A simplified integrated molecular and immunohistochemistry-based algorithm allows high accuracy prediction of glioblastoma transcriptional subtypes


Glioblastomas (GBM) can be classified into three major transcriptional subgroups (proneural, mesenchymal, classical), underlying different molecular alterations, prognosis, and response to therapy. However, transcriptional analysis is not routinely feasible and assessment of a simplified method for glioblastoma subclassification is required. We propose an integrated molecular and immunohistochemical approach aimed at identifying GBM subtypes in routine paraffin-embedded material. RNA-sequencing analysis was performed on representative samples (n = 51) by means of a “glioblastoma transcriptional subtypes (GliTS) redux” custom gene signature including a restricted number (n = 90) of upregulated genes validated on the TCGA dataset. With this dataset, immunohistochemical profiles, based on expression of a restricted panel of gene classifiers, were integrated by a machine-learning approach to generate a GliTS based on protein quantification that allowed an efficient GliTS assignment when applied to an extended cohort (n = 197). GliTS redux maintained high levels of correspondence with the original GliTS classification using the TCGA dataset. The machine-learning approach designed an immunohistochemical (IHC)-based classification, whose concordance was 79.5% with the transcriptional- based classification, and reached 90% for the mesenchymal subgroup. Distribution and survival of GliTS were in line with reported data, with the mesenchymal subgroup given the worst prognosis. Notably, the algorithm allowed the identification of cases with comparable probability to be assigned to different GliTS, thus falling within overlapping regions and reflecting an extreme heterogeneous phenotype that mirrors the underlying genetic and biological tumor heterogeneity. Indeed, while mesenchymal and classical subgroups were well segregated, the proneural types frequently showed a mixed proneural/classical phenotype, predicted as proneural by the algorithm, but with comparable probability of being assigned to the classical subtype. These cases, characterized by concomitant high expression of EGFR and proneural biomarkers, showed lower survival. Collectively, these data indicate that a restricted panel of highly sensitive immunohistochemical markers can efficiently predict GliTS with high accuracy and significant association with different clinical outcomes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Transcriptional classification based on “GliTS redux” gene signature.
Fig. 2: GliTS transcriptional status prediction based on immunohistochemical profile.
Fig. 3: GliTS and correlation with histopathological features.
Fig. 4: GliTS and clinical correlations.


  1. 1.

    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131:803–20.

    Article  Google Scholar 

  2. 2.

    Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L, et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell. 2018;33:152.

    CAS  Article  Google Scholar 

  3. 3.

    Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17:98–110.

    CAS  Article  Google Scholar 

  4. 4.

    Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 2006;9:157–73.

    CAS  Article  Google Scholar 

  5. 5.

    Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17:510–22.

    CAS  Article  Google Scholar 

  6. 6.

    Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360:765–73.

    CAS  Article  Google Scholar 

  7. 7.

    Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR, et al. The somatic genomic landscape of glioblastoma. Cell. 2013;155:462–77.

    CAS  Article  Google Scholar 

  8. 8.

    Olar A, Aldape KD. Using the molecular classification of glioblastoma to inform personalized treatment. J Pathol. 2014;232:165–77.

    Article  Google Scholar 

  9. 9.

    Cominelli M, Grisanti S, Mazzoleni S, Branca C, Buttolo L, Furlan D, et al. EGFR amplified and overexpressing glioblastomas and association with better response to adjuvant metronomic temozolomide. J Natl Cancer Inst. 2015;107:djv041.

    Article  Google Scholar 

  10. 10.

    Hovinga KE, McCrea HJ, Brennan C, Huse J, Zheng J, Esquenazi Y, et al. EGFR amplification and classical subtype are associated with a poor response to bevacizumab in recurrent glioblastoma. J Neurooncol. 2019;142:337–45.

    CAS  Article  Google Scholar 

  11. 11.

    De Bacco F, D’Ambrosio A, Casanova E, Orzan F, Neggia R, Albano R, et al. MET inhibition overcomes radiation resistance of glioblastoma stem-like cells. EMBO Mol Med. 2016;8:550–68.

    Article  Google Scholar 

  12. 12.

    Sandmann T, Bourgon R, Garcia J, Li C, Cloughesy T, Chinot OL, et al. Patients with proneural glioblastoma may derive overall survival benefit from the addition of bevacizumab to first-line radiotherapy and temozolomide: retrospective analysis of the AVAglio trial. J Clin Oncol. 2015;33:2735–44.

    CAS  Article  Google Scholar 

  13. 13.

    Le Mercier M, Hastir D, Moles Lopez X, De Neve N, Maris C, Trepant AL, et al. A simplified approach for the molecular classification of glioblastomas. PLoS ONE. 2012;7:e45475.

    Article  Google Scholar 

  14. 14.

    Conroy S, Kruyt FA, Joseph JV, Balasubramaniyan V, Bhat KP, Wagemakers M, et al. Subclassification of newly diagnosed glioblastomas through an immunohistochemical approach. PLoS ONE. 2014;9:e115687.

    Article  Google Scholar 

  15. 15.

    Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38:e178.

    Article  Google Scholar 

  16. 16.

    Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    CAS  Article  Google Scholar 

  17. 17.

    Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012;22:1760–74.

    CAS  Article  Google Scholar 

  18. 18.

    Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J Classif. 2014;31:274–95.

    Article  Google Scholar 

  19. 19.

    Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  20. 20.

    Bowman RL, Wang Q, Carro A, Verhaak RG, Squatrito M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol. 2017;19:139–41.

    CAS  Article  Google Scholar 

  21. 21.

    Park NI, Guilhamon P, Desai K, McAdam RF, Langille E, O’Connor M, et al. ASCL1 reorganizes chromatin to direct neuronal fate and suppress tumorigenicity of glioblastoma stem cells. Cell Stem Cell. 2017;21:411.

    CAS  Article  Google Scholar 

  22. 22.

    Narayanan A, Gagliardi F, Gallotti AL, Mazzoleni S, Cominelli M, Fagnocchi L, et al. The proneural gene ASCL1 governs the transcriptional subgroup affiliation in glioblastoma stem cells by directly repressing the mesenchymal gene NDRG1. Cell Death Differ. 2018;26:1813–31.

    Article  Google Scholar 

  23. 23.

    De Bacco F, Casanova E, Medico E, Pellegatta S, Orzan F, Albano R, et al. The MET oncogene is a functional marker of a glioblastoma stem cell subtype. Stem Cells. 2012;72:4537–50.

    Google Scholar 

  24. 24.

    Perry A, Aldape KD, George DH, Burger PC. Small cell astrocytoma: an aggressive variant that is clinicopathologically and genetically distinct from anaplastic oligodendroglioma. Cancer. 2004;101:2318–26.

    Article  Google Scholar 

  25. 25.

    Brennan C, Momota H, Hambardzumyan D, Ozawa T, Tandon A, Pedraza A, et al. Glioblastoma subclasses can be defined by activity among signal transduction pathways and associated genomic alterations. PLoS ONE. 2009;4:e7752.

    Article  Google Scholar 

  26. 26.

    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344:1396–401.

    CAS  Article  Google Scholar 

  27. 27.

    Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell. 2019;178:835–49.e21.

    CAS  Article  Google Scholar 

  28. 28.

    Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  Google Scholar 

  29. 29.

    Isella C, Terrasi A, Bellomo SE, Petti C, Galatola G, Muratore A, et al. Stromal contribution to the colorectal cancer transcriptome. Nat Genet. 2015;47:312–9.

    CAS  Article  Google Scholar 

  30. 30.

    Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA. 2013;110:4009–14.

    CAS  Article  Google Scholar 

  31. 31.

    Orzan F, De Bacco F, Crisafulli G, Pellegatta S, Mussolin B, Siravegna G, et al. Genetic evolution of glioblastoma stem-like cells from primary to recurrent tumor. Stem Cells. 2017;35:2218–28.

    CAS  Article  Google Scholar 

  32. 32.

    Mazzoleni S, Politi LS, Pala M, Cominelli M, Franzin A, Sergi Sergi L, et al. Epidermal growth factor receptor expression identifies functionally and molecularly distinct tumor-initiating cells in human glioblastoma multiforme and is required for gliomagenesis. Cancer Res. 2010;70:7500–13.

    CAS  Article  Google Scholar 

  33. 33.

    Yang W, Xia Y, Cao Y, Zheng Y, Bu W, Zhang L, et al. EGFR-induced and PKCepsilon monoubiquitylation-dependent NF-kappaB activation upregulates PKM2 expression and promotes tumorigenesis. Mol Cell. 2012;48:771–84.

    CAS  Article  Google Scholar 

  34. 34.

    Yamini B. NF-kappaB, mesenchymal differentiation and glioblastoma. Cells. 2018;7:125.

    CAS  Article  Google Scholar 

  35. 35.

    Segerman A, Niklasson M, Haglund C, Bergstrom T, Jarvius M, Xie Y, et al. Clonal variation in drug and radiation response among glioma-initiating cells is linked to proneural-mesenchymal transition. Cell Rep. 2016;17:2994–3009.

    CAS  Article  Google Scholar 

  36. 36.

    Ozawa T, Riester M, Cheng YK, Huse JT, Squatrito M, Helmy K, et al. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma. Cancer Cell. 2014;26:288–300.

    CAS  Article  Google Scholar 

  37. 37.

    De Bacco F, Luraghi P, Medico E, Reato G, Girolami F, Perera T, et al. Induction of MET by ionizing radiation and its role in radioresistance and invasive growth of cancer. J Natl Cancer Inst. 2011;103:645–61.

    Article  Google Scholar 

Download references


We thank Sara Cancelli, Elisabetta Spada, and Elisabetta Sandrini for collecting data and technical support, Gigliola Reato and Daniela Cantarella for technical support with RNAseq profiles, and James Hughes for revising the manuscript. A special thanks goes to Salvatore Grisanti for his suggestions and helpful critical discussions. Work was conducted on behalf of Neuro-Oncology Group, Spedali Civili, University of Brescia.


This work was supported by Italian Ministry of Health (Grant RF-2016-02361014) and “Associazione dedicato A te” to PLP; Italian Association for Cancer Research (AIRC; Investigator Grant no. 19933) and “Comitato per Albi 98” to CB; Italian Association for Cancer Research (AIRC; Investigator Grant IG16823) to RG; Italian Ministry of University (PRIN; projects no. 20 178S4EK9) to SC.

Author information





Conceptual design and critical revision of data: PLP, CB, and RG; bioinformatic analyses: SC, CI, and EM; RNAseq and molecular analysis: FO, CI, FP, and FDB; methodological expertise and performed most of the experiments: FO, FP, MC, and FDB; clinical data revision and sample collection: LT, MB, MMF, PPP, and RL; neuropathology, IHC, FISH, and revision of clinical diagnosis: PLP, FP, MC, DM, and PB; and manuscript preparation and critical revision: PLP, CB, RG, FO, FP, and MC. All authors contributed to data analysis/interpretation and approved the final version of this manuscript.

Corresponding author

Correspondence to Pietro Luigi Poliani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics statement

All procedures was conducted in compliance with Declaration of Helsinki and policies approved by Ethical Board of Spedali Civili of Brescia for retrospective and exclusively observational study on archival material obtained for diagnostic purpose for which patient consent was previously collected. Clinical information were obtained on behalf of Neuro-Oncology Group, Spedali Civili, University of Brescia. Article does not contain any studies with animals.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Orzan, F., Pagani, F., Cominelli, M. et al. A simplified integrated molecular and immunohistochemistry-based algorithm allows high accuracy prediction of glioblastoma transcriptional subtypes. Lab Invest (2020). https://doi.org/10.1038/s41374-020-0437-0

Download citation