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Genome and Epigenome

Comparative Transcriptome Analysis QuantifiesImmune Cell Transcript Levels, MetastaticProgression, and Survival in OsteosarcomaMilcah C. Scott1,2,3, Nuri A.Temiz1,4, Anne E. Sarver5,6, Rebecca S. LaRue1,7, Susan K. Rathe1,Jyotika Varshney1, Natalie K.Wolf1,8, Branden S. Moriarity1,9,10,11, Timothy D. O'Brien1,2,11,12,Logan G. Spector1,6, David A. Largaespada1,6,8,9,10, Jaime F. Modiano1,2,3,12,13,14,Subbaya Subramanian1,5, and Aaron L. Sarver1,4

Abstract

Overall survival of patients with osteosarcoma (OS) hasimproved little in the past three decades, and better models forstudy are needed. OS is common in large dog breeds and isgenetically inducible in mice, making the disease ideal for com-parative genomic analyses across species. Understanding the levelof conservation of intertumor transcriptional variation acrossspecies and how it is associated with progression to metastasiswill enable us to more efficiently develop effective strategies tomanage OS and to improve therapy. In this study, transcriptionalprofiles of OS tumors and cell lines derived from humans (n ¼49),mice (n¼ 103), and dogs (n¼ 34) were generated using RNAsequencing. Conserved intertumor transcriptional variation waspresent in tumor sets from all three species and comprised geneclusters associated with cell cycle and mitosis and with thepresence or absence of immune cells. Further, we developed a

novel gene cluster expression summary score (GCESS) to quantifyintertumor transcriptional variation and demonstrated that theseGCESS values associated with patient outcome. Human OStumors with GCESS values suggesting decreased immune cellpresence were associated with metastasis and poor survival. Wevalidated these results in an independent human OS tumorcohort and in 15 different tumor data sets obtained from TheCancer Genome Atlas. Our results suggest that quantification ofimmune cell absence and tumor cell proliferation may betterinform therapeutic decisions and improve overall survival for OSpatients.

Significance: This study offers new tools to quantify tumorheterogeneity in osteosarcoma, identifying potentially usefulprognostic biomarkers for metastatic progression and survival inpatients. Cancer Res; 78(2); 326–37. �2017 AACR.

IntroductionOsteosarcoma (OS) is the most common primary bone malig-

nancy, accounting for �2% of childhood cancers. The totalnumber of new human cases diagnosed each year in the UnitedStates is low, �400–600 annually, which presents a significantchallenge to studying this rare but deadly cancer (1). The relative5-year survival rates for all OS (�60%) and for metastatic OS(�20%) have not significantly changed since the mid-1980s(1–7). OS in dogs is much more common, with an overallincidence rate 30 to 50 times higher than humans, and a lifetimerisk up to 10% in larger, pre-disposed breeds (8). OS tumors canalso be generated in wild-type and predisposed mice via tissue-specific mobilization of the T2/ONC transposon by the SleepingBeauty Transposase (9, 10).

While in humansOS is primarily a pediatric disease, in dogs themajority of cases occur in older dogs with an average onset at7.5 years (8). Unfortunately �80% of dogs die as a result ofmetastasis within 1 year of diagnosis (11). Human dog andmouse OS share many clinical andmolecular features and insightgained from one species may be translatable to the others (9–12).

Identifying and understanding molecular mechanisms of OShave lagged behind other cancer types, partly due to the smallnumber of human tumors available for study (12, 13). Addition-ally, OS is characterized by complex karyotypes with highlyvariable structural and numerical chromosomal aberrations inhumans, dogs, and mice (9, 14–16). Sequencing studies haveidentified recurring genetic alterations in OS, some of which are

1Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota. 2Ani-mal Cancer Care and Research Program, University of Minnesota, St. Paul,Minnesota. 3Department of Veterinary Clinical Sciences, University of MinnesotaCollege of Veterinary Medicine, St. Paul, Minnesota. 4Institute for Health Infor-matics, University of Minnesota, Minneapolis, Minnesota. 5Department of Sur-gery, University of Minnesota School of Medicine, Minneapolis, Minnesota.6Department of Pediatrics, University of Minnesota School of Medicine, Min-neapolis, Minnesota. 7Department ofMedicine, University ofMinnesota School ofMedicine, Minneapolis, Minnesota. 8Department of Genetics, Cell Biology andDevelopment, University of Minnesota School of Medicine, Minneapolis, Minne-sota. 9Brain Tumor Program, University of Minnesota, Minneapolis, Minnesota.10Center for Genome Engineering, University of Minnesota, Minneapolis, Min-nesota. 11Department of Veterinary PopulationMedicine, University ofMinnesotaCollege of Veterinary Medicine, St. Paul, Minnesota. 12Stem Cell Institute Uni-versity of Minnesota, Minneapolis, Minnesota. 13Department of LaboratoryMedicine and Pathology, University of Minnesota School of Medicine, Minnea-polis, Minnesota. 14Center for Immunology, University ofMinnesota,Minneapolis,Minnesota.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

D.A. Largaespada, J.F. Modiano, and S. Subramanian contributed equally to thisarticle.

Corresponding Author: Aaron L. Sarver, University of Minnesota, 515 DelawareStreet, Minneapolis, MN 55455. Phone: 612-624-2445; Fax: 612-624-2445;E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-17-0576

�2017 American Association for Cancer Research.

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common to both human and dog OS (12, 14, 17–19). However,understanding of these genetic alterations hasnot led to improvedoutcomes forOSpatients. There remains a critical need to developdiagnostic tests that can identify risk ofOS progression at an early,pre-metastatic stage.

The goal of this study was to assess transcriptional variation inOS tumor samples, identify common patterns of expressionacross different species, and evaluate their associationwithmetas-tases and clinical outcomes. To accomplish these goals, we devel-oped the gene cluster expression summary score (GCESS) tech-nique to identify and quantify transcriptional patterns presentacross datasets and across species.We then use theGCESSmethodto derive associations between metastatic progression, outcome,and transcriptional patterns in OS and extend these results to awide range of human tumors. Based solely on our genome widemethod, we show that the loss of immune cell transcripts as wellas increases in cell-cycle transcripts are associated with pooroutcomes in OS and extend these results to a wide range ofhuman tumor types.

Materials and MethodsBiospecimen collection and processing

Human and dog biospecimens were collected from newlydiagnosed OS patients prior to treatment with cytotoxic chemo-therapy drugs. Specimens were obtained under protocolsapproved by either the University of Minnesota's InstitutionalReview Board or Institutional Animal Care and Use Committee(protocol numbers 0802A27363, 1101A94713, and 1312-31131A)or theUniversity ofColorado Institutional ReviewBoardor Institutional Animal Care and Use Committee (AMC635040202, AMC 200201jm, AMC 2002141jm, 02905603(01)1F, and COMIRB 06-1008).

Human samplesHuman patientOS samples (n¼ 44) and normal bone samples

(n ¼ 3) were obtained from the University of Minnesota Biolog-ical Materials Procurement Network (UMN BioNet) or theCooperative Human Tissue Network, both of which followstandardized patient consent protocols. Samples had beendeidentified and only a limited amount of patient informationwas provided. Saos-2, U-2 OS, MG-63, and 143B human OS celllines were purchased from ATCC and were authenticated by theUniversity of Arizona Genetics Core using short tandem repeatprofiling, as well as an osteoblast cell line, which was a gift fromDr. Richard G. Gorlick (Albert Einstein College of Medicine, NewYork, NY), were also sequenced. Supplementary Table S1 sum-marizes the available metadata characteristics for the samplesused in this study.

Dog samplesDog OS samples (total n ¼ 31) were obtained from dogs with

naturally occurring primary appendicular tumors, recruitedbetween 1999 and 2012. The majority of the samples were fromRottweilers andGolden Retrievers. Specimenswere obtainedwithowner consent under approved protocols as previously described(13). Also included in this study were two cell lines (OSCA-8 andOSCA-78) derived from primary OS tumor as previouslydescribed (20), and one dog osteoblast sample, CnOB, purchasedfrom Cell Applications. The OSCA cell lines are available fordistribution through Kerafast, Inc.

Mouse samplesMouseOS samples (n¼92 tumor,n¼11 cell line)werederived

from previously established experimental models. Included inthis study were 25 OS samples frommice with somatic inductionof Trp53R270H expression inOsx1-expressing cells (9), 67 samplesfrom Sleeping Beauty transposon accelerated OS in wild-type andTrp53R270H mice (10), and 11 cell lines established from mouseOS tumors (9).

RNA extraction from frozen tumor tissue and cell linesIsolation of total RNA from tissues, avoiding areas of necrosis,

and from cell lines was performed according to the recommendedprotocol for Ambion's TotalRNA kit from Thermo Fisher Scien-tific. Samples were quantified using fluorescence by RiboGreendye (Thermo Fisher Scientific). RNA integrity was assessed usingcapillary electrophoresis (RIN > 6.5) with the Agilent 2100BioAnalyzer system (Agilent Technologies).

RNA sequencingSequencing libraries of each sample were prepared using the

TruSeq Library Preparation Kit (Illumina). Paired end sequencing(30–40 million reads per sample) was done at the University ofMinnesota Genomics Center on a High Seq 2000 (Illumina). Theraw FASTQ files are available at the NCBI Sequence Read Archiveand linked to from Gene Expression Omnibus SuperSeriesGSE87686.

Publicly available human dataRNA sequencing (RNA-Seq) FASTQ files and outcome related

metadata for 35 additional human OS samples (HOS2) wereobtained from dbGap:phs000699.v1.p1 (http://www.ncbi.nlm.nih.gov/gap; ref. 21). RNA-seq files were also obtainedfrom 25 additional human OS samples (HOS3) available frompreviously published studies (9, 15, 17).

FASTQ files from the HOS2 cohort were mapped to the refer-ence genome, and fragments per kilobase of transcript permillionmapped (FPKM) values were calculated using the same protocolsas the samples in this study, while FASTQ files from the HOS3cohort were mapped to the reference genome and RSEM expres-sion values were calculated using the The Cancer Genome Atlas(TCGA) RNA-sequencing protocol (22, 23). In this study, thesamples from both of these cohorts were used for calculatingpairwise Pearson correlation coefficients andwere included in thetranscriptome analyses described below. The HOS2 cohortincluded survival data and information on presence of metastasisat diagnosis, so this cohort was used for survival analysis.

GEO dataset series GSE21257, which consisted of genome-wide expression data of 53 humanOS tumors produced from theIllumina human-6 v2.0 expression array, was downloaded for thisstudy. Patient outcome data, including survival and metastasis,was included in this study.

TCGA data portal (https://gdc.cancer.gov/) was used to down-load survival times, death events, and RNA-Seq data for 5582tumors as described in Supplementary Table S2.

RNA-seq workflowBriefly, mapping was carried out using Tophat (24), Samtools

(25), and Cufflinks (26) to generate FPKM values using referencegenomes as described in Supplementary Methods. To minimizethe effects of dividing FPKM values by numbers close to 0 andstochastic noise, 0.1 was added to each FPKM value (27, 28). The

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FPKM files are available at GEOGSE87686. Mapping statistics aresummarized in Supplementary Table S3.

Transcriptome analysesDue to the high correlations between human OS datasets from

multiple sources, the human datasets were combined for furtheranalyses (9, 15, 17, 21). The �8,000 most variable genes acrossthe full same-species datasets were identified for clustering byspecies (standard deviation cutoffs: human > 0.93, mouse > 0.72,and dog > 0.79). Cluster 3.0 (C Clustering Library 1.52) was usedto log2 transform and gene-mean-center the data and then per-form hierarchical average linkage clustering using the Pearsonsimilaritymetric. Clustering data were visualized in Java TreeView(version 1.1.6r4).OptiType, a precisionHLA typing tool (29),wasused to identify human OS samples derived from the samepatient.

Systematic identification of gene clustersGene clusterswith a dendrogramnode correlation >0.60 and at

least 60 individual genes were identified in each of the speciesdatasets. The 0.6 Pearson correlation cutoff value was chosen, as itis a widely accepted conservative confidence threshold. The min-imum cluster size of 60 was chosen to ensure that only largertranscriptional patterns were identified. Permuted and randomdatasets were used to show that these thresholds would notidentify clusters in artificial datasets that do not contain mean-ingful transcription patterns. Gene clusters representing batcheffects from the combination of different human samples wereremoved from further analyses.

Generation of control datasetsFor each species, a randomdataset and a permuted dataset were

generated as controls. Random datasets were generated by ran-domly selecting values between �2 and 2 to replace the actual(mean-centered) values. Permuted datasets were generated byrandomly reordering the values for each gene.

GCESS calculationThe GCESS is defined as the sum of expression values

(log2-transformed and mean centered) of all genes in aparticular defined cluster for a single sample. The GCESSquantifies transcriptional variation between tumors. It takesmany correlated individual transcript data points and con-denses them into a single value.

Pathway analysisThe Ingenuity Pathway Analysis (IPA) suite (Qiagen) was used

to identify pathways associated with gene clusters.

Statistical analysisStatistical significance was calculated using the log-rank test or

by Fisher exact test depending on analysis and a P < 0.05 was

considered significant. Kaplan–Meier (KM) survival plots weregenerated using the "survival" package in R (Version 0.98.1103;refs. 30, 31). The GCESS values were used to rank the tumors intoquartile groups and the quartile groups were systematically testedfor association with outcome.

Histopathology, immunohistochemistry, and additional sta-tistical information is provided in Supplementary Methods.

ResultsOS tumors show a common transcriptional profile acrossspecies that is distinct from other types of tumors

To better understand the OS transcriptome, we performedRNA-seq analysis on mRNA libraries generated from human,dog, andmouse tumors and cell lines (Table 1). We hypothesizedthat despite the highly complex karyotypes associated with OS,tumors would maintain common transcriptional characteristicsbetween species that were distinct from other types of tumors. Totest this hypothesis, we calculated pairwise Pearson correlationcoefficients within and between our OS cohort (HOS1) and twoindependent human OS tumor cohorts (HOS2 and HOS3)recently published by other groups (Fig. 1; refs. 9, 15, 17, 21).Strong correlations were observed within each human cohort(average intracohort correlations: HOS1 0.62, HOS2 0.67, andHOS3 0.66). Strong correlations were also observed between ourcohort and the previously published cohorts (average intercohortcorrelations: HOS1–HOS2 0.61, HOS1–HOS3 0.53). In contrast,strong correlations were not observed between HOS1 and otherhuman tumors obtained from TCGA (cervical 0.26, colorectal0.41, glioblastoma0.30, leukemia0.24, prostate adenocarcinoma0.34, and thyroid cancer 0.17), indicating that there are commontranscriptional events that define OS pathology (Fig. 1A).

To establish the cross-species relevance of the dog(DOS; Fig. 1B) and mouse (MOS; Fig. 1C) OS tumors, wecalculated both inter- and intraspecies correlations. Similar tohumans, average intraspecies correlations were high in both dog(0.76) and mouse (0.68) samples. Dog and mouse OS sampleswere both correlated with the human OS tumors (HOS1-DOS0.48,HOS1-MOS 0.52), but notwith other human cancers (DOS-cervical 0.18, MOS-cervical 0.14, DOS-colorectal 0.31, MOS-colorectal 0.28, DOS-glioblastoma 0.21, MOS-glioblastoma0.25, DOS-leukemia 0.14, MOS-leukemia 0.14, DOS-prostateadenocarcinoma 0.24, MOS-prostate adenocarcinoma 0.21,DOS-thyroid cancer 0.13, MOS-thyroid cancer 0.12), validatinga cross-species analysis strategy to identify common transcrip-tional components in the development and progression of OS.

OS tumors show common transcriptional variation patternsacross three species

We hypothesized that patterns of transcriptional variationwouldbe conserved inOS tumors across species. To systematicallyassess transcriptional variations across human, dog, and mouse

Table 1. RNA-seq samples used for this study

Source Species OS tumors OS cell lines Other Total

This study Human 44 5 3 normal bone 52This study Mouse 92 11 103This study Dog 31 2 1 osteoblast cell line 34Perry et al. (21) Human 35 35Previous studies (9, 15, 17) Human 25 25

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OS tumors, each dataset was first analyzed independently usingaverage linkage clustering. Tightly correlated and large gene clus-ters representing patterns of intertumor transcriptional variationwere defined as having both a dendrogram node correlationgreater than 0.6 and a minimum of 60 genes. A total of 9 human,5 dog, and 11 mouse gene clusters were identified (Fig. 2A,Supplementary Table S4). To ensure that these clusters were notartifacts, similarly sized random datasets and shuffled permuta-tions of the real data were generated and clustered. No clusterspassed the threshold criteria in either the random or permuteddatasets, validating that the clusters observed in the real RNA-Seq

data represent true transcriptional variation (SupplementaryFigs. S1–S9).

To determine whether the identified gene clusters repre-sented sets of genes common across the 3 species, pairwisepercent overlaps were calculated between each gene clusterfrom a single species and all clusters in the other 2 species. Theresulting percentage overlap values were clustered for eachspecies pair. Several clusters of genes showed significant over-lap across species (Fig. 2B–D; Supplementary Table S4), indi-cating conserved patterns of transcriptional variation in OStumor samples.

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Figure 1.

OS transcriptome profiles across three species are more similar to each other than to other types of human tumors. Pairwise Pearson correlations werecalculated between human (HOS1; A), dog (DOS; B), and mouse (MOS; C) OS data sets, publicly available OS datasets (HOS2 and HOS3) and sets of 25 tumorsfrom the TCGA representing a wide range of tumor types using 12,062 genes common in all three species.






To describe the potential biological significance of these com-mon gene clusters, enriched functional annotations were identi-fied using IPA software (Qiagen). The most conserved clusteracross species was composed of transcripts that were indepen-dently highly enriched in genes associated with cell cycle andmitotic functions. The next two highly conserved clusters acrossspecieswere each independently enriched in transcripts relating toimmune cell functions (Fig. 2D, Supplementary Table S5). A genecluster enriched for muscle differentiation genes was observed inthe human and mouse tumors but it was not present in the dogtumors.

We next asked if the genes in the two immune cell gene clustersrepresented a broad spectrum of immune cells or were enrichedfor particular leukocytes. We compared these immune cell geneclusters to a gene signature matrix (LM22), which consists of 547genes and was created to identify genes unique to 11 differentsubtype populations of immune cells (32). Overall, our humanimmune cell annotated gene clusters were highly enriched in the

LM22 gene transcripts (Supplementary Table S4). Using the LM22annotations to determine the types of active leukocytes present (asrepresented by their unique transcripts), we found genes repre-senting monocytes to be most enriched in human cluster-1 andgenes representing T cells to be most enriched in human genecluster-8 (Supplementary Table S4). Results from assessingenrichment of the LM22 genes in the dog and mouse immunecell gene clusters also supported these results (SupplementaryTable S4).

The GCESS technique quantifies tumor transcriptionalvariation

The GCESS was developed to reduce the high dimension-ality of expression profiles generating a normalized and easilycomparable value per sample for use in further associationanalyses. The GCESS is defined as the sum of expression values(log2-transformed and mean centered) of all genes in a par-ticular gene cluster for a single sample. A negative GCESS

Figure 2.

OS transcriptome profiles show common intertumortranscriptional variation across human, mouse, anddog samples.A, FPKMvalues derived fromOS tumorsand cell line data (indicated by black bars belowheatmaps) were log transformed and mean centeredwithin each species. Invariant genes were thenremoved leaving (i) human (n ¼ 9,190), (ii) mouse(n ¼ 8,051), and (iii) dog (n ¼ 8,003) genes, whichwere used for unsupervised average linkageclustering. Transcripts with increased levels areshown in yellow, while transcripts with decreasedlevels are shown in blue. Transcript level clusters withcorrelation > 0.60 and containing � 60 genes weresystematically identified and these clusters arevisualized with a numbered black bar to the right ofeach of the heatmaps. Lists of each gene in eachcluster are provided in Supplementary Table S4. Geneclusters observed in more than one species aresurrounded by colored boxes and are given areference number. The "cell cycle"–conservedtranscript cluster is shown in red (HOS-4, MOS-8,DOS-3). The "Immune-1" transcript cluster is shown ingreen (HOS-1, MOS-4, DOS-4). The "Immune-2"transcript cluster is shown in purple (HOS-8, MOS-1,DOS-5). A cluster composed of muscle transcriptsonly present in human and mouse data is shown inblue (HOS-3, MOS-2). B, Close-up of the conservedcell-cycle clusters (HOS-4, MOS-4, and DOS-3)showing the namesof representative genes.C,Close-up of the Immune-1 (HOS-1, MOS-4, and DOS-4) andImmune-2 (HOS-8, MOS-1, and DOS-5) regionsshowing the names of representative genes. D, Venndiagrams showing the number of overlapping genesobserved to be commonly present in both datasets.Fisher exact test results indicated that the observedoverlap is highly unlikely to occur by random chance.Highly significant Fisher exact tests (P < 10E�10) aremarked with asterisks (��).

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indicates relative underexpression of the group of genes in thatsample compared with all of the samples in the analysis set, apositive GCESS indicates overexpression, and a GCESS close to0 (zero) indicates mean expression. The GCESS summarizesthe relative transcript levels of many correlated genes into asingle value. This value is calculated for each cluster in eachtumor. This allows for tumors to be rank ordered by summaryscore, which is based on the observed transcriptional data.Multiple summary scores were generated for each tumor sam-ple, allowing for the independent comparison of the impact ofeach identified gene cluster, thereby achieving an unbiaseddimensional reduction.

To better characterize the conserved OS tumor transcriptionalvariation, GCESS values were calculated for the identified for allidentified gene clusters in each sample (Fig. 3A and Supplemen-tary Table S6). In addition to human, dog, and mouse OS tumorsamples, we also analyzed tumor-derived cell lines, normalhumanbone cells, anddogosteoblasts. Thedistribution ofGCESSvalues (Fig. 3B–D) shows distinct patterns for cells/cell lines,normal bone, and tumor samples.

Tumor-derived cell lines had higher cell-cycle GCESS valuescompared with tumor tissues, while normal human bone hadcell-cycle GCESS values corresponding to the lower range ofGCESS values obtained from tumor samples (Fig. 3B). Dogosteoblasts had an extremely low cell-cycle GCESS. These resultsare consistent with the upregulation of cell-cycle genes in a subsetof highly proliferating OS tumors (13).

Tumor-derived cell lines had lower immune GCESS valuescompared with the majority of tumor tissues in all three speciesdatasets. The immune cell GCESS of the normal human bonesample was within the middle of the range seen in human tumorsamples. Dog osteoblasts had GCESS values corresponding to thetumor-derived cell lines (Fig. 3C and D). These results are con-sistent with the absence of immune cells in cell lines and thevariable presence of immune cells in tumor-derived tissues fromall 3 species.

To validate the variable presence of immune cells indicated byGCESS immune cluster scores, we evaluated 10 FFPE sectionsderived from canine OS tumors, which were also sequenced, forthe presence of T cells and macrophages within tumor stroma byimmunohistochemistry (Supplementary Fig. S10A–S10D andSupplementary Table S7). Immunohistochemistry staining sup-ported the transcriptome derived GCESS data for both MAC387and CD3 staining. Stromal MAC387 was not observed in the 3tumorswith the lowestGCESS scores for the immune cell cluster-1and occasional MAC387-positive cells were observed for the 3tumors with the highest immune cluster-1 GCESS scores. One ofthe middle range tumors showed the presence of MAC387 pos-itive staining cells within the stroma while 3 others did not. ForCD3, only the sample with a positive GCESS immune cluster-2score showed T cells presentwithin the tumor stroma.Of note, thenext four tumors sorted by immune cluster-2 GCESS score allshowed MAC387-positive cells within the tumor stroma.

Minimization of multiple testing errorsWe hypothesized that patterns of transcriptional variation

present in tumors should indicate associations with patient out-come and that methodological data analyses improvementswould be necessary to reveal these associations. Associationsbetween OS patient outcomes and individual gene transcriptlevels are commonly calculated using the KM estimator, a non-

parametric statistic that estimates the survival function based oncensored lifetime data. KM survival analyses were calculated usinggroups defined by individual transcript levels for our dog andhumanHOS2 datasets. Potentially significant events were presentfollowing comparison of all transcripts (P < 0.00001; Supple-mentary Fig. S11) used in clustering. To assess whether theseassociations were false positives or not, similarly sized randomdatasets and shuffled permutations of the real data were alsoanalyzed. Random and shuffled permutations led to similarly"significant events" as were observed in the real data due tomultiple testing of �8 to 9,000 individual tests, indicating thatmethodological improvements were necessary (SupplementaryFig. S11).

Two routes are typically used to minimize the effects of mul-tiple testing in statistical analyses. The first entails increasingsample numbers in order to surpass multiple testing correctionsvia increased statistical power. This was not feasible in this case. Asecond approach, which we used here, was to drastically decreasethe number of tests performed (dimensionality reduction) bytesting the gene cluster GCESS values rather than all individualtranscript values. The GCESS was used to rank the tumors intoquartile (Q) groups and systematically compare Q1- (lowestGCESSs) vs. Q234, Q12 vs. Q34, and Q123 vs. Q4. (Fig. 3A)Using this approach, associations between outcome and immunecell GCESS could be systematically examined without priorknowledge of the type of association present. Examination ofrandomly generated and real, but permuted datasets using the fullpipeline did not identify gene clusters, limiting association anal-yses to gene clusters defined by nonrandom transcript patterns.

Systematic examination of GCESSs with tumor outcomesidentifies poor patient outcome association with highexpression of cell-cycle cluster genes and low expression ofimmune cell cluster genes

Applying the GCESS approach to the dog data revealed anassociation between increased transcript levels of cell-cycle genesand decreased time to death. Significantly worse outcomes wereassociated with the higher GCESS groups in both theQ123 vs. Q4and Q12 vs. Q34 comparisons. Similarly, the human outcomeanalysis revealed that the highest cell cycle GCESSs (Q4) also hada strong trend towards decreased patient survival times (Q123 vs.Q4). Analyzing the human Immune-1GCESSs demonstrated thathuman patients with the lowest immune cell cluster GCESSs hadsignificantly shorter times to death (Q1 vs. Q234). The Immune-2GCESSs also showed a trend towards association between lowGCESSs and decreased time to death (Fig. 3; Supplementary Figs.S12–S15).

Association between GCESS and patient outcome validated inan independent cohort

If the conserved tumor transcriptional variation and associa-tion between GCESS and patient outcome revealed by this meth-odology are generally observable in OS tumors, they should alsobe observable in older array hybridization-based data. To validatethis hypothesis, we analyzed GSE21257, an IlluminamRNA arraydataset, which contained genome wide expression data for 53tumors from patients with known outcome data, including sur-vival and metastasis (33). Following a strategy similar to the oneused for the RNA-seq data, 14 highly correlated gene clusters wereidentified.Geneoverlap analyses comparing clusters derived fromarray and RNA-seq data sets identified gene clusters from the array






Figure 3.

GCESS values represent the relative amount of transcript present for each cluster of genes and identify correlation between high cell cycle or low immune cellGCESSs and poor survival. A, Overview of analyses method. i, The log2-transformed and mean-centered values for each gene in a cluster are summed togenerate a single score (GCESS value) for each sample. Samples with high relative expression levels have large positive GCESSs, while samples with lowrelative levels have large negative GCESSs. ii, The GCESS scores can be used to separate the tumors into groups based on GCESS score quartiles. iii, TheGCESS quartile-based groups can then be examined for associations with outcome using KM analyses. GCESSs for human (tumors, normal bone, OS cell lines),mouse (tumors, OS cell lines), and dog (tumors, osteoblasts, OS cell lines) sampleswere calculated and violin plotswere generated for the "cell-cycle" cluster (B), the"Immune-1" cluster (C), and the "Immune-2" cluster (D). Samples were ranked by GCESS and divided into quartiles groups (Q1 ¼ lowest GCESSs; Q4 ¼ highestGCESSs). KM analyses were performed, using human (n¼ 35) and dog (n¼ 19) samples for which survival data was known, to determine correlations between low/high GCESSs and survival. There were 17 (out of 35) death events in the human data (HOS2) and 19 (out of 19) death events in the dog data. B, A significantassociation with shorter time to death was observed with a high cell-cycle GCESS in the dog cohort, and a strong trend was also present in the human data.C and D, In the human data, low Immune-1 GCESS was significantly associated with a worse survival (C) and a strong trend was present betweenlow Immune-2 and worse survival (D).

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Figure 4.

Replication of association between high cell-cycle GCESS or low immune GCESS and poor survival outcomes using array data from independent cohort.Following a similar strategy to the one used for the RNA-seq data, 14 strong and highly correlated clusters were identified in the GSE21257 dataset (A). Gene clusteroverlap analyses comparing clusters derived from human array and human RNA-seq data sets identified four clusters that corresponded to the conservedRNA-Seq clusters. The cell-cycle cluster is labeled in red (B), the Immune-1 cluster is labeled in green (C), and the Immune-2 cluster is labeled in purple (D). Fisherexact test to assess the likelihood of observing the overlap by random chance indicated that the enrichment was highly significant (P < 1E�10) for the cell-cycle andimmune clusters. B, KM analyses using GCESS groups using the approach outlined in Fig. 3A showed that high levels of cell-cycle transcripts were associated withworse outcomes and significantly increased likelihood of tumormetastasis. C andD, Low levels of Immune-1 (C) and Immune-2 (D) transcriptswere associated withsignificantly worse survival and significantly increased likelihood of tumor metastasis. E, Metastatic samples have lower Immune-2 transcript levels in mouseand human samples. MOS-1 GCESS values were lower in tumors frommice where metastatic lesions were observed during necropsy (P < 0.05). Tumors from humanpatients with metastases present at diagnosis showed a trend toward lower Immune-2 GCESS scores in both the RNA-Seq data as well as the array data,and this trend became increasingly significant in the array data when tumors where metastases were observed in the patient within 1 year (P < 0.001) orat any point (P < 0.0001) were included with the patients with metastases at diagnosis.






that corresponded to theRNA-seq defined Immune-1, Immune-2,cell-cycle, and muscle transcript clusters (Fig. 4A). KM survivalanalyses utilizing sample GCESSs generated from each of thesearray gene clusters again showed that patients whose tumors hadlow immune cell GCESSs were more likely to succumb to OS(Fig. 4B–D; Supplementary Figs. S16 and S17).

A KM-analysis examining the time to metastasis was alsoperformed. Low immune cell GCESSs or high cell cycle GCESSswere strongly associated with faster progression to metastasis(P ¼ 0.0001 and P ¼ 0.02, respectively; SupplementaryFigs. S18–S20). These results independently validate the initialhuman data set findings as well as the general utility of themethodology described independent of experimental platform.

Low immune-2 GCESS associated with metastasis in humanand mouse samples

Following necropsy, many of the mice had observable metas-tases. Scores from samples with metastases had lower Immune-2scores than samples where metastatic tumors were not observed.In both human datasets, Immune-2 scores were lower in sampleswhere metastases were present at diagnosis. This differencebecame significant when patients with metastasis in less thanone year were combined with patients where metastasis wasobserved at diagnosis. The significance became even strongerwhen patients who showed metastasis at any point were com-pared with patients where metastasis was not observed. Thesefindings indicate that the Immune-2 score has prognostic poten-tial for determining the likelihood of a tumor metastasizing inhuman patients (Fig. 4E).

OS-derived cell-cycle and immune cell GCESSs correlate withpoor clinical outcomes across multiple tumor types

We hypothesized that the patterns of transcriptional variationswe found to be associated with outcomes in OS may also berelevant across many different types of tumors. To determineif increased cell-cycle transcripts and decreased immune cell

transcripts are each also associated with poor clinical outcomemeasures across different tumor types, GCESSswere calculated foreach of the TCGA tumor datasets (using the genes comprising therespective human OS clusters to identify each tumor's cell cycleand immune cluster), which were then subjected to KM-survivalanalysis.High cell-cycleGCESSswere significantly associatedwithpoor survival outcomes in kidney renal clear cell carcinoma(KIRC), and clear trends were apparent in liver hepatocellularcarcinoma (LIHC), lung adenocarcinoma (LUAD), pancreaticadenocarcinoma (PAAD), head and neck squamous cell carcino-ma (HNSC), and cutaneous melanoma (SKCM; Table 2). Lowimmune cell GCESSs from human OS gene cluster 1 were signif-icantly associatedwithpoor survival outcomes in SKCM, and cleartrends were apparent in LUAD, colon adenocarcinoma (COAD),and cervical squamous cell carcinoma and endocervical adeno-carcinoma (CESC; Table 2). Low immune cell GCESSs derivedfrom human OS gene cluster 8 were significantly associated withpoor survival outcomes in SKCM and clear trends were visible in,HNSC, LUAD, LIHC, COAD, CESC, and breast invasive carcino-ma (BRCA). These results indicate that the survival associationsbetween cell-cycle and immune transcript expression levelsobserved in OS are also present in a wide range of tumor typesand that the GCESS methodology is capable of observing theseassociations in datasets where improved sample power exists.

DiscussionMany diverse genetic events, including a catalog of rare events,

have been reported to lead to OS formation, progression, andmetastasis. Our data indicate that loss of immune cell infiltrationand increased levels of cell-cycle transcripts are two specifictranscriptional prognostic biomarkers for metastasis, and overallpoor survival of OS patients. These transcriptional signatures alsohad prognostic utility across many types of human tumors,suggesting they are common transcriptional markers of patho-logical progression. Further, these two transcriptional patterns

Table 2. Validation of GCESS methodology in TCGA cancer data sets

A. HOS-4 "cell cycle" B. HOS-1 "Immune-1" C. HOS-8 "Immune-2"q1 vs. q234 q12 vs. q34 q123 vs. q4 q1 vs. q234 q12 vs. q34 q123 vs. q4 q1 vs. q234 q12 vs. q34 q123 vs. q4

BLCA 0.33 0.82 0.74 BLCA 0.72 0.81 0.64 BLCA 0.60 0.38 0.11BRCA 0.94 0.11 0.71 BRCA 0.75 0.37 0.76 BRCA 0.04 0.07 0.28CESC 0.41 0.27 0.55 CESC 0.27 0.31 0.01 CESC 0.07 0.02 0.01COAD 0.41 0.31 0.79 COAD 0.01 0.15 0.01 COAD 0.04 0.97 0.44GBM 0.95 0.97 0.22 GBM 0.29 0.19 0.47 GBM 0.84 0.56 0.18HNSC 0.52 0.49 0.03 HNSC 0.56 0.43 0.15 HNSC 0.003 0.02 0.02KIRC 0.17 1.26E�06 4.68E�15 KIRC 0.28 0.11 0.14 KIRC 0.05 0.03 0.20LIHC 0.01 0.03 0.001 LIHC 0.09 0.25 0.78 LIHC 0.07 0.01 0.13LUAD 0.002 0.01 0.02 LUAD 0.61 0.05 0.02 LUAD 0.20 0.09 0.01LUSC 0.01 0.03 0.78 LUSC 0.21 0.30 0.11 LUSC 0.65 0.21 0.40OV 0.78 0.73 0.88 OV 0.74 0.67 0.69 OV 0.44 0.17 0.19PAAD 0.002 0.004 0.003 PAAD 0.08 0.87 0.52 PAAD 0.86 0.67 0.37PRAD 0.37 0.06 0.77 PRAD 0.25 0.14 0.92 PRAD 0.94 0.20 0.92READ 0.21 0.39 0.33 READ 0.66 0.64 0.85 READ 0.03 0.35 0.96SKCM 0.11 0.04 0.83 SKCM 4.99E�03 2.21E�05 6.48E�04 SKCM 6.58E�05 4.43E�06 1.88E�04

Higher GCESS ¼ poor outcomeLower GCESS ¼ worse outcome

NOTE: GCESSswere calculated for each of the TCGA tumor datasets (using the genes comprising the respective humanOS clusters to identify each tumor's cell-cycleand immune cell clusters) and KM analyses were used to determine correlation with poor survival outcomes. A, High cell-cycle GCESSs (based on human OScluster-4) were highly significantly associated (red) with poor survival outcomes in KIRC, and clear trends were also apparent LIHC, LUAD, and PAAD. B, LowImmune-1 GCESSs [based on human OS cluster-1 (monocyte enriched)] were significantly associated (green) with poor survival outcomes in SKCM, and clear trendswere apparent in CESC, COAD, LUAD, and SKCM. C, Low Immune-2 GCESSs [based on human OS cluster-8 (T-cell enriched)] were significantly associated (green)with poor survival outcomes in SKCM, and clear trends were apparent in BRCA, CESC, COAD, HNSC, LIHC, LUAD, rectum adenocarcinoma (READ), and SKCM.

Scott et al.





appear to be independent (Supplementary Table S6; Supplemen-tary Fig. S21).

Tumor transcription can be conceptualized as resulting fromloss of control of a series of independent transcriptional modules(gene clusters). The GCESS technique described in this papergenerates a single meaningful value for each module in a tumor.The GCESS value can then be used for phenotype associationdiscovery, thereby minimizing the multiple testing risks in data-sets underpowered for meaningful genome-wide associationanalyses. These types of errors are clearly described for SNPassociation studies but remain prevalent in genome-wide studiesutilizing large numbers of parallel analyses. Empirical testing ofsingle-gene–based strategies to associate tumor transcript levelwithoutcome revealed that for every 20 random transcripts tested,1 false positive prognostic transcript was likely to be identified(using an uncorrected P < 0.05). If 1,000 transcripts were tested,then this would result in 50 likely false positives. Many genome-wide analyses of RNA-seq or array data routinely involve testingthousands of transcripts, which would potentially result in hun-dreds of false positives. Thismay explainwhymany transcription-based prognostic tests fail to be reproducible in independentcohorts.

Our previous work identified increased levels of cell-cycletranscripts in cell lines generated from OS tumors with worseoutcomes (13). We have now extended this work to OS tumorsand provide a novel methodology for identifying transcription-ally related gene clusters, even if they are not the primary com-ponent present in the dataset, and testing their association with avariety of outcomes while minimizing errors from multipletesting. These results are highly consistent with the CINSARCsignature (Complexity INdex in SARComas) predictive of metas-tasis-free survival. Many of the genes identified encode for pro-teins involved in cell-cycle/mitosis, cytokinesis, mitotic check-point, and DNA damage repair (34, 35).

Our data also indicate that cell-cycle–mediated events havemore prognostic potential for dog disease progression comparedwith humandisease.We speculate this is largely due to differencesin therapeutic regimens and overall commitment to therapy;however, it may also represent a fundamental difference betweenhuman and dog immune responses. Another potential confound-ing factor is the general characteristic of dog OS to progress muchfaster than typically observed in either normal or late-onsethuman OS. Dogs with OS may not survive long enough for therole of the immune cells to become observable. In specific dogbreeds, the immune systemmay also have a reduced capability torecognize and respond to tumor cells.

Transcriptional profiles derived from grossly dissected tumorshave variable types and quantities of cells present, includingstromal and immune cells in addition to cancer cells. Our GCESStechnique provides a straightforward method to indicate therelative abundance of immune cells in the tumor tissue sample,which can also be generally applied to any component present in atranscriptional dataset. Importantly, we compared results fromour method with a previously published tool (ESTIMATE) thatinfers tumor purity (36). We found that in the naturally occurringhuman and dog OS tumor samples there was a strong correlationbetween our immune cell GCESS value and the ESTIMATEimmune score.

The GCESS method provides a useful tool to distinguishbetween osteosarcoma tumor subtypes that seem to have dis-tinctly different likelihoods ofmetastatic progression as a result of

the presence of decreased numbers of immune cells within thetumor stroma. Supporting our conclusion is the lack of expressionof these genes in OS cell lines, high correlation to the ESTIMATEscores, validation in multiple independent datasets, and a grow-ing body of literature on the potential, variable immunogenicityin cancers such as OS.

As a genomically chaotic disease, conventional wisdom sug-gests OS must provide an abundance of antigens that typicallywould result in increased recognition by the host immune system,yet somehow OS tumors and their metastases have proven capa-ble of escaping immune surveillance. Our results reinforce thetheory that immune cell infiltration and monitoring is a normalprocess in bone tissue and suggest that disruption of this processin a subset of tumors is associated with increased tumormortalityand progression to metastasis (Supplementary Fig. S22). Deter-mining whether the observed decrease in immune cells is intrin-sically controlled by the tumor, or extrinsically by the immunesystem, or both, is an important, future research question in OSand other cancers.

Analyses of the TCGAdata showed that the association betweendecreased immune cell GCESS and outcome was strongest incutaneous melanoma (SKCM), a data set containing a largenumber of metastatic samples. Importantly, melanoma patientsrespond to immune checkpoint blockade therapy (37), and ourresults indicate that this same approachmayhave clinical utility insome OS patients. Furthermore, these results support an associ-ation between absence of immune cells, metastasis, and clinicaloutcome. Where immune cell outcome associations were seen inthe TCGA the effects were more significant using the T cell–enriched cluster relative to the monocyte enriched cluster. Onthe contrary, in our OS datasets the monocyte enriched genecluster was more significant (lower P values) than the T cell–enriched gene cluster.

We conclude that using multispecies datasets provides uniqueopportunities and insights to identify biologically meaningfulgene signatures and to identify aspects of the immune responsethat can bemanipulated therapeutically to improve the quality oflife and outcomes of childrenwith bone cancer, as well as patientswith other sarcomas and solid tumors.

Disclosure of Potential Conflicts of InterestD.A. Largaespada is Chief Science Officer for Surrogen/Recombinetics Inc.

and Chair of the Board for B-MoGen, Inc., reports receiving a commercialresearch grant from Genentech, has ownership interest (including patents) inSurrogen/Recombinetics, B-MoGen, Inc., Immunsoft, Inc., and NeoClone, andis a consultant/advisory board member for NeoClone Inc. and Immusoft, Inc.J.F. Modiano has ownership interest in a patent application. No potentialconflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design:M.C. Scott, R.S. LaRue, L.G. Spector, D.A. Largaespada,J.F. Modiano, S. Subramanian, A.L. SarverDevelopment of methodology:M.C. Scott, A.E. Sarver, R.S. LaRue, J. Varshney,D.A. Largaespada, J.F. Modiano, S. Subramanian, A.L. SarverAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): J. Varshney, N.K. Wolf, B.S. Moriarity, T.D. O'Brien,L.G. Spector, D.A. Largaespada, J.F. Modiano, S. SubramanianAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): M.C. Scott, N.A. Temiz, A.E. Sarver, R.S. LaRue,S.K. Rathe, T.D.O'Brien, L.G. Spector, J.F. Modiano, S. Subramanian, A.L. SarverWriting, review, and/or revision of the manuscript: M.C. Scott, N.A. Temiz,A.E. Sarver, S.K. Rathe, B.S. Moriarity, T.D. O'Brien, L.G. Spector, D.A. Largae-spada, J.F. Modiano, S. Subramanian, A.L. Sarver






Administrative, technical, or material support (i.e., reporting or organizingdata, constructingdatabases):M.C. Scott, R.S. LaRue, J. Varshney, J.F.Modiano,S. Subramanian, A.L. SarverStudy supervision:D.A. Largaespada, J.F.Modiano, S. Subramanian, A.L. SarverOther (obtained funding to support research study): J.F. Modiano

AcknowledgmentsThe authors would like to thank John Garbe for technical assistance in the

RNA-seq data processing pipeline, Rebecca S. Larue for running the RNA-seqdata processing pipeline, Colleen Forster and the Clinical and TranslationalScience Institute's Bionet laboratory for optimization and performance ofimmunohistochemistry, the Minnesota Supercomputing Institute for comput-ing time and storage space, and the University of Minnesota Genomics Centerfor sequencing services. This work was generously supported by the Zach

Sobiech Osteosarcoma Fund, Keren Wyckoff Rein in Sarcoma Foundation,grants to A.L. Sarver NCI (CA211249), J.F. Modiano NCI (CA208529) andMorris Animal Foundation (D13CA-032), D.A. Largaespada NCI (CA113636)and ACS (#123939), and A.E. Sarver NIH (CA099936), S. Subramanian ACS(RSG-13-381-01) and the Masonic Cancer Center Comprehensive CancerCenter Support Grant (CA077598).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received February 27, 2017; revised July 13, 2017; acceptedOctober 18, 2017;published OnlineFirst October 24, 2017.

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2018;78:326-337. Published OnlineFirst October 24, 2017.Cancer Res Milcah C. Scott, Nuri A. Temiz, Anne E. Sarver, et al. OsteosarcomaTranscript Levels, Metastatic Progression, and Survival in Comparative Transcriptome Analysis Quantifies Immune Cell

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