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• W2951880440 abstract "•GBM-intrinsic transcriptional subtypes: proneural, classical, mesenchymal•NF1 deficiency drives recruitment of tumor-associated macrophages/microglia•Resistance to radiotherapy may associate with M2 macrophage presence•CD8+ T cells are enriched in temozolomide-induced hypermutated GBMs at recurrence We leveraged IDH wild-type glioblastomas, derivative neurospheres, and single-cell gene expression profiles to define three tumor-intrinsic transcriptional subtypes designated as proneural, mesenchymal, and classical. Transcriptomic subtype multiplicity correlated with increased intratumoral heterogeneity and presence of tumor microenvironment. In silico cell sorting identified macrophages/microglia, CD4+ T lymphocytes, and neutrophils in the glioma microenvironment. NF1 deficiency resulted in increased tumor-associated macrophages/microglia infiltration. Longitudinal transcriptome analysis showed that expression subtype is retained in 55% of cases. Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence. Hypermutation at diagnosis or at recurrence associated with CD8+ T cell enrichment. Frequency of M2 macrophages detection associated with short-term relapse after radiation therapy. We leveraged IDH wild-type glioblastomas, derivative neurospheres, and single-cell gene expression profiles to define three tumor-intrinsic transcriptional subtypes designated as proneural, mesenchymal, and classical. Transcriptomic subtype multiplicity correlated with increased intratumoral heterogeneity and presence of tumor microenvironment. In silico cell sorting identified macrophages/microglia, CD4+ T lymphocytes, and neutrophils in the glioma microenvironment. NF1 deficiency resulted in increased tumor-associated macrophages/microglia infiltration. Longitudinal transcriptome analysis showed that expression subtype is retained in 55% of cases. Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence. Hypermutation at diagnosis or at recurrence associated with CD8+ T cell enrichment. Frequency of M2 macrophages detection associated with short-term relapse after radiation therapy. Glioblastoma expression subtypes have been related to genomic abnormalities, treatment response, and differences in tumor microenvironment. We defined tumor-intrinsic gene expression subtypes, which establishes a role for the tumor immune environment in shaping the tumor cell transcriptome. Notably, NF1 inactivation resulted in chemoattraction of macrophages/microglia. Comparison of matching primary and recurrent gliomas elucidated treatment-induced phenotypic tumor evolution, including expression subtype switching in nearly half of our cohort, as well as associations between microenvironmental components and treatment response. Characterization of the evolving glioblastoma transcriptome and tumor microenvironment aids in designing more effective immunotherapy trials. Our study provides a comprehensive transcriptional and cellular landscape of IDH wild-type glioblastoma during treatment-modulated tumor evolution. All expression datasets are accessible through http://recur.bioinfo.cnio.es/. The intrinsic capacity of glioblastoma (GBM) tumor cells to infiltrate normal brain impedes surgical eradication and predictably results in high rates of early recurrence. To better understand determinants of GBM tumor evolution and treatment resistance, The Cancer Genome Atlas Consortium (TCGA) performed high-dimensional profiling and molecular classification of nearly 600 GBM tumors (Brennan et al., 2013Brennan C.W. Verhaak R.G. McKenna A. Campos B. Noushmehr H. Salama S.R. Zheng S. Chakravarty D. Sanborn J.Z. Berman S.H. et al.The somatic genomic landscape of glioblastoma.Cell. 2013; 155: 462-477Abstract Full Text Full Text PDF PubMed Scopus (3081) Google Scholar, Cancer Genome Atlas Research, Network, 2008Cancer Genome Atlas Research, NetworkComprehensive genomic characterization defines human glioblastoma genes and core pathways.Nature. 2008; 455: 1061-1068Crossref PubMed Scopus (5824) Google Scholar, Ceccarelli et al., 2016Ceccarelli M. Barthel F.P. Malta T.M. Sabedot T.S. Salama S.R. Murray B.A. Morozova O. Newton Y. Radenbaugh A. Pagnotta S.M. et al.Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.Cell. 2016; 164: 550-563Abstract Full Text Full Text PDF PubMed Scopus (1237) Google Scholar, Noushmehr et al., 2010Noushmehr H. Weisenberger D.J. Diefes K. Phillips H.S. Pujara K. Berman B.P. Pan F. Pelloski C.E. Sulman E.P. Bhat K.P. et al.Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma.Cancer Cell. 2010; 17: 510-522Abstract Full Text Full Text PDF PubMed Scopus (1782) Google Scholar, Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar). TCGA identified common mutations in genes such as TP53, EGFR, IDH1, and PTEN, as well as the frequent and concurrent presence of abnormalities in the p53, RB, and receptor tyrosine kinase pathways. Unsupervised transcriptome analysis also revealed four clusters, referred to as classical (CL), mesenchymal (MES), neural (NE), and proneural (PN), which were tightly associated with genomic abnormalities (Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar). The PN and MES expression subtypes have been most consistently described in the literature with PN relating to a more favorable outcome and MES relating to poor survival (Huse et al., 2011Huse J.T. Phillips H.S. Brennan C.W. Molecular subclassification of diffuse gliomas: seeing order in the chaos.Glia. 2011; 59: 1190-1199Crossref PubMed Scopus (179) Google Scholar, Phillips et al., 2006Phillips H.S. Kharbanda S. Chen R. Forrest W.F. Soriano R.H. Wu T.D. Misra A. Nigro J.M. Colman H. Soroceanu L. et al.Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.Cancer Cell. 2006; 9: 157-173Abstract Full Text Full Text PDF PubMed Scopus (2319) Google Scholar, Zheng et al., 2012Zheng S. Chheda M.G. Verhaak R.G. Studying a complex tumor: potential and pitfalls.Cancer J. 2012; 18: 107-114Crossref PubMed Scopus (23) Google Scholar), but these findings were affected by the relatively favorable outcome of IDH mutant GBMs which are consistently classified as PN (Noushmehr et al., 2010Noushmehr H. Weisenberger D.J. Diefes K. Phillips H.S. Pujara K. Berman B.P. Pan F. Pelloski C.E. Sulman E.P. Bhat K.P. et al.Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma.Cancer Cell. 2010; 17: 510-522Abstract Full Text Full Text PDF PubMed Scopus (1782) Google Scholar, Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar). PN-to-MES switching upon disease recurrence has been implicated in treatment resistance in GBM relapse (Bao et al., 2006Bao S. Wu Q. McLendon R.E. Hao Y. Shi Q. Hjelmeland A.B. Dewhirst M.W. Bigner D.D. Rich J.N. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response.Nature. 2006; 444: 756-760Crossref PubMed Scopus (4877) Google Scholar, Bhat et al., 2013Bhat K.P. Balasubramaniyan V. Vaillant B. Ezhilarasan R. Hummelink K. Hollingsworth F. Wani K. Heathcock L. James J.D. Goodman L.D. et al.Mesenchymal differentiation mediated by NF-kappaB promotes radiation resistance in glioblastoma.Cancer Cell. 2013; 24: 331-346Abstract Full Text Full Text PDF PubMed Scopus (671) Google Scholar, Ozawa et al., 2014Ozawa T. Riester M. Cheng Y. Huse J.T. Squatrito M. Helmy K. Charles N. Michor F. Holland E.C. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma.Cancer Cell. 2014; 26: 288-300Abstract Full Text Full Text PDF PubMed Scopus (260) Google Scholar, Phillips et al., 2006Phillips H.S. Kharbanda S. Chen R. Forrest W.F. Soriano R.H. Wu T.D. Misra A. Nigro J.M. Colman H. Soroceanu L. et al.Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.Cancer Cell. 2006; 9: 157-173Abstract Full Text Full Text PDF PubMed Scopus (2319) Google Scholar), but the frequency and relevance of this phenomenon in glioma progression remains ambiguous. GBM tumor cells, along with the tumor microenvironment, together create a complex milieu that ultimately promotes tumor cell transcriptomic adaptability and disease progression (Olar and Aldape, 2014Olar A. Aldape K.D. Using the molecular classification of glioblastoma to inform personalized treatment.J. Pathol. 2014; 232: 165-177Crossref PubMed Scopus (182) Google Scholar). The presence of tumor-associated stroma results in an MES tumor gene signature and poor prognosis in colon cancers (Isella et al., 2015Isella C. Terrasi A. Bellomo S.E. Petti C. Galatola G. Muratore A. Mellano A. Senetta R. Cassenti A. Sonetto C. et al.Stromal contribution to the colorectal cancer transcriptome.Nat. Genet. 2015; 47: 312-319Crossref PubMed Scopus (433) Google Scholar). Furthermore, the association between MES gene expression signature and reduced tumor purity has been identified as a common theme across cancers (Martinez et al., 2015Martinez E. Yoshihara K. Kim H. Mills G.M. Trevino V. Verhaak R.G. Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects.Oncogene. 2015; 34: 2732-2740Crossref PubMed Scopus (39) Google Scholar, Yoshihara et al., 2013Yoshihara K. Shahmoradgoli M. Martinez E. Vegesna R. Kim H. Torres-Garcia W. Trevino V. Shen H. Laird P.W. Levine D.A. et al.Inferring tumour purity and stromal and immune cell admixture from expression data.Nat. Commun. 2013; 4: 2612Crossref PubMed Scopus (3587) Google Scholar). Tumor-associated macrophages, including either those of peripheral origin or representing brain-intrinsic microglia in glioma (Gabrusiewicz et al., 2016Gabrusiewicz K. Rodriguez B. Wei J. Hashimoto Y. Healy L.M. Maiti S.N. Thomas G. Zhou S. Wang Q. Elakkad A. et al.Glioblastoma-infiltrated innate immune cells resemble M0 macrophage phenotype.JCI Insight. 2016; 1https://doi.org/10.1172/jci.insight.85841Crossref PubMed Scopus (269) Google Scholar, Hambardzumyan et al., 2015Hambardzumyan D. Gutmann D.H. Kettenmann H. The role of microglia and macrophages in glioma maintenance and progression.Nat. Neurosci. 2015; 19: 20-27Crossref Scopus (845) Google Scholar), have been proposed as regulators of PN-to-MES transition through nuclear factor κB activation (Bhat et al., 2013Bhat K.P. Balasubramaniyan V. Vaillant B. Ezhilarasan R. Hummelink K. Hollingsworth F. Wani K. Heathcock L. James J.D. Goodman L.D. et al.Mesenchymal differentiation mediated by NF-kappaB promotes radiation resistance in glioblastoma.Cancer Cell. 2013; 24: 331-346Abstract Full Text Full Text PDF PubMed Scopus (671) Google Scholar) and may provide growth factor-mediated proliferative signals which could be therapeutically targeted (Patel et al., 2014Patel A.P. Tirosh I. Trombetta J.J. Shalek A.K. Gillespie S.M. Wakimoto H. Cahill D.P. Nahed B.V. Curry W.T. Martuza R.L. et al.Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.Science. 2014; 344: 1396-1401Crossref PubMed Scopus (2513) Google Scholar, Pyonteck et al., 2013Pyonteck S.M. Akkari L. Schuhmacher A.J. Bowman R.L. Sevenich L. Quail D.F. Olson O.C. Quick M.L. Huse J.T. Teijeiro V. et al.CSF-1R inhibition alters macrophage polarization and blocks glioma progression.Nat. Med. 2013; 19: 1264-1272Crossref PubMed Scopus (1401) Google Scholar, Yan et al., 2015Yan J. Kong L.Y. Hu J. Gabrusiewicz K. Dibra D. Xia X. Heimberger A.B. Li S. FGL2 as a multimodality regulator of tumor-mediated immune suppression and therapeutic target in gliomas.J. Natl. Cancer Inst. 2015; 107https://doi.org/10.1093/jnci/djv137Crossref Scopus (69) Google Scholar). Here, we explored the properties of the microenvironment in different GBM gene expression subtypes before and after therapeutic intervention. We set out to elucidate the tumor-intrinsic and tumor microenvironment-independent transcriptional heterogeneity of GBMs by identifying genes uniquely expressed by glioma cells and not by tumor-associated host cells. We performed RNA sequencing of 133 single cells isolated from three GBMs (Lee et al., 2017Lee J.K. Wang J. Sa J.K. Ladewig E. Lee H.O. Lee I.H. Kang H.J. Rosenbloom D.S. Camara P.G. Liu Z. et al.Spatiotemporal genomic architecture informs precision oncology in glioblastoma.Nat. Genet. 2017; 49: 594-599Crossref PubMed Scopus (156) Google Scholar), and compiled transcriptomes of an additional 672 single cells isolated from five GBMs (Patel et al., 2014Patel A.P. Tirosh I. Trombetta J.J. Shalek A.K. Gillespie S.M. Wakimoto H. Cahill D.P. Nahed B.V. Curry W.T. Martuza R.L. et al.Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.Science. 2014; 344: 1396-1401Crossref PubMed Scopus (2513) Google Scholar). A set of 596 out of the 805 single cells passed quality control procedures and were determined to be single glioma cells (SGCs) (Figure S1A). We observed that 14,656 of 22,870 unique genes were expressed in at least 5% of the 596 SGCs and were considered candidate bona fide glioma genes (BFGs) (Figure S1B). To filter genes that were expressed by both GBM cells and the tumor microenvironment, we collected a cohort of 37 GBMs from which we derived glioma sphere-forming cell cultures (GSCs). Following RNA sequencing of this set, we performed pairwise gene expression comparison and identified 3,099 genes significantly overexpressed in GBM compared with their derivative GSCs (false discovery rate [FDR]-adjusted t test p value <0.01). Removing these candidate microenvironment marker genes reduced the BFG list to 13,165 genes (Figure S1C). Finally, we analyzed the RNA sequencing data of 30 cellular tumors and 19 matching leading edges of eight GBM surgery specimens from the Ivy Glioblastoma Atlas Project (Ivy GAP, http://glioblastoma.alleninstitute.org/). Cellular tumors are considered near 100% tumor cells versus no more than 10% tumor cells in the leading edge. We identified 5,978 genes significantly greater expressed in leading edge compared with matching cellular tumor (FDR-adjusted t test p values <0.01), resulting in discarding 1,636 genes from the BFG list (Figure S1D). Of the 11,529 genes on the resulting BFG list, 7,425 genes are represented on the Affymetrix U133A microarray used to profile the TCGA GBM cohort (Figures 1A and S1E; Table S1). GBMs with IDH mutations (IDHmut) represent 5% of the cases, and have distinct biological properties and confer favorable clinical outcomes compared with IDH wild-type (IDH-WT) GBMs (Brennan et al., 2013Brennan C.W. Verhaak R.G. McKenna A. Campos B. Noushmehr H. Salama S.R. Zheng S. Chakravarty D. Sanborn J.Z. Berman S.H. et al.The somatic genomic landscape of glioblastoma.Cell. 2013; 155: 462-477Abstract Full Text Full Text PDF PubMed Scopus (3081) Google Scholar, Cancer Genome Atlas Research Network et al., 2015Brat D.J. Verhaak R.G. Aldape K.D. Yung W.K. Salama S.R. Cooper L.A. Rheinbay E. Miller C.R. Vitucci M. Morozova O. et al.Cancer Genome Atlas Research NetworkComprehensive, integrative genomic analysis of diffuse lower-grade gliomas.N. Engl. J. Med. 2015; 372: 2481-2498Crossref PubMed Scopus (1993) Google Scholar, Ceccarelli et al., 2016Ceccarelli M. Barthel F.P. Malta T.M. Sabedot T.S. Salama S.R. Murray B.A. Morozova O. Newton Y. Radenbaugh A. Pagnotta S.M. et al.Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.Cell. 2016; 164: 550-563Abstract Full Text Full Text PDF PubMed Scopus (1237) Google Scholar, Noushmehr et al., 2010Noushmehr H. Weisenberger D.J. Diefes K. Phillips H.S. Pujara K. Berman B.P. Pan F. Pelloski C.E. Sulman E.P. Bhat K.P. et al.Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma.Cancer Cell. 2010; 17: 510-522Abstract Full Text Full Text PDF PubMed Scopus (1782) Google Scholar). Using the filtered BFG/U133A set, we performed consensus non-negative matrix factorization clustering to identify three distinct subtypes comprising 369 IDH-WT GBMs (Figures 1B and 1C; Table S1). When comparing the clustering result with the previously defined PN, NE, CL, and MES classification (Brennan et al., 2013Brennan C.W. Verhaak R.G. McKenna A. Campos B. Noushmehr H. Salama S.R. Zheng S. Chakravarty D. Sanborn J.Z. Berman S.H. et al.The somatic genomic landscape of glioblastoma.Cell. 2013; 155: 462-477Abstract Full Text Full Text PDF PubMed Scopus (3081) Google Scholar, Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar), the three subtypes were strongly enriched for MES, PN, and CL GBMs, respectively (Figure S1F). Consequently, we designated the groups as MES, PN, and CL. None of the three subtypes were enriched for the NE subtype, suggesting the NE phenotype is non-tumor specific. The NE subtype has previously been related to the tumor margin where increased normal NE tissue is likely to be detected (Gill et al., 2014Gill B.J. Pisapia D.J. Malone H.R. Goldstein H. Lei L. Sonabend A. Yun J. Samanamud J. Sims J.S. Banu M. et al.MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma.Proc. Natl. Acad. Sci. USA. 2014; 111: 12550-12555Crossref PubMed Scopus (165) Google Scholar, Sturm et al., 2012Sturm D. Witt H. Hovestadt V. Khuong-Quang D.A. Jones D.T. Konermann C. Pfaff E. Tonjes M. Sill M. Bender S. et al.Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma.Cancer Cell. 2012; 22: 425-437Abstract Full Text Full Text PDF PubMed Scopus (1291) Google Scholar), and such contamination might explain why the NE subtype was the only subtype to lack characteristic gene abnormalities (Brennan et al., 2013Brennan C.W. Verhaak R.G. McKenna A. Campos B. Noushmehr H. Salama S.R. Zheng S. Chakravarty D. Sanborn J.Z. Berman S.H. et al.The somatic genomic landscape of glioblastoma.Cell. 2013; 155: 462-477Abstract Full Text Full Text PDF PubMed Scopus (3081) Google Scholar, Li et al., 2013Li J. Lu Y. Akbani R. Ju Z. Roebuck P.L. Liu W. Yang J.Y. Broom B.M. Verhaak R.G. Kane D.W. et al.TCPA: a resource for cancer functional proteomics data.Nat. Methods. 2013; 10: 1046-1047Crossref PubMed Scopus (314) Google Scholar). To be able to classify external GBM samples, we implemented a single sample gene set enrichment analysis (ssGSEA)-based equivalent distribution resampling classification strategy using 50-gene signatures for each subtype (Figure 1D and Table S1) to assign each sample three empirical classification p values by which we determined the significantly activated subtype(s). The overlap between 50-gene signatures and the previously reported transcriptional subtype signatures (Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar) ranged from 42% to 54% (Figure S1G). We prepared an R-library to enable others to evaluate our approach (Method S1). To assess the robustness of our GBM subtype classification method, we compared cluster assignments of 144 TCGA GBM samples profiled using both RNA sequencing and Affymetrix U133A microarrays, and the assessment revealed 93% concordance (Figure S1H; Table S2). The 93% concordance was an improvement over the 77% subtype concordance determined using previously reported methods (Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar). In fact, classifying TCGA GBMs using the updated 50-gene signatures resulted in classification concordances ranging from 93% to 100% when comparing across different batches, different compositions of IDH-WT/IDHmut cases, and different RNA sequencing expression measurements (Figures S1I–S1K). Notably, we found high classification stability in small sample sizes, such as 85% concordance in cohorts of ten randomly selected samples (Figure S1L). We also evaluated the distribution of somatic variants across the three molecular subtypes (Figure 1E) and confirmed the strong associations between subtypes and genomic abnormalities in previously reported driver genes (Brennan et al., 2013Brennan C.W. Verhaak R.G. McKenna A. Campos B. Noushmehr H. Salama S.R. Zheng S. Chakravarty D. Sanborn J.Z. Berman S.H. et al.The somatic genomic landscape of glioblastoma.Cell. 2013; 155: 462-477Abstract Full Text Full Text PDF PubMed Scopus (3081) Google Scholar, Verhaak et al., 2010Verhaak R.G. Hoadley K.A. Purdom E. Wang V. Qi Y. Wilkerson M.D. Miller C.R. Ding L. Golub T. Mesirov J.P. et al.Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell. 2010; 17: 98-110Abstract Full Text Full Text PDF PubMed Scopus (5017) Google Scholar). We observed that 29/369 (8%) TCGA samples showed significant enrichment of multiple ssGSEA scores (empirical classification p value <0.05), suggesting that these cases activate more than one transcriptional subtype (Figure 2A). To quantify such transcriptional heterogeneity, a score ranging from 0 to 1 was defined to quantitatively evaluate the simplicity of subtype activation based on order statistics of ssGSEA score. Samples with high simplicity scores activated a single subtype and those with lowest simplicity scores activated multiple subtypes. All multi-subtype TCGA samples scored simplicities of less than 0.05 (Figure 2A). To determine whether transcriptional heterogeneity associated with genomic intratumoral heterogeneity, we correlated simplicity scores, total mutation rates, and subclonal mutation rates. Included in the analysis were 224 TCGA GBMs with available whole-exome sequencing data (Kim et al., 2015Kim H. Zheng S. Amini S.S. Virk S.M. Mikkelsen T. Brat D.J. Grimsby J. Sougnez C. Muller F. Hu J. et al.Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution.Genome Res. 2015; 25: 316-327Crossref PubMed Scopus (246) Google Scholar), and ABSOLUTE (Carter et al., 2012Carter S.L. Cibulskis K. Helman E. McKenna A. Shen H. Zack T. Laird P.W. Onofrio R.C. Winckler W. Weir B.A. et al.Absolute quantification of somatic DNA alterations in human cancer.Nat. Biotechnol. 2012; 30: 413-421Crossref PubMed Scopus (1265) Google Scholar) determined high tumor purity (>0.7) to equalize the mutation detection sensitivity (Aran et al., 2015Aran D. Sirota M. Butte A.J. Systematic pan-cancer analysis of tumour purity.Nat. Commun. 2015; 6: 8971Crossref PubMed Scopus (622) Google Scholar). Although not significant (Wilcoxon rank test p value = 0.30), the total mutation rate was less in the bottom 50% of samples with lowest simplicity scores versus the top 50% with highest simplicity scores. Meanwhile, the subclonal mutation rate and fraction was significantly higher (Wilcoxon rank test p value = 0.03 and 0.02, respectively) in samples with lowest simplicity scores (Figure 2B and Table S3), suggesting that increased intratumoral heterogeneity associates with increased transcriptional heterogeneity. We compared outcomes among the three transcriptional groups and observed no significant differences (Figure S2A). However, upon restricting the analysis to those samples with high simplicity scores (>0.99, n = 74, top 20% cases), we discovered median survival of 11.5, 14.7, and 17.0 months in MES, CL, and PN cases, respectively, which revealed a significant survival difference between MES and non-MES cases (log rank test, p value = 0.03) (Figure 2C and Table S4). Consistent with this trend, greater simplicity scores correlated with relatively favorable outcome within the PN subtype, while outcome remained unchanged within the CL subtype and the MES subtype (Figures S2B–S2D). Single GBM cell RNA sequencing recently suggested that GBMs are comprised of a mixture of tumor cells with variable GBM subtype footprints (Patel et al., 2014Patel A.P. Tirosh I. Trombetta J.J. Shalek A.K. Gillespie S.M. Wakimoto H. Cahill D.P. Nahed B.V. Curry W.T. Martuza R.L. et al.Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.Science. 2014; 344: 1396-1401Crossref PubMed Scopus (2513) Google Scholar). Within this dataset, we compiled the RNA-sequenced transcriptomes of 501 SGCs in addition to the bulk tumor derived from five primary GBMs (Table S4) to investigate the intratumoral transcriptional heterogeneity at the SGC level. In four of five cases (MGH26, MGH28, MGH29, and MGH30), the bulk tumor samples were classified in the same primary subtype as the majority of their single cells (Figure 2D). Our analysis suggests that the heterogeneity observed at the single-cell level is captured in the expression profile of the bulk tumor. Despite restricting the cluster analysis to genes exclusively expressed by GBM cells, we found that tumor purity predictions based on ABSOLUTE were significantly reduced in GBM classified as MES (Figure 3A). This was corroborated by gene expression-based predictions of tumor purity using the ESTIMATE method (Student’s t test p value <2.2 × 10−16; Figure 3B) (Yoshihara et al., 2013Yoshihara K. Shahmoradgoli M. Martinez E. Vegesna R. Kim H. Torres-Garcia W. Trevino V. Shen H. Laird P.W. Levine D.A. et al.Inferring tumour purity and stromal and immune cell admixture from expression data.Nat. Commun. 2013; 4: 2612Crossref PubMed Scopus (3587) Google Scholar). The ESTIMATE method has been optimized to quantify tumor-associated fibroblasts and immune cells (Yoshihara et al., 2013Yoshihara K. Shahmoradgoli M. Martinez E. Vegesna R. Kim H. Torres-Garcia W. Trevino V. Shen H. Laird P.W. Levine D.A. et al.Inferring tumour purity and stromal and immune cell admixture from expression data.Nat. Commun. 2013; 4: 2612Crossref PubMed Scopus (3587) Google Scholar), and the convergence of decreased ABSOLUTE and decreased ESTIMATE tumor purity confirms previous indications of increased presence of macrophages/microglia and neuroglial cells in MES GBM (Bao et al., 2006Bao S. Wu Q. McLendon R.E. Hao Y. Shi Q. Hjelmeland A.B. Dewhirst M.W. Bigner D.D. Rich J.N. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response.Nature. 2006; 444: 756-760Crossref PubMed Scopus (4877) Google Scholar, Engler et al., 2012Engler J.R. Robinson A.E. Smirnov I. Hodgson J.G. Berger M.S. Gupta N. James C.D. Molinaro A. Phillips J.J. Increased microglia/macrophage gene expression in a subset of adult and pediatric astrocytomas.PLoS One. 2012; 7: e43339Crossref PubMed Scopus (113) Google Scholar, Gabrusiewicz et al., 2016Gabrusiewicz K. Rodriguez B. Wei J. Hashimoto Y. Healy L.M. Maiti S.N. Thomas G. Zhou S. Wang Q. Elakkad A. et al.Glioblastoma-infiltrated innate immune cells resemble M0 macrophage phenotype.JCI Insight. 2016; 1https://doi.org/10.1172/jci.insight.85841Crossref PubMed Scopus (269) Google Scholar, Ye et al., 2012Ye X.Z. Xu S.L. Xin Y.H. Yu S.C. Ping Y.F. Chen L. Xiao H.L. Wang B. Yi L. Wang Q.L. et al.Tumor-associated microglia/macrophages enhance the invasion of glioma stem-like cells via TGF-beta1 signaling pathway.J. Immunol. 2012; 189: 444-453Crossref PubMed Scopus (324) Google Scholar). The mean simplicity score of samples classified as MES was 0.48, which was significantly less than mean simplicity scores of samples classified as PN (Wilcoxon rank test p value <0.003) and CL (Wilcoxon rank test p value <1.13 × 10−5), confirming increased transcriptional heterogeneity. Tumor-associated macrophages are a major source of tumor-associated non-neoplastic cells. In the brain, macrophages can be categorized as microglia, the resident macrophages in the CNS, and circulation-derived monocytes. Comparison of transcriptional levels of the macrophages/" @default.
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