Biology of Human Tumors

Tumor Purity as an Underlying Key Factor inGliomaChuanbao Zhang1,Wen Cheng2, Xiufang Ren3, Zheng Wang1, Xing Liu1,Guanzhang Li1, Sheng Han2, Tao Jiang1, and Anhua Wu2

Abstract

Purpose: Glioma tissues consist of not only glioma cells butalso glioma-associated nontumor cells, such as stromal cells andimmune cells. These nontumor cells dilute the purity of gliomacells and play important roles in glioma biology. Currently, theimplications of variation in glioma purity are not sufficientlyclarified.

Experimental Design: Here, tumor purity was inferred for2,249 gliomas and 29 normal brain tissues from 5 cohorts. Basedon the transcriptomic profiling method, we classified CGGA andTCGA-RNAseq cohorts as the RNAseq set for discovery. Casesfrom TCGA-microarray, REMBRANDT, and GSE16011 cohortswere grouped as a microarray set for validation. Tissues from theCGGA cohort were reviewed for histopathologic validation.

Results: We found that glioma purity was highly associatedwith major clinical and molecular features. Low purity caseswere more likely to be diagnosed as malignant entities and

independently correlated with reduced survival time. Integratingglioma purity into prognostic nomogram significantly improvedthe predictive validity. Moreover, most recognized prognosticindicators were no longer significantly effective under differentpurity conditions. These results highlighted the clinical impor-tance of glioma purity. Further analyses found distinct genomicpatterns associated with glioma purity. Low purity cases weredistinguished by enhanced immune phenotypes. Macrophages,microglia, and neutrophils were mutually associated andenriched in low purity gliomas, whereas only macrophages andneutrophils served as robust indicators for poor prognosis.

Conclusions: Glioma purity and relevant nontumor cellswithin microenvironment confer important clinical, genomic,and biological implications, which should be fully valuedfor precise classification and clinical prediction. Clin Cancer Res;23(20); 6279–91. �2017 AACR.

IntroductionGlioma is the most common and lethal type of tumor in the

central nervous system (CNS). Traditional histopathology classi-fies gliomas chiefly based on glioma cell features, includingmicroscopic similarities with putative origin cells and presumedlevels of differentiation. Emerging molecular advances integratethe genotype of glioma cells, including IDH mutation and the1p/19q codeletion, into the 2016 CNS World Health Organiza-tion (WHO) classification (1). Even though increasing studiesuncover mechanisms affecting the malignant properties ofglioma cells and identify reasonable therapeutic targets (2, 3),

the current clinical prediction and treatment outcome are stillnot satisfactory.

At present, we are deeply aware that glioma tissues containabundant glioma-associated nontumor cells within theirmicroenvironment. These nontumor cells play important rolesin glioma development and are represented by stromal andimmune cells (4, 5). Studies reveal that stromal cells are deeplyinvolved in glioma proliferation, invasion, and angiogenesis(6–9). Furthermore, increasing evidence indicates that infiltrat-ing immune cells have diverse roles in glioma biology (10, 11).Indeed, even nontumor cells dilute the purity of glioma cells.They mutually cooperate to maintain a subtle homeostasis forgliomagenesis, malignancy progression, and treatment resis-tance (12). However, current classification systems apply littleattention to the nontumor fractions within glioma tissue, andthere is limited knowledge on the characteristics of glioma cellsunder various purities.

A proper analytical model is necessary for exploring this issue.Recently, Yoshihara and colleagues have developed a universalalgorithm based on transcriptomic expression data to infertumor purity—quantifying tumor cell content in the tissue—andestimate infiltration of stromal and immune cells (13). Here,transcriptomic data of 2,249 gliomas and 29 normal brain tissueswere collected for analysis. Tumor purity was calculated usingthe ESTIMATE algorithm (13). The role of glioma purity wassystematically investigated in clinical, genomic, and biologicalconditions, which may provide novel insights into the gliomamicroenvironment and aid in the development of robust classi-fication and predictive systems.

1Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.2Department of Neurosurgery, The First Hospital of China Medical University,Shenyang, China. 3Department of Pathology, Shengjing Hospital of ChinaMedical University, Shenyang, China.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

C. Zhang and W. Cheng contributed equally to this article.

Corresponding Authors: Anhua Wu, The First Hospital of China Medical Uni-versity, Nanjing Street 155, Heping District, Shenyang, Liaoning 110001, China.Phone: 8602483283301; Fax: 8602483283301; E-mail: wuanhua@yahoo.com; orTao Jiang, BeijingNeurosurgical Institute, CapitalMedical University, No.6TiantanXili, Dongcheng District, Beijing 100050, China. Phone: 861067021832; Fax:861067096713; E-mail: taojiang1964@163.com

doi: 10.1158/1078-0432.CCR-16-2598

�2017 American Association for Cancer Research.

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Materials and MethodsPatients

This study collected 2,249 gliomas and 29 normal brain tissuesfrom four databases: CGGA (China), TCGA (the United States),REMBRANDT (the United States), and GSE16011 (the Nether-lands). There are two available cohorts of gliomas in the TCGAdatabase. Accordingly, five glioma cohorts were involved in thisstudy: CGGA, TCGA-RNAseq, TCGA-microarray, REMBRANDT,andGSE16011. Based on the transcriptomic profilingmethod,wegroupedCGGAandTCGA-RNAseq cohorts into anRNAseq set fordiscovery. Whereas, cases from TCGA-microarray, REMBRANDT,and GSE16011 cohorts were classified into a microarray set forvalidation.

The CGGA cohort consisted of 325 cases whose clinical andmolecular information was obtained from the CGGA database(http://www.cgga.org.cn). Each case enrolled was treated bymembers of the CGGA group. Tumor tissue samples werecollected at the time of surgery after informed consent. Neu-ropathologists established the diagnosis and ensured the qual-ity of the tissue for molecular testing (14). Overall survival (OS)was calculated from the date of diagnosis until death or the endof follow-up. The point of death is defined by death certifica-tion, which could be obtained from local hospitals and policestations. Another 1,953 glioma cases were included from theTCGA (https://gdc.cancer.gov/), REMBRANDT (https://caintegrator.nci.nih.gov/rembrandt/), and GSE16011 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc¼GSE16011) databases.Information on these patients is available from correspondingdata portal. Patient characteristics are described in SupplementaryTables S1 and S2.

Ethical statementCollection of tumor tissue and clinicopathologic information

was undertaken with informed consent. Study protocols wereapproved by the ethics committees of the participating institu-tions. The study was conducted in accordance with the Declara-tion of Helsinki.

RNAseq and quality controlThe libraries were sequenced on the Illumina HiSeq 2000

platform by the 101-bp pair-end sequencing strategy. Base calling(Illumina pipeline CASAVA v1.8.2) was used to convert originalimage data into sequence data. The sequence data were furtherprocessed to define improper reads according to standard qualitycontrol criteria. All reads that fit any of the following parameterswere removed:

(1) The reads that aligned to adaptors or primers with nomore than two mismatches.

(2) The reads withmore than 10%unknown bases (N bases).(3) The reads with more than 50% of low-quality bases

(quality value � 5) in one read.

Read mapping and expression analysis of RefSeq genesWe downloaded the Hg19 RefSeq (RNA sequences, GRCh37)

from the UCSC Genome Browser (http://genome.ucsc.edu). Thegene expression was estimated using the reads per kilobasetranscriptome permillion reads (RPKM)method (15). The RPKMmethod could remove the influence of different gene lengths andsequencing discrepancies from the expression calculation. There-fore, we could directly use the calculated gene expression data tocompare the differences in gene expression among samples.

Evaluation of IDH mutation status by DNA pyrosequencingGenomic DNA was extracted from frozen tissues using a

QIAamp DNA Mini kit (Qiagen) according to the manufac-turer's protocol. DNA concentration and quality were measuredusing a Nano-Drop ND-1000 spectrophotometer. Pyrosequen-cing of mutations in IDH1 or IDH2 was supported by GeneTech and performed on a Pyro-Mark Q96 ID System (Qiagen).PCR amplification was performed using the primers 50-GCTTGT GAG TGG ATG GGT AAA AC-30, 50-Biotin- TTG CCA ACATGA CTT ACT TGA TC-30 for IDH1 and 50-ATC CTG GGG GGGACT GTC TT-30, 50-Biotin-CTC TCC ACC CTG GCC TAC CT-30

for IDH2. Pyrosequencing was performed using the primers 50-TGG ATG GGT AAA ACC T-30 for IDH1 and 50-AGC CCA TCACCA TTG-30 for IDH2 (16).

Evaluation of the TERT promoter mutation by Sangersequencing

Mutational hotspots in the TERT core promoter were obtained[nucleotide numbers 1 295 228 (C228T) and 1 295 250 (C250T)]from the human reference sequence (GrCh37 February 2009;http://genome.ucsc.edu/). Nested PCR was used to amplify themutational hotspots. The first PCR primer sequences were 50-GTCCTG CCC CTT CAC CTT-30 (forward) and 50-GCA CCT CGCGGTAGT GG-30 (reverse), which yielded a 273-bp product. Furthernested PCR was conducted using primers (Primer set 2) 50-CCGTCCTGCCCCTTCACC-30 (forward) and50-GGGCCGCGGAAAGGA AG-30 (reverse), which yielded a 128-bp product. We puri-fied products from the second PCR using Illustra ExoProStar (GEHealthcare). The purified products were subjected to directsequencing on an ABI 3100 PRISM DNA sequencer with a Big-Dye Terminator cycle sequencing kit (Applied Biosystems). Thequality of all PCR products was checked before sequencing usingelectrophoresis on 2% agarose gels (17).

Evaluation of MGMT promoter methylation by DNApyrosequencing

Bisulfite modification of DNA was performed using an EpiTectkit (Qiagen). The MGMT promoter region was amplified using

Translational Relevance

Glioma tissues are always intermixed with a considerablefraction of nontumor cells. Whereas, current classification andprediction systems apply little attention to the purity of gliomacells. Here, we found that glioma purity correlatedwith severalclinical features. Low purity cases were more likely to bediagnosed as malignant entities and lead to a poor prognosis.Different glioma purity also indicated diverse genomic aberra-tions and biological phenotypes. Low purity gliomas werecharacterized by intensive local immune phenotypes andenriched with macrophages, neutrophils, and microglia.Whereas, only macrophages and neutrophils served as robustindicators for poor prognosis. This research offers a compre-hensive evaluation of the importance of glioma purity inmultidimensional conditions and identifies the resultingenriched immune cells. These findings may facilitate the fullunderstanding of microenvironmental biology and offer nov-el insights into glioma classification, prognostic prediction,and effective therapeutic strategy.

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primers as follows: 50-GTT TYG GAT ATG TTG GGA TA-30 and50-biotin-ACC CAA ACA CTC ACC AAA TC-30. Gene Techperformed pyrosequencing analysis. We averaged the methyl-ation values across seven CpG loci within theMGMT promoter.We defined methylation as samples with an average methyla-tion value of >10%.

Hematoxylin and eosin staining and immunohistochemicalstaining

We reviewed 12 glioma tissues from the CGGA cohort to inferglioma purity through the visual evaluation of hematoxylin andeosin–stained slides. Results of morphologic purity were inde-pendently confirmed by two neuropathologists.

Paraffin-embedded tissues of 43 cases from the CGGA cohortwere collected. Because there is no specific antibody to distinguishmacrophage and microglia in the CNS, we selected antibodyagainst Iba-1 (Abcam; ab5076, 1:1,000) to detect macrophage/microglia (5). Anti-neutrophil elastase (Abcam; ab68672,1:1,000) was used to stain neutrophil. Immunohistochemistryof paraffin sections was performed as previously described (18).Briefly, the sections were incubated with primary antibody over-night at 4�C, and then incubated with respective secondaryantibody (ZSGB, 1:200) at room temperature for 1 hour. Afterwashing with PBS buffer, the sections were stained with DAB for5minutes, rinsed in water, and counterstained with hematoxylin.Quantitative evaluation was performed by examining each sec-tion using at least five different high-power fields (X40 objectiveand X10 eyepiece) with the most abundant stained cells. Stainedcells were manually counted 3 times for each photograph inde-pendently by two investigators. The number of stained cells wasdetermined by the average method.

Bioinformatic analysisStromal and immune scores were calculated using the ESTI-

MATE R package (https://sourceforge.net/projects/estimateproject/), and glioma purity was estimated according to theformula described in Yoshihara and colleagues (13). Basedon the expression data, cases were classified into relevantsubtypes using the TCGA and EM/PM (EM is the abbreviationfor EGFR module; PM is the abbreviation for PDGFRA mod-ule) classification schemes (19, 20). We used GISTIC2.0 toassess copy-number alterations (CNA) associated with gliomapurity. Locus with GISTIC value more than 1 or less than �1was defined as an amplification or deletion, respectively.Knowledge on gene interactions and pathways was obtainedfrom the KEGG website (http://www.kegg.jp/; ref. 21). Theprincipal components analysis (PCA) package of R was used toprofile transcriptomic patterns attributing to glioma purity.Significance analysis of microarrays (SAM) was used to identifydifferential genes based on the threshold of fold change morethan 3 and FDR less than 0.05. Spearman correlation analysiswas used to evaluate the association between gene expressionand glioma purity. Gene sets were submitted to the DAVIDwebsite (http://david.abcc.ncifcrf.gov/home.jsp) to performgene ontology (GO) analysis and to retrieve relevant biologicalimplications (22). The biological phenotype was further ver-ified using gene set enrichment analysis (GSEA; ref. 23). Asso-ciations between immune cells and glioma purity were ana-lyzed using the gene set variation analysis (GSVA) package of R(24). The gene list of immune cells was summarized byGabriela and colleagues (25) and Oleg and colleagues (26).

Statistical analysisAssociations between continuous variables were tested using

Spearman correlation analysis. Differences in variables betweengroups were evaluated by the Student t test, one-way ANOVA,or Yate's continuity corrected c2 tests. The Kaplan–Meier sur-vival curve was depicted to estimate survival distributions. Thelog-rank test was used to assess statistical significance betweengroups. Univariate cox and further backward stepwise multi-variate cox regression analyses were performed to identifyindependent prognostic factors. A nomogram was formulatedbased on the results of multivariate cox regression analysis(27). The predictive validity was assessed by comparing nomo-gram-predicted with observed survival probability. Bootstrapswith 1,000 resamples were applied to these activities. Theperformance of the nomogram was evaluated using the con-cordance index (C-index). A larger C-index indicated a moreaccurate prognostic prediction. Patients with missing informa-tion were excluded from corresponding analysis. All statisticalanalyses were conducted using R 3.3.0, SPSS software, andGraphPad Prism 6.0 software. A two-sided P value less than0.05 was regarded as significant.

ResultsAssociations of glioma purity with clinical and molecularcharacteristics

A total of 2,249 gliomas and 29 normal brain tissues wereincluded for analysis (Supplementary Tables S1 and S2). Fourglioma cell lines (U87, U251, H4, and LN229 cell lines) wereanalyzed as references. We assessed glioma purity, as well asstromal and immune scores using the ESTIMATE algorithm.Morphology evaluation of glioma purity was conducted for 12cases from the CGGA cohort (Supplementary Fig. S1A). Con-sistent with previous reports, glioma purity based on theESTIMATE algorithm exhibited moderate correlation with mor-phology evaluation (Supplementary Fig. S1B and S1C; refs. 13,28, 29). A high degree of purity was observed in glioma celllines (median purity: 99.44%; Supplementary Table S3), ver-ifying the validity of the ESTIMATE algorithm. There is a widerange of purity among the cases, 696 (30.95%) of the cases werehighly purified (>90%), whereas 213 (9.47%) had poor purity(<60%; Supplementary Fig. S2B). In accordance with a previousreport (13), we found a highly positive correlation betweenstromal and immune scores in glioma (R ¼ 0.9021; Supple-mentary Fig. S2A).

Gliomas from the RNAseq and microarray sets were arrangedin order of increasing purity (Fig. 1). The purity distributionwas evaluated among groups and in association with otherfactors. We found a negative correlation between glioma purityand age at diagnosis (Fig. 1). Low purity gliomas were morelikely to belong to mesenchymal and EM subtype. On thecontrary, high purity gliomas were enriched in proneural andPM subtype (Supplementary Fig. S3A and S3E). With regard togenomic alterations, the IDH mutation and 1p/19q codeletionindicated higher glioma purity (Fig. 1; Supplementary Fig. S3).Even the TERT promoter mutation conferred lower purity inLGGs, and its influences were abolished in glioblastoma (GBM;Supplementary Fig. S3D). In addition, we also examined thecorrelation between purity and other parameters in stratifiedhistopathology and molecular classification (SupplementaryTable S4).

Tumor Purity in Glioma

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Aggressive gliomas are characterized by higher stromal andimmune scores, but lower purity

According to the WHO grading system and histogenetic fea-tures, gliomas were classified into corresponding entities. Stromaland immune scores significantly increased alongwithmalignancyprogression, whereas glioma purity decreased in higher grades(Fig. 2; Supplementary Table S3). With respect to histopathologicclassification, oligodendroglioma had the lowest level of stromaland immune scores, but the highest purity. Contrarily, GBM wasaccompanied with high stromal and immune score, but lowpurity (Fig. 2; Supplementary Table S3).

Recent studies have incorporated important genetic alterations,such as IDH mutation, 1p/19q codeletion, and TERT promotermutation, into glioma classification (30–32). Here, we dividedgliomas into fivemolecular entities including lower-grade glioma(LGG)-Oligo (IDH-mutant LGGwithTERTpromotermutationor1p/19q codeletion), LGG-Astro (IDH-mutant LGG without TERT

promotermutation or 1p/19q codeletion), early-GBM (LGGwithwild-type IDH), GBM-IDHmut (GBM with mutant IDH), andGBM-IDHwt (GBM with wild-type IDH). We found survival timewas significantly reduced in an order of LGG-Oligo, LGG-Astro,Early-GBM, GBM-IDHmut, and GBM-IDHwt (SupplementaryFig. S4, P< 0.0001). In accordancewith survival variation, stromaland immune scores were highest in GBM-IDHwt but lowest inLGG-Oligo (Fig. 2A–D; Supplementary Table S3). Glioma puritywas highest in LGG-Oligo and lowest in GBM-IDHwt (Fig. 2E andF; Supplementary Table S3).

In addition, in RNAseq set, recurrent glioma or secondary GBMshowed similar tumor purity as primary glioma (SupplementaryFig. S5A).Whenwe focused onGBM, limited differences in tumorpurity were observed among primary, recurrent, and secondaryGBM (Supplementary Fig. S5B and S5C). These results indicatedthat recurrent glioma and secondary GBM had similar purity asprimary cases.

Figure 1.

Landscape of clinical and molecular characteristics in association with glioma purity. RNAseq (top) and microarray (bottom) sets were arranged in anincreasing order of glioma purity. The relationship between glioma purity and patients' characteristics was evaluated (a, The distribution of glioma puritywas assessed using the Student t test between two groups. b, The association between glioma purity and continuous variables was assessed usingSpearman correlation tests. c, The distribution of glioma purity between several groups was assessed using one-way ANOVA).

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Low glioma purity indicates unfavorable prognosisWe used the dichotomization on median value to separate

cases for depicting the survival curves. Parallel analyses were

conducted in both RNAseq and microarray sets. Cases with lowpurity generally had shorterOS than thosewith higher purity (Fig.3A and B). Stratified analyses revealed that low purity conferred

Figure 2.

Distinct distribution of stromal score,immune score, and tumor purityamong glioma subtypes. Stromalscore (A and B), immune score (C andD), and glioma purity (E and F)exhibited various distribution patternsamong WHO grades, histopathologicclassifications, and molecular entities.

Tumor Purity in Glioma

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reducedOS inmostWHOgrades except grade II in the RNAseq set(Fig. 3C–H). Survival curves were also depicted in differentsubtypes (Supplementary Fig. S6), recurrent gliomas, and sec-ondary GBMs (Supplementary Fig. S7). Even though some of thetests failed to achieve statistical significance, low purity stillimplied a trend suggestive of inferior outcome in most subtypesand recurrent gliomas (Supplementary Figs. S6 and S7). In addi-tion, similar analyses were applied for stromal (SupplementaryFigs. S8 and S9) and immune scores (Supplementary Figs. S10and S11), indicating that they were unfavorable indicatorsfor prognosis.

We next conducted cox regression analyses to assess theprognostic independence of glioma purity among other factors.Glioma purity independently correlated with a better outcomein both RNAseq and microarray sets (Table 1). Among all cases,multivariate cox regression also confirmed that glioma purityalong with age, Karnofsky performance score (KPS), radiation,chemotherapy, and 1p/19q status were independent prognosticindicators in glioma (Table 1). Afterward, these independentfactors were integrated to develop a prognostic nomogram(Supplementary Fig. S12A). The calibration plot for the prob-ability of survival at 1, 2, and 3 years showed an optimalagreement between the prediction by nomogram and actualobservation (Supplementary Fig. S12B). We compared thepredictive power for OS between nomograms and its consti-tuting factors. The C-index for OS prediction was 0.736 (95%confidence interval, 0.656–0.816) by the nomogram, whichwas significantly higher than the C-indices of the constitutingfactors (Supplementary Fig. S12C). These results indicated thatintegrating glioma purity into a predictive model significantlyimproved the predictive validity.

Validity of recognized prognostic indicators in gliomas withdivergent purity

Subsequently, the prognostic significance of known indicatorswas evaluated in gliomas with various purities. We sought togroup all cases based on the median purity, but the sample sizebecame limited owing to missing information and differentproportion of cases among cohorts. Accordingly, we conductedthe analyses in cases from the TCGA and CGGA databases,respectively. Cases were divided into low and high purity groupsbased on their median purity. Among traditional clinical andmolecular factors, only KPS was verified as an independentprognostic factor for low purity gliomas in both TCGA and CGGAdatabases (Supplementary Table S5). In the high purity group,only histopathologic classification was identified with statisticalsignificance. However, the improper HR, which was discrepantfrom the histopathology reality, undermined its validity (Sup-plementary Table S6). Accordingly, no factor could be deemed asan independent prognostic indicator for high purity gliomas inthe present study. These results indicate the necessity of consid-ering glioma purity for better prognostication.

Glioma purity associates with distinct patterns of genomicalterations

To uncover the molecular mechanisms influencing tumor cellpercentage within glioma, TCGA cases with available mutationandCNA informationwere analyzed for that purpose.We found anegative correlation between somatic mutations and gliomapurity (R ¼ �0.2434, P < 0.0001; Supplementary Fig. S13A).Based on the increasing purity, cases were respectively dividedinto two, three, or four subgroups. Parallel analyses were con-ducted in each kind of grouping to enhance the credibility of our

Figure 3.

Clinical outcome of different purity conditions. A and B, Different glioma purity conferred significantly different prognosis in all cases. C–H, The prognosticvalue of glioma purity in different grades.

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Table

1.Univariatean

dmultivariateco

xan

alyses

inglio

mas RNAseqset

Microarrayset

Allcases

Univa

riate

Multiva

riate

Univa

riate

Multiva

riate

Univa

riate

Multiva

riate

Factor

Num

ber

of

samples

Pva

lue

HR

Pva

lue

HR

Num

ber

of

samples

Pva

lue

HR

Pva

lue

HR

Num

ber

of

samples

Pva

lue

HR

Pva

lue

HR

Age Increa

sing

years

938

<0.0001

1.057

877

9<0

.0001

1.039

0<0

.0001

1.025

91,7

17<0

.0001

1.052

3<0

.0001

1.030

6Grade

Maligna

ncyprogress

938

<0.0001

4.16

530.0069

3.9990

1,024

<0.0001

2.1057

1,962

<0.0001

2.932

7Histology

938

<0.0001

0.2446

1,124

<0.0001

0.0015

<0.0001

Glio

blastoma

303

Ref.

Ref.

Ref.

Ref.

858

Ref.

Ref.

Ref.

Ref.

1,161

Ref.

Ref.

Astrocytoma

236

<0.0001

0.2040

0.0854

3.474

8132

<0.0001

0.330

50.0664

0.420

436

8<0

.0001

0.2698

Olig

oastrocytoma

187

<0.0001

0.1138

0.553

51.6

755

35<0

.0001

0.4140

0.9317

1.0519

222

<0.0001

0.18

52Olig

oden

droglio

ma

212

<0.0001

0.074

30.9674

<0.0001

100

<0.0001

0.3162

0.0003

0.333

7312

<0.0001

0.17

37KPS Increa

sing

score

454

<0.0001

0.9645

0.0002

0.9680

644

<0.0001

0.977

4<0

.0001

0.979

81,0

98

<0.0001

0.9711

0.0441

0.9876

Rad

iation

Yes

vs.N

o28

7<0

.0001

0.429

00.0056

0.4906

710

0.0002

0.436

3<0

.0001

0.2215

997

0.034

40.7627

0.0001

0.239

5Che

motherap

yYes

vs.N

o27

90.079

51.3

777

712

0.0001

0.726

6<0

.0001

0.436

3991

0.036

70.853

9<0

.0001

0.4184

IDHstatus

Mutationvs.W

ild-typ

e931

<0.0001

0.15

53616

<0.0001

0.452

81,5

47

0.036

70.853

91p/19qstatus

Codel

vs.N

oncodel

623

<0.0001

0.2451

651

<0.0001

0.2845

0.0148

0.5154

1,274

<0.0001

0.257

70.026

10.19

61

TERTpromoterstatus

Mutationvs.W

ild-typ

e56

10.4995

1.1029

370.0511

4.2860

598

0.052

81.2

987

MGMTpromoterstatus

Methy

latedvs.U

nmethy

lated

839

<0.0001

0.3679

0.0464

0.5961

476

0.5990

1.0887

1,315

<0.0001

0.3189

Glio

mapurity

Increa

sing

percentag

e938

<0.0001

0.0072

0.0052

0.0866

1,124

<0.0001

0.13

170.0002

0.18

89

2,063

<0.0001

0.035

40.0004

0.15

96

Tumor Purity in Glioma

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findings. Here, we showed that the quartering condition wasrepresentative. First, we compared the frequency of mutationsbetween cases with low and high purity. More somatic muta-tions were detected in cases with low purity (1st vs. 2nd half,9,180 vs. 7,441 mutations; 1st vs. 3rd tertile, 6,532 vs. 4,790mutations; 1st vs. 4th quarter, 4,872 vs. 3,518 mutations).Frequent mutations in IDH1, CIC, FUBP1, NOTCH1, as wellas the 1p/19q codeletion were significantly enriched in caseswith a higher purity (Fig. 4A; Supplementary Fig. S14A andS14B; Supplementary Table S7). On the other hand, mutationsin PTEN, EGFR, and NF1 occurred more frequently in gliomaswith a lower purity (Fig. 4A; Supplementary Fig. S14A andS14B; Supplementary Table S7). We also observed that asignificantly different frequency of mutations in TTN, PCLO,MUC17, ZBTB20, NIPBL, FRAS1, and SPTA1 was attributed tovarious purity conditions, whereas existing studies have barelyexplored their roles in glioma.

Genomic mutations were further explored on the basis ofKEGG pathways. Consistent with above findings, low puritycases carried more mutations in the 32 representative pathways(Supplementary Table S8). Calcium, FoxO, and cAMP path-ways were the most significant differential pathways which hadhigher mutation rates among cases with low purity (Fig. 4B;Supplementary Fig. S14C and S14D; Supplementary Table S8).Meanwhile, a lower purity also indicated more mutations ingenes involving focal adhesion, cell interaction, and recognizedpathways in glioma, such as PI3K-AKT, Ras, and mTOR path-ways (Fig. 4B; Supplementary Fig. S14C and S14D; Supple-mentary Table S8).

Subsequently, CNA data were investigated showing distinctchromosomal alteration patterns between gliomas with low andhigh purity. A negative correlation between CNAs and purity wasfound (R ¼ �0.2939, P < 0.0001; Supplementary Fig. S13B).Consistently, more CNAs were detected in low purity gliomas

Figure 4.

Distinct genomic profiles associated with glioma purity. A, Differential somatic mutations were detected by comparing gliomas with low and high purity.B, Associations of mutations in pathways with glioma purity. The horizontal axis represents the significance of association, calculated using the c2 test.The dashed line corresponds to a P value of 0.05. C, The overall CNA profile in order of increasing glioma purity. D, A distinct CNA profile could be observedbetween gliomas with a low and high purity. The horizontal axis represents the frequency of chromosomal deletion (blue) and amplification (red).

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(1st vs. 2nd half, 81,515 vs. 52,961 CNAs; 1st vs. 3rd tertile,54,944 vs. 31,362 CNAs; 1st vs. 4th quarter, 38,258 vs. 18,875CNAs). The incidence of the 1p/19q codeletion, which is agenomic hallmark in oligodendroglioma, increased along withthe increasing glioma cell percentage (Fig. 4C). Whereas, Chr 7amplification paired with Chr 10 loss, a representative event inGBM (19), were enriched in low purity cases (Fig. 4C). A total of808 genes were identified with a different frequency of amplifi-cation attributing to various glioma purities. Meanwhile, another44 genes were differentially deleted in different purity situations(Supplementary Dataset S1). All these differential alterationsoccurred more frequently in the lower purity subgroups. Amongcaseswith lowpurity, the frequently deleted genomic regionswere9p21.3 encompassing the CDKN2A/CDKN2B locus (47.25% inthe 1st quarter;P¼2.2E–17) aswell as 10q23.3 encompassing thePTEN locus (19.51% in the 1st quarter; P ¼ 0.0005; Fig. 4D;Supplementary Fig. S14E and S14F). On the other hand, themostcommonly amplified region associated with low purity was7p11.2 encompassing the EGFR (45.05% in the 1st quarter, P¼ 9.1E–24; Fig. 4D; Supplementary Fig. S14E and S14F). CDK4(12q14.1; 12.64% in the 1st quarter; P ¼ 0.0043) and PDGFRA(4q12; 13.19% in the 1st quarter; P¼0.0005) amplificationswerealso significantly frequent in low purity gliomas (Fig. 4D; Sup-plementary Fig. S14E and S14F).

Low purity glioma exhibits an intensive immune phenotypeGene expression data were analyzed to explore the biological

phenotypes associated with glioma purity. The TCGA-RNAseqcohort was selected as discovery set, and other cohorts wereused for validation. We carried out PCA to explore the tran-scriptomic features associated with glioma purity. Here, weobserved a tight association between whole transcriptomeexpression profile and glioma purity (Fig. 5A; SupplementaryFig. S15A–S15D), implying distinct general biological pheno-types attributing to various tumor cell percentage. Afterward,we sought to find out the biological features which are signif-icantly enhanced in gliomas with low purity. We conductedspearman correlation analysis to identify genes associated withglioma purity. Genes whose expression were most negativelycorrelated with purity (R < �0.8) were submitted for GOanalysis, showing that most of the top enriched biologicalimplications were immune-relevant (Fig. 5B). Meanwhile, wedivided cases into high and low purity groups based on themedian purity. SAM was used to identify differential genesbetween cases with high and low purity. Genes that differen-tially upregulated in low purity cases were significantlyenriched with terms related to immune response (Fig. 5C).Further, GSEA confirmed that cases with various glioma purityexhibited distinct immune status and low purity ones suffered astrengthened immune phenotype compared with high purityones (Fig. 5D).

Macrophage and neutrophil are enriched in low purity gliomaand indicate poor prognosis

As lower purity confers intensive local immune status, wesought to investigate which type of immune cell forms the non-tumor fraction and contributes to the enhanced immuneresponse. We employed GSVA for estimating signatures of mul-tiple immune cells and correlated the signatures with gliomapurity (Fig. 5E; Supplementary Fig. S15E–S15H). According tothe association with glioma purity, immune cells could be clus-

tered into two subgroups. A threshold of absolute correlationcoefficient more than 0.3 was used for filtration across cohorts(Supplementary Table S9).Macrophages, neutrophils, andmicro-glia exhibited consistent negative correlation (R < �0.3) withglioma purity.Whereas, centralmemory T cells, follicular helper Tcells, CD8 T cells, natural killer (NK) cells, eosinophils, and Thelper 1 cells were consistently positively correlated (R > 0.3)withgliomapurity. Amutual relationship clustered these immune cellsinto two subgroups (Fig. 5F; Supplementary Fig. S15I–S15L),which were in accordance with their correlation to glioma purity.We further collected 43 tissues from the CGGA cohort. IHCanalysis confirmed that numbers of macrophage/microglia andneutrophil were enriched in low purity cases (Fig. 5G and H) anddiversely correlated with glioma purity (Supplementary Fig. S16).Therefore, macrophages, microglia, and neutrophils could be acluster of immune cells that contributed to the nontumor fractionwithin low purity gliomas. Considering macrophages, neutro-phils, and microglia were highly enriched in gliomas with fewertumor cells, we attempted to investigate their prognostic implica-tions. Survival analyses showed macrophages and neutrophils,but not microglia, were significantly correlated with gliomaprognosis (Fig. 5I–K). Similar analyses in other cohorts confirmedthat enrichment of macrophages and neutrophils indicatedreduced survival time (Supplementary Fig. S17).

DiscussionTumor tissues have a diverse mixture of tumor and nontumor

cells within their microenvironments. Tumor purity is routinelydetermined by pathologists through visual evaluation, whichcould be affected by the sensitivity of histopathology, interob-server bias, and variability in accuracy (33). Other than routinepathology-based estimation, genomic advances introduce manycomputational methods to determine tumor purity based onmultiform genomic data, such as somatic mutation (34), somaticCNA (29), and DNAmethylation (35), which elicit objective andhighly concordant results (28). Owing to the compatibility ofESTIMATE algorithm in both microarray- and RNAseq-basedtranscriptome profiles, we reviewed 2,249 glioma cases andsystematically investigated the role of glioma purity in presentstudy. Glioma purity was highly associated with major clinicaland genomic features, strengthening that glioma purity is anintrinsic characteristic of the tumor cell in developing a suitablemicroenvironment (28).

We found cases with low purity glioma cells exhibited fewfavorable implications but were more likely to be ascribed inmalignant entities and have reduced survival time. On the oneside, glioma cells with limited proliferative and invasive proper-ties tend to grow slowly and form a solid bulk with less nontumorcell infiltration. On the other side, tumor cells have been reportedto be capable of dominating amicroenvironment (11), giving riseto the hypothesis that malignant gliomas recruit abundant sur-rounding cells and subjugate them to compose aprotective shield.Therefore, low tumor purity and correlated cellular heterogeneityare responsible for glioma's aggressive phenotype and poorprognosis. Purity of glioma cells may add a new dimension inestimatingmalignant identities and explainwhymost therapeuticstrategies aimed purely against glioma cells do not have an idealoutcome.

Glioma purity was identified as a robust indicator for progno-sis, whereas most prognostic indicators were no longer

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Figure 5.

Low purity glioma was characterized by an enhanced local immune response. A, There is a tight correlation between glioma purity and transcriptomicexpression profiles. B, Immune relevant processes were the main biological implication originating from genes that were negatively correlated withglioma purity. C, Genes differentially upregulated in the low purity group were also enriched in immune relevant processes. D, GSEA indicated a significantlyenhanced immune phenotype in cases with low purity. E, Associations between glioma purity and immune cell enrichment. F, Two clusters of immunecells could be summarized according to their mutual relationship. G and H, Immunohistochemical analysis verified that macrophages, microglia, andneutrophils were highly enriched in low purity cases. I–K, Survival curves indicated that the enrichment of macrophages and neutrophils, but not microglia,conferred poor prognosis.

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significantly effective even among gliomas with high purity. Tightassociations between glioma purity and genomic indicatorsimplied that the prognostic and predictive value of genomicindicators may be partially derived from their roles inmonitoringa balance between tumor and nontumor cells (36). These resultshighlight the pivotal role of nontumor cells in determiningprognosis and question the use of traditional indicators forclinical prediction, especially in individual cases. Here, we havedeveloped a prognostic nomogram comprehensively taking gli-oma purity and other robust indicators into consideration, whichshowed superior validity in predicting survival time. Therefore,integrating glioma purity into a prognostic system would behelpful for precise prediction.

Stromal and immune cells constitute the major nontumorfraction within a gliomamicroenvironment. Moreover, there wasa tight association between stromal and immune scores. Distinctlocal immune status was the primary differential phenotype thatresulted from various glioma purities, which was significantlyenhanced in cases with low purity. This verifies our previousreport, which found that an intensive local immune response isunfavorable for glioma prognosis (37). Importantly, not allimmune cells were enriched in tissues with low glioma purity,summarizing two subgroups of immune cells based on theirrelationship with glioma purity. This highlights the inability ofantitumor immune cells in infiltrating into the established pro-tective shield around the glioma cell. These antitumor cells couldbe represented by CD8 T cells and NK cells, whose enrichmentcould significantly prolong patients' survival (38, 39).

We should also note the ability of glioma cells in selectivelyrecruiting immune cells to establish a proper microenviron-ment. We found that macrophages, microglia, and neutrophilswere enriched in low purity cases, which could be verified usinghistologic methods. These cells were of myeloid lineage andassociated with cellular immunosuppression in glioma (40). Inparticular, glioma-associated macrophages and microglia havebeen reported to form as many as 30% to 50% of the cells ingliomas (5). Different polarization statuses of macrophagesand microglia have been defined based on stimulation in vitro,including the classical M1 phenotype and alternative M2 phe-notype. Their enrichment generally contributes to the gliomagrowth, invasion, and increasing histopathologic grade (41).Previous studies deemed macrophage and microglia as unfa-vorable indicators for prognosis (41–43). Whereas, only mac-rophage was identified with prognostic significance in thepresent study. Unlike previous results which were mainlyderived from analysis on a single marker, we evaluated theenrichment of macrophage and microglia by conducting aGSVA algorithm that was based on the expression profile ofa characteristic gene set and generated a signature from a morecomprehensive perspective. Although there are abundant stud-ies that focus on the role of macrophages and microglia, furtheranalysis is still needed to confirm our results and explore thedifferent roles of polarization in glioma.

However, low purity glioma contains abundant neutrophilinfiltration, which is positively associated with grade progression,shorter survival, and treatment resistance (44–46). A previousstudy reported complex interaction among neutrophils, macro-phages, and microglia. This interaction is critical in maintainingglioma's local immunosuppressive phenotype (47). Neutrophilsalso inhibit the cytolytic activity of NK cells and CD8 T cells (45).Therefore, blocking neutrophil infiltration could be suggested for

combined treatment strategy. Considering the tight associationbetween the neutrophil-to-lymphocyte ratio in peripheral bloodand local neutrophil infiltration, the neutrophil-to-lymphocyteratio may be an invasive indicator for evaluating the treatmentresponse (45).

Specific genomic alterations have been established as earlyevent initiating gliomagenesis (32). Here, we observed distinctgenomic patterns attributing to various glioma purities. Despitethere being doubt that low purity may reduce the sensitivity ofsomaticmutation andCNA testing (48), we still detected negativecorrelation between glioma purity and events of genomic alter-ation, indicating an instable genomic status in low purity cases.Therefore, genomic alterations and heterogeneity may have sub-stantial roles in editing the glioma microenvironment. The lowpurity induced an intensive immunephenotype that could furtheraggravate the genomic instability (49), thus creating a positivefeedback that exacerbates the poor prognosis and treatmentresistance (50).

Themain advantage of our researchwas the use of large sample-sized glioma cohorts and systematic analysis inmultidimensionalconditions. Our findings highlight the important role of tumorpurity and local immune cells in glioma biology and clinicalmanagement. However, we still need to clarify the extent and typeof immune responses that can promote glioma progression. Itwould be interesting to depict cellular components within amicroenvironment, interpret their clinical implications, andexplore the immunoregulatory role of glioma cells.

Taken together, glioma purity exerted a considerable effect onclinical, genomic, and biological conditions. Assessing the pro-portion of different cell typesmayhelp in elucidating the complexrole of glioma microenvironment and provide new insights intoits clinical management. An ideal classification system shouldfocus not only on the properties of tumor cells but also onnontumor cells.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: W. Cheng, G. Li, S. Han, T. Jiang, A. WuDevelopment of methodology: C. Zhang, W. Cheng, A. WuAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): C. Zhang, W. Cheng, X. Ren, X. Liu, G. Li, T. Jiang,A. WuAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): C. Zhang, W. Cheng, X. Ren, Z. Wang, G. Li, S. Han,A. WuWriting, review, and/or revision of themanuscript:C. Zhang,W. Cheng, G. Li,T. Jiang, A. WuAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): C. Zhang, W. Cheng, X. Ren, G. Li, T. JiangStudy supervision: T. Jiang, A. Wu

AcknowledgmentsThe authors conducting this work represent the ChineseGliomaCooperative

Group (CGCG). We thank Lin Fu and Qingchang Li at the Department ofPathology, the First Affiliated Hospital of China Medical University, for assis-tance in morphology analysis.

Grant SupportThis work was supported by grants from the National Natural Science

Foundation of China [Grant Numbers: 81172409 (A. Wu), 81472360 (A. Wu),and 81402045 (S. Han)], the Science and Technology Department of LiaoningProvince [GrantNumber: 2011225034 (A.Wu)], theNational Key Research and

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Development Plan [Grant Number: 2016YFC0902500 (T. Jiang)], and theCapital Medical Development Research Fund [Grant Number: 2016-1-1072(T. Jiang)].

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby marked

advertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received October 16, 2016; revised April 26, 2017; accepted July 20, 2017;published OnlineFirst July 28, 2017.

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2017;23:6279-6291. Published OnlineFirst July 28, 2017.Clin Cancer Res   Chuanbao Zhang, Wen Cheng, Xiufang Ren, et al.   Tumor Purity as an Underlying Key Factor in Glioma

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