD1 Ectonucleoside triphosphate diphosphohydrolase 1 209473_at 2.07 0.0491 
 PTPN6 Protein tyrosine phosphatase, nonreceptor type 6 206687_s_at 2.45 0.0140 
Miscellaneous immune-related 
 FBLN1 Fibulin 1 202994_s_at 2.41 0.0375 
 GIMAP2 GTPase IMAP family member 2 232024_at 2.33 0.0252 
 LY75 Lymphocyte Ag 75 205668_at 4.28 0.0158 
 PLAC8 Placenta-specific 8 219014_at 2.07 0.0330 
 SIGLEC10 Sialic acid binding Ig-like lectin 10 1552807_a_at 3.55 0.00521 
Gene SymbolGene NameAffymetrix Probeset IDFold IncreaseP Value
 ARHGAP4 Rho GTPase-activating protein 4 204425_at 5.45 0.0150 
 CORO1A Coronin, actin binding protein, 1A 209083_at 2.72 0.0187 
 DOCK2 Dedicator of cytokinesis protein 2 213160_at 2.51 0.0274 
 SELPLG Selectin P ligand 209879_at 3.15 0.0180 
Hematopoietic cells 
 ADAM28 ADAM metallopeptidase domain 28 205997_at 3.58 0.0373 
 CD300A CD300A Ag 209933_s_at 3.12 0.0300 
 ENTPD1 Ectonucleoside triphosphate diphosphohydrolase 1 209473_at 2.07 0.0491 
 PTPN6 Protein tyrosine phosphatase, nonreceptor type 6 206687_s_at 2.45 0.0140 
Miscellaneous immune-related 
 FBLN1 Fibulin 1 202994_s_at 2.41 0.0375 
 GIMAP2 GTPase IMAP family member 2 232024_at 2.33 0.0252 
 LY75 Lymphocyte Ag 75 205668_at 4.28 0.0158 
 PLAC8 Placenta-specific 8 219014_at 2.07 0.0330 
 SIGLEC10 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. The level of overexpression is measured by fold increase and Student’s t test (p value).

Table V.

Immune-related genes positively correlated with TTPa

Gene SymbolGene NameAffymetrix Probeset IDRp Value
Innate immune response 
  CLEC1 Dendritic cell-associated lectin-1 1561899_at 0.64 0.0443 
  GATA6 GATA binding protein 6 210002_at 0.70 0.0233 
  RARA Retinoic acid receptor, α 203750_s_at 0.74 0.0152 
  SIGLEC1 Sialic acid binding Ig-like lectin 1, sialoadhesin 44673_at 0.79 0.00617 
 Viral response 
  MX2 Myxovirus (influenza virus) resistance 2 (mouse) 204994_at 0.84 0.00228 
  OAS2 2′-5′-oligoadenylate synthetase 2, 69/71 kDa 204972_at 0.65 0.0408 
  TMC8 Transmembrane channel-like 8 227353_at 0.75 0.0130 
 Inflammation 
  GPR84 G protein-coupled receptor 84 223767_at 0.69 0.0280 
  NFX1 Nuclear transcription factor, X-box binding 1 1553103_at 0.73 0.0171 
  PSD Pleckstrin and Sec7 domain containing 208102_s_at 0.77 0.00982 
  TPSAB1 Tryptase α 215382_x_at 0.66 0.0371 
 Complement 
  C2 Complement component 2 203052_at 0.87 0.00109 
  C3 Complement component 3 217767_at 0.67 0.0351 
  C3AR1 Complement component 3a receptor 1 209906_at 0.69 0.0263 
  C6 Complement component 6 210168_at 0.71 0.0211 
  C7 Complement component 7 202992_at 0.68 0.0312 
  CD53 CD53 Ag 203416_at 0.69 0.0277 
  CD59 CD59 Ag, complement regulatory protein 200985_s_at 0.82 0.00361 
  CR1 Complement component (3b/4b) receptor 1 217552_x_at 0.69 0.0264 
  ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 0.73 0.0165 
  TLR signaling    
  BTK Bruton agammaglobulinemia tyrosine kinase 205504_at 0.75 0.0124 
 Macrophage/microglia    
  AIF1 Allograft inflammatory factor 1 215051_x_at 0.63 0.0494 
  APOB48R Apolipoprotein B48 receptor 220023_at 0.86 0.00139 
  CD36 CD36 Ag (collagen type I receptor, thrombospondin receptor) 206488_s_at 0.76 0.0112 
  COLEC12 Collectin subfamily member 12 221019_s_at 0.77 0.00924 
  CSF2RB CSF2 receptor, β, low-affinity (granulocyte-macrophage) 205159_at 0.73 0.0162 
  FMNL1 Formin-like 1 204789_at 0.67 0.0356 
  HLA-DMB MHC, class II, DMβ 203932_at 0.66 0.0389 
  LILRA2 Leukocyte Ig-like receptor, subfamily a (with TM domain), member 2 207857_at 0.76 0.0109 
  LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at 0.74 0.0135 
  LILRB2 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 2 210146_x_at 0.68 0.0301 
  LILRB4 Leukocyte Ig-like receptor, subfamily B (with TM and ITIM domains), member 4 210152_at 0.82 0.00338 
  LY86 Lymphocyte Ag 86 205859_at 0.87 0.00122 
  PRDM1 PR domain containing 1, with ZNF domain 228964_at 0.64 0.0457 
  SIRPA Protein tyrosine phosphatase, nonreceptor type substrate 1 202897_at 0.66 0.0362 
  SLC15A1 Solute carrier family 15 (oligopeptide transporter), member 1 211349_at 0.75 0.0134 
Adaptive immune response 
 T cell 
  APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at 0.75 0.0127 
  FYB FYN binding protein (FYB-120/130) 211795_s_at 0.72 0.0201 
  GPR65 G protein-coupled receptor 65 214467_at 0.74 0.0136 
  HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 0.64 0.0472 
  LAT2 Linker for activation of T cells family member 2 221581_s_at 0.83 0.00299 
  TAGAP T cell activation GTPase-activating protein 229723_at 0.67 0.0325 
  TRDV2 TCRδ variable 2 210972_x_at 0.65 0.0428 
 CD4+ T cell 
  HP Haptoglobin 206697_s_at 0.70 0.0229 
  HPR Haptoglobin-related protein 208470_s_at 0.69 0.0260 
  SPDEF SAM pointed domain containing ETS transcription factor 214403_x_at 0.67 0.0357 
  MOG Myelin oligodendrocyte glycoprotein 205989_s_at 0.71 0.0222 
 B cell 
  CD48 CD48 Ag (B cell membrane protein) 204118_at 0.72 0.0190 
  IGHA2 Ig heavy constant α 2 (A2m marker) 214916_x_at 0.70 0.0232 
  IGHG3 Ig heavy constant γ 3 (G3m marker) 211868_x_at 0.65 0.0419 
  IGHM Ig heavy locus 209374_s_at 0.64 0.0455 
  IGJ Ig J polypeptide, linker protein for Igα and Igμ polypeptides 212592_at 0.65 0.0416 
  IGKC Igκ constant 211644_x_at 0.64 0.0460 
  IGKV1D-13 Igκ variable 1D-13 216207_x_at 0.85 0.00199 
(Table continues
Gene SymbolGene NameAffymetrix Probeset IDRp Value
Innate immune response 
  CLEC1 Dendritic cell-associated lectin-1 1561899_at 0.64 0.0443 
  GATA6 GATA binding protein 6 210002_at 0.70 0.0233 
  RARA Retinoic acid receptor, α 203750_s_at 0.74 0.0152 
  SIGLEC1 Sialic acid binding Ig-like lectin 1, sialoadhesin 44673_at 0.79 0.00617 
 Viral response 
  MX2 Myxovirus (influenza virus) resistance 2 (mouse) 204994_at 0.84 0.00228 
  OAS2 2′-5′-oligoadenylate synthetase 2, 69/71 kDa 204972_at 0.65 0.0408 
  TMC8 Transmembrane channel-like 8 227353_at 0.75 0.0130 
 Inflammation 
  GPR84 G protein-coupled receptor 84 223767_at 0.69 0.0280 
  NFX1 Nuclear transcription factor, X-box binding 1 1553103_at 0.73 0.0171 
  PSD Pleckstrin and Sec7 domain containing 208102_s_at 0.77 0.00982 
  TPSAB1 Tryptase α 215382_x_at 0.66 0.0371 
 Complement 
  C2 Complement component 2 203052_at 0.87 0.00109 
  C3 Complement component 3 217767_at 0.67 0.0351 
  C3AR1 Complement component 3a receptor 1 209906_at 0.69 0.0263 
  C6 Complement component 6 210168_at 0.71 0.0211 
  C7 Complement component 7 202992_at 0.68 0.0312 
  CD53 CD53 Ag 203416_at 0.69 0.0277 
  CD59 CD59 Ag, complement regulatory protein 200985_s_at 0.82 0.00361 
  CR1 Complement component (3b/4b) receptor 1 217552_x_at 0.69 0.0264 
  ITGB2 Integrin, β 2 (complement component 3 receptor 3 and 4 subunit) 202803_s_at 0.73 0.0165 
  TLR signaling    
  BTK Bruton agammaglobulinemia tyrosine kinase 205504_at 0.75 0.0124 
 Macrophage/microglia    
  AIF1 Allograft inflammatory factor 1 215051_x_at 0.63 0.0494 
  APOB48R Apolipoprotein B48 receptor 220023_at 0.86 0.00139 
  CD36 CD36 Ag (collagen type I receptor, thrombospondin receptor) 206488_s_at 0.76 0.0112 
  COLEC12 Collectin subfamily member 12 221019_s_at 0.77 0.00924 
  CSF2RB CSF2 receptor, β, low-affinity (granulocyte-macrophage) 205159_at 0.73 0.0162 
  FMNL1 Formin-like 1 204789_at 0.67 0.0356 
  HLA-DMB MHC, class II, DMβ 203932_at 0.66 0.0389 
  LILRA2 Leukocyte Ig-like receptor, subfamily a (with TM domain), member 2 207857_at 0.76 0.0109 
  LILRB1 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 1 229937_x_at 0.74 0.0135 
  LILRB2 Leukocyte Ig-like receptor, subfamily b (with TM and ITIM domains), member 2 210146_x_at 0.68 0.0301 
  LILRB4 Leukocyte Ig-like receptor, subfamily B (with TM and ITIM domains), member 4 210152_at 0.82 0.00338 
  LY86 Lymphocyte Ag 86 205859_at 0.87 0.00122 
  PRDM1 PR domain containing 1, with ZNF domain 228964_at 0.64 0.0457 
  SIRPA Protein tyrosine phosphatase, nonreceptor type substrate 1 202897_at 0.66 0.0362 
  SLC15A1 Solute carrier family 15 (oligopeptide transporter), member 1 211349_at 0.75 0.0134 
Adaptive immune response 
 T cell 
  APBB1IP Amyloid β (a4) precursor protein-binding, family b, member 1 interacting protein 230925_at 0.75 0.0127 
  FYB FYN binding protein (FYB-120/130) 211795_s_at 0.72 0.0201 
  GPR65 G protein-coupled receptor 65 214467_at 0.74 0.0136 
  HCLS1 Hematopoietic cell-specific Lyn substrate 1 202957_at 0.64 0.0472 
  LAT2 Linker for activation of T cells family member 2 221581_s_at 0.83 0.00299 
  TAGAP T cell activation GTPase-activating protein 229723_at 0.67 0.0325 
  TRDV2 TCRδ variable 2 210972_x_at 0.65 0.0428 
 CD4+ T cell 
  HP Haptoglobin 206697_s_at 0.70 0.0229 
  HPR Haptoglobin-related protein 208470_s_at 0.69 0.0260 
  SPDEF SAM pointed domain containing ETS transcription factor 214403_x_at 0.67 0.0357 
  MOG Myelin oligodendrocyte glycoprotein 205989_s_at 0.71 0.0222 
 B cell 
  CD48 CD48 Ag (B cell membrane protein) 204118_at 0.72 0.0190 
  IGHA2 Ig heavy constant α 2 (A2m marker) 214916_x_at 0.70 0.0232 
  IGHG3 Ig heavy constant γ 3 (G3m marker) 211868_x_at 0.65 0.0419 
  IGHM Ig heavy locus 209374_s_at 0.64 0.0455 
  IGJ Ig J polypeptide, linker protein for Igα and Igμ polypeptides 212592_at 0.65 0.0416 
  IGKC Igκ constant 211644_x_at 0.64 0.0460 
  IGKV1D-13 Igκ variable 1D-13 216207_x_at 0.85 0.00199 
(Table continues
Table 5A.

(Continued)

Gene SymbolGene NameAffymetrix Probeset IDRp Value
  IGLC2 Igλ constant 1 (Mcg marker) 216984_x_at 0.64 0.0447 
  IGLJ3 Igλ joining 3 211798_x_at 0.72 0.0189 
  IGLL3 Similar to omega protein 215946_x_at 0.79 0.00695 
  IGSF6 Ig superfamily, member 6 206420_at 0.66 0.0377 
  RALY RNA binding protein, autoantigenic (hnRNP-associated with lethal yellow homolog (mouse)) 224096_at 0.69 0.0275 
 Antibody 
  FCGR1A Fc fragment of IgG, high-affinity Ia 216950_s_at 0.64 0.0469 
  FCGR1B Fcγ receptor I B2 214511_x_at 0.64 0.0443 
  FCGR2C Fc fragment of IgG, low-affinity IIc, receptor for (CD32) 211395_x_at 0.67 0.0356 
Adhesion molecules 
 ITGAL Integrin, α L (Ag CD11A (p180), lymphocyte function-associated Ag 1; α polypeptide) 213475_s_at 0.69 0.0260 
 SELE Selectin E (endothelial adhesion molecule 1) 206211_at 0.65 0.0422 
Cytokines, chemokines, and cytokine signaling 
 CCL7 Chemokine (C-C motif) ligand 7 208075_s_at 0.65 0.0411 
 CCL11 Chemokine (C-C motif) ligand 11 210133_at 0.65 0.0413 
 CCR5 Chemokine (C-C motif) receptor 5 206991_s_at 0.71 0.0220 
 CX3CR1 Chemokine (C-X3-C motif) receptor 1 205898_at 0.70 0.0234 
 CYSLTR1 Cysteinyl leukotriene receptor 1 230866_at 0.78 0.00813 
 EDA2R Ectodysplasin A2 receptor 221399_at 0.65 0.0435 
 IL1B IL-1, β 39402_at 0.67 0.0322 
 IL4I1 IL-4-induced 1 214935_at 0.69 0.0259 
 IRF8 IFN regulatory factor 8 204057_at 0.82 0.00351 
 KLK7 Kallikrein 7 (chymotryptic, stratum corneum) 239381_at 0.67 0.0331 
 MLCK3 MLCK3 protein 1568925_at 0.64 0.0442 
 SAA2 Serum amyloid A2 208607_s_at 0.67 0.0352 
 TRADD TNFRSF1A-associated via death domain 205641_s_at 0.87 0.00110 
Oxidative burst 
 CYBASC3 Cytochrome b, ascorbate dependent 3 224735_at 0.67 0.0346 
 CYBB Cytochrome b-245, β polypeptide 203923_s_at 0.69 0.0266 
 GZMA 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 0.0491 
 TPSB2 Tryptase β 2 207134_x_at 0.82 0.00387 
Tethering and rolling of lymphocytes 
 ABCA1 ATP-binding cassette, subfamily A (ABC1), member 1 203505_at 0.73 0.0168 
 ARHGAP4 Rho GTPase-activating protein 4 204425_at 0.75 0.0118 
 ARHGAP9 Rho GTPase-activating protein 9 224451_x_at 0.67 0.0325 
 CORO1A Coronin, actin-binding protein, 1A 209083_at 0.70 0.0239 
 DOCK2 Dedicator of cytokinesis protein 2 213160_at 0.73 0.0156 
 FPRL2 Formyl peptide receptor-like 2 230422_at 0.66 0.0376 
 SELPLG Selectin P ligand 209879_at 0.82 0.00383 
 ST3GAL1 ST3 β-galactoside α-2,3-sialyltransferase 1 244074_at 0.72 0.0182 
Expressed in hematopoietic cells 
 BCL11B B cell CLL/lymphoma 11B (zinc finger protein) 222895_s_at 0.84 0.00234 
 CD109 CD109 Ag (GOV platelet alloantigens) 226545_at 0.73 0.0163 
 GNA15 Guanine nucleotide binding protein (G protein), α 15 205349_at 0.77 0.00868 
 GIMAP4 GTPase IMAP family member 4 219243_at 0.66 0.0364 
 GMFG Glia maturation factor γ 204220_at 0.64 0.0468 
 LCP1 Lymphocyte cytosolic protein 1 (L-plastin) 208885_at 0.80 0.0054 
 MYLC2PL Myosin light chain 2, lymphocyte-specific 221660_at 0.65 0.0405 
 NCKAP1L NCK-associated protein 1-like 209734_at 0.76 0.00996 
 NT5E 5′-nucleotidase, ecto (CD73) 203939_at 0.68 0.0311 
 PCSK5 Proprotein convertase subtilisin/kexin type 5 213652_at 0.70 0.0254 
 PSCD4 Pleckstrin homology, Sec7 and coiled-coil domains 4 219183_s_at 0.83 0.00294 
 SPTB Spectrin, β, erythrocytic (Includes spherocytosis, clinical type I) 214145_s_at 0.85 0.00203 
Miscellaneous immune-related 
 GVIN1 GTPase, very large IFN inducible 1 220577_at 0.65 0.0399 
 IFI27 Interferon, α -inducible protein 27 202411_at 0.72 0.0194 
 INPP5D Inositol polyphosphate-5-phosphatase, 145 kDa 203332_s_at 0.73 0.0166 
 LENG9 Leukocyte receptor cluster (LRC) member 9 1554589_at 0.66 0.0385 
 LGALS9 Lectin, galactoside-binding, soluble, 9 (galectin 9) 203236_s_at 0.68 0.0316 
 MALL Mal, T cell differentiation protein-like 209373_at 0.79 0.00642 
 SEMA3G Sema domain, Ig domain (Ig), short basic domain, secreted, (semaphorin) 3G 219689_at 0.65 0.0403 
 SERPINB9 Serpin peptidase inhibitor, clade B (ovalbumin), member 9 209723_at 0.68 0.0293 
 SLA SRC-like adapter 203761_at 0.75 0.0133 
Gene SymbolGene NameAffymetrix Probeset IDRp Value
  IGLC2 Igλ constant 1 (Mcg marker) 216984_x_at 0.64 0.0447 
  IGLJ3 Igλ joining 3 211798_x_at 0.72 0.0189 
  IGLL3 Similar to omega protein 215946_x_at 0.79 0.00695 
  IGSF6 Ig superfamily, member 6 206420_at 0.66 0.0377 
  RALY RNA binding protein, autoantigenic (hnRNP-associated with lethal yellow homolog (mouse)) 224096_at 0.69 0.0275 
 Antibody 
  FCGR1A Fc fragment of IgG, high-affinity Ia 216950_s_at 0.64 0.0469 
  FCGR1B Fcγ receptor I B2 214511_x_at 0.64 0.0443 
  FCGR2C Fc fragment of IgG, low-affinity IIc, receptor for (CD32) 211395_x_at 0.67 0.0356 
Adhesion molecules 
 ITGAL Integrin, α L (Ag CD11A (p180), lymphocyte function-associated Ag 1; α polypeptide) 213475_s_at 0.69 0.0260 
 SELE Selectin E (endothelial adhesion molecule 1) 206211_at 0.65 0.0422 
Cytokines, chemokines, and cytokine signaling 
 CCL7 Chemokine (C-C motif) ligand 7 208075_s_at 0.65 0.0411 
 CCL11 Chemokine (C-C motif) ligand 11 210133_at 0.65 0.0413 
 CCR5 Chemokine (C-C motif) receptor 5 206991_s_at 0.71 0.0220 
 CX3CR1 Chemokine (C-X3-C motif) receptor 1 205898_at 0.70 0.0234 
 CYSLTR1 Cysteinyl leukotriene receptor 1 230866_at 0.78 0.00813 
 EDA2R Ectodysplasin A2 receptor 221399_at 0.65 0.0435 
 IL1B IL-1, β 39402_at 0.67 0.0322 
 IL4I1 IL-4-induced 1 214935_at 0.69 0.0259 
 IRF8 IFN regulatory factor 8 204057_at 0.82 0.00351 
 KLK7 Kallikrein 7 (chymotryptic, stratum corneum) 239381_at 0.67 0.0331 
 MLCK3 MLCK3 protein 1568925_at 0.64 0.0442 
 SAA2 Serum amyloid A2 208607_s_at 0.67 0.0352 
 TRADD TNFRSF1A-associated via death domain 205641_s_at 0.87 0.00110 
Oxidative burst 
 CYBASC3 Cytochrome b, ascorbate dependent 3 224735_at 0.67 0.0346 
 CYBB Cytochrome b-245, β polypeptide 203923_s_at 0.69 0.0266 
 GZMA 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 0.0491 
 TPSB2 Tryptase β 2 207134_x_at 0.82 0.00387 
Tethering and rolling of lymphocytes 
 ABCA1 ATP-binding cassette, subfamily A (ABC1), member 1 203505_at 0.73 0.0168 
 ARHGAP4 Rho GTPase-activating protein 4 204425_at 0.75 0.0118 
 ARHGAP9 Rho GTPase-activating protein 9 224451_x_at 0.67 0.0325 
 CORO1A Coronin, actin-binding protein, 1A 209083_at 0.70 0.0239 
 DOCK2 Dedicator of cytokinesis protein 2 213160_at 0.73 0.0156 
 FPRL2 Formyl peptide receptor-like 2 230422_at 0.66 0.0376 
 SELPLG Selectin P ligand 209879_at 0.82 0.00383 
 ST3GAL1 ST3 β-galactoside α-2,3-sialyltransferase 1 244074_at 0.72 0.0182 
Expressed in hematopoietic cells 
 BCL11B B cell CLL/lymphoma 11B (zinc finger protein) 222895_s_at 0.84 0.00234 
 CD109 CD109 Ag (GOV platelet alloantigens) 226545_at 0.73 0.0163 
 GNA15 Guanine nucleotide binding protein (G protein), α 15 205349_at 0.77 0.00868 
 GIMAP4 GTPase IMAP family member 4 219243_at 0.66 0.0364 
 GMFG Glia maturation factor γ 204220_at 0.64 0.0468 
 LCP1 Lymphocyte cytosolic protein 1 (L-plastin) 208885_at 0.80 0.0054 
 MYLC2PL Myosin light chain 2, lymphocyte-specific 221660_at 0.65 0.0405 
 NCKAP1L NCK-associated protein 1-like 209734_at 0.76 0.00996 
 NT5E 5′-nucleotidase, ecto (CD73) 203939_at 0.68 0.0311 
 PCSK5 Proprotein convertase subtilisin/kexin type 5 213652_at 0.70 0.0254 
 PSCD4 Pleckstrin homology, Sec7 and coiled-coil domains 4 219183_s_at 0.83 0.00294 
 SPTB Spectrin, β, erythrocytic (Includes spherocytosis, clinical type I) 214145_s_at 0.85 0.00203 
Miscellaneous immune-related 
 GVIN1 GTPase, very large IFN inducible 1 220577_at 0.65 0.0399 
 IFI27 Interferon, α -inducible protein 27 202411_at 0.72 0.0194 
 INPP5D Inositol polyphosphate-5-phosphatase, 145 kDa 203332_s_at 0.73 0.0166 
 LENG9 Leukocyte receptor cluster (LRC) member 9 1554589_at 0.66 0.0385 
 LGALS9 Lectin, galactoside-binding, soluble, 9 (galectin 9) 203236_s_at 0.68 0.0316 
 MALL Mal, T cell differentiation protein-like 209373_at 0.79 0.00642 
 SEMA3G Sema domain, Ig domain (Ig), short basic domain, secreted, (semaphorin) 3G 219689_at 0.65 0.0403 
 SERPINB9 Serpin peptidase inhibitor, clade B (ovalbumin), member 9 209723_at 0.68 0.0293 
 SLA 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 level by which listed genes are correlated with TTP is measured as a continuous variable by two-sided Pearson correlation test (R) and estimated p value.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

This work was supported by the Tanner Seebaum Foundation.

3

Abbreviations used in this paper: EPN, ependymoma; AIF-1, allograft inhibitory factor-1; DAVID; Database for Annotation, Visualization, and Integrated Discovery; FDR, false discovery rate; FFPE, formalin-fixed paraffin-embedded; gcRMA, GeneChip robust multiarray average; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; IHC, immunohistochemistry; TIL, tumor-infiltrating lymphocyte; TTP, time to progression; GOTERM, Gene Ontology Project term.

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