Identification of new Genetic Clusters in Glioblastoma Multiforme: EGFR status and ADD3 losses influence prognosis
Original author: L. Navarro et al. Cell, vol 9, 2429 (2020) (DOI: 10.3390/cells9112429)
Summary
Sr. scientist – Omics
June 24, 2022
Overview
Glioblastoma (GB) multiforme, IDH wild-type (GB-IDHwt) is the most frequent brain tumor with poor survival rate. Glioblastoma with mutant IDH variant shows good prognosis. The highly genetic heterogeneity is responsible for absence of effective treatment of GB. Among the genetic features, amplification of both wild type and mutant EGFR is a signature event of different types of cancers including glioblastomas. EGFR amplification is often associated with a deletion of exon 2-7 of the EGFR gene. This mutant variant is called EGFR variant III. The current manuscript aims to characterize frequent alterations in EGFR via its amplification or the presence of EGFR variant III with associated somatic copy number alterations (SCNA) in primary GB samples. Also, the importance of ADD3 and EGFR variant III as genetic biomarkers is being explored.
Samples
Samples were obtained from 128 surgically treated GB-IDHwt patients prior to any chemotherapy or radiotherapy. GB samples were also accessed from TCGA for further validation of the correlation between SCNA and EGFR amplification.
Results & discussion
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The multiplex ligation-dependent probe amplification (MLPA) method is used to characterize multiple genes at the same time. It is a variation of the multiplex polymerase chain reaction that permits the amplification of multiple targets with only a single primer pair. EGFR amplification was observed in almost 70% of the samples. However, no significant association between EGFR amplification and survival were observed. Also, there was no association between with age and sex.
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On the other hand EGFRvIII shows higher presence in women but no association with age, tumor location, and size.
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High genetic heterogeneity was observed in the samples. EGFR appears as the most altered gene, followed by CDKN2A, TIMP3, MEN1, CDKN2B, MVP, PTEN, MTAP, and ADD3. Among them, only ADD3 showed an association with the overall survival period.
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Depending on the EGFR amplification status, genes MSH6, CDKN2A, MTAP, and JAG1 showed different gain/loss. Also, EGFR variant III samples varied widely from wild type EGFR samples in SCNAs.
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91 samples were considered for the classification algorithm. Among them, 18 were not classified. Remaining 73 samples were classified among three clusters depending on the frequency of alterations.
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Different clusters point to different pathways. Cluster2 showed the highest frequency of SCNAs in CDKN2A, MEN1, EGFR, TIMP3, PTEN, MTAP, MVP, SMARCA4, ADD3, MSH6, JAG1, SPG11 and DOCK8. Cluster2 also showed an abundance of EGFR variant III samples with ADD3 losses. It shows more association with ‘regulation of cell surface adhesion’ and ‘cell-matrix adhesion’ processes. On the other hand, cluster3 shows an association with ‘regulation of cell cycle phase transition’. Cluster1 is the less altered cluster, not affected by EGFR.
Impact of the research
The impact of this manuscript is two-fold. It sets out MLPA as an advantageous methodology for probing multiple genes avoiding the cost of the NGS method. The major impact of this study is, in spite of highly genetic heterogeneity, the cluster analysis has allowed a categorization based on the frequency of alterations. This analysis was also validated on the TCGA data. It highlights the importance of EGFR variant III along with ADD3 SCNAs as prognostic markers in the case of GB. The current study also showed the presence of a group of GB-IDHwt samples where EGFR alterations are not noticed. So the association of EGFR and GB-IDHwt is not linear. It strongly validates the necessity of genetically diagnosed personalized treatments in GB patients.
