Frequency and clinical significance of chromosome 7 and 10 aneuploidies, amplification of the EGFR gene, deletion of PTEN and TP53 genes, and 1p/19q deficiency in a sample of adult patients diagnosed with glioblastoma from Southern Brazil
Abstract:Glioblastoma stands out as the most frequent central nervous system neoplasia, presenting a poor prognosis. The aim of this study was to verify the frequency and clinical significance of the aneuploidy of chromosomes 7 and 10, EGFR amplification, PTEN and TP53 deletions and 1p/19q deficiency in adult patients diagnosed with glioblastoma. The sample consisted of 40 patients treated from November 2011 to March 2015 at two major neurosurgery services from Southern Brazil. Molecular cytogenetic analyses of the tum… Show more
“…Our proposed algorithm is readily scalable to enable meaningful genomic characterization of gliomas. [44][45][46][47] Therapeutically, EGFR signaling and downstream activation of receptor tyrosine kinase/Ras/phosphatidylinositol-3 kinase pathways have received significant attention in multiple cancer types. [48][49][50] Despite the relative success in lung and breast cancer, EGFR tyrosine kinase inhibitors have not shown significant response rates in gliomas to date, 51,52 and antibody-based therapies have been similarly disappointing.…”
Background. Update 3 of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) recognizes amplification of epidermal growth factor receptor (EGFR) as one important aberration in diffuse gliomas (World Health Organization [WHO] grade II/III). While these recommendations endorse testing, a cost-effective, clinically relevant testing paradigm is currently lacking. Here, we use real-world clinical data to propose a financially effective diagnostic test algorithm in the context of new guidelines. Methods. To determine the prevalence, distribution, neuroradiographic features (Visually Accessible REMBRANDT Images [VASARI]), and prognostic relevance of EGFR amplification in lower-grade gliomas, we assembled a consecutive series of diffuse gliomas. For validation we included publicly available data from The Cancer Genome Atlas. For a cost-utility analysis we compared combined EGFR and isocitrate dehydrogenase (IDH) testing, EGFR testing based on IDH results, and no EGFR testing. Results. In n = 71 WHO grade II/III gliomas, we identified EGFR amplification in 28.2%. With one exception, all EGFR amplifications occurred in IDH-wildtype gliomas. Comparison of overall survival showed that EGFR amplification denotes a significantly more aggressive subset of tumors (P < 0.0001, log-rank). The radiologic phenotype in the EGFR-amplified tumors includes diffusion restriction (15%, P = 0.02), >5% tumor contrast enhancement (75%, P = 0.016), and mild (not avid) enhancement (P = 0.016). The proposed testing algorithm reserves EGFR fluorescence in situ hybridization (FISH) testing for IDH-wildtype cases. Implementation would result in ~37.9% cost reduction at our institution, or about $1.3-4 million nationally. Conclusion. EGFR-amplified diffuse gliomas are "glioblastoma-like" in their behavior and may represent undersampled glioblastomas, or subsets of IDH-wildtype diffuse gliomas with inherently aggressive biology. EGFR FISH after IDH testing is a financially effective and clinically relevant test algorithm for routine clinical practice.
Key Points1. EGFR-amplified diffuse gliomas are "glioblastoma-like" in their behavior.
“…Our proposed algorithm is readily scalable to enable meaningful genomic characterization of gliomas. [44][45][46][47] Therapeutically, EGFR signaling and downstream activation of receptor tyrosine kinase/Ras/phosphatidylinositol-3 kinase pathways have received significant attention in multiple cancer types. [48][49][50] Despite the relative success in lung and breast cancer, EGFR tyrosine kinase inhibitors have not shown significant response rates in gliomas to date, 51,52 and antibody-based therapies have been similarly disappointing.…”
Background. Update 3 of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) recognizes amplification of epidermal growth factor receptor (EGFR) as one important aberration in diffuse gliomas (World Health Organization [WHO] grade II/III). While these recommendations endorse testing, a cost-effective, clinically relevant testing paradigm is currently lacking. Here, we use real-world clinical data to propose a financially effective diagnostic test algorithm in the context of new guidelines. Methods. To determine the prevalence, distribution, neuroradiographic features (Visually Accessible REMBRANDT Images [VASARI]), and prognostic relevance of EGFR amplification in lower-grade gliomas, we assembled a consecutive series of diffuse gliomas. For validation we included publicly available data from The Cancer Genome Atlas. For a cost-utility analysis we compared combined EGFR and isocitrate dehydrogenase (IDH) testing, EGFR testing based on IDH results, and no EGFR testing. Results. In n = 71 WHO grade II/III gliomas, we identified EGFR amplification in 28.2%. With one exception, all EGFR amplifications occurred in IDH-wildtype gliomas. Comparison of overall survival showed that EGFR amplification denotes a significantly more aggressive subset of tumors (P < 0.0001, log-rank). The radiologic phenotype in the EGFR-amplified tumors includes diffusion restriction (15%, P = 0.02), >5% tumor contrast enhancement (75%, P = 0.016), and mild (not avid) enhancement (P = 0.016). The proposed testing algorithm reserves EGFR fluorescence in situ hybridization (FISH) testing for IDH-wildtype cases. Implementation would result in ~37.9% cost reduction at our institution, or about $1.3-4 million nationally. Conclusion. EGFR-amplified diffuse gliomas are "glioblastoma-like" in their behavior and may represent undersampled glioblastomas, or subsets of IDH-wildtype diffuse gliomas with inherently aggressive biology. EGFR FISH after IDH testing is a financially effective and clinically relevant test algorithm for routine clinical practice.
Key Points1. EGFR-amplified diffuse gliomas are "glioblastoma-like" in their behavior.
“…So many central nervous system tumors were named according to molecular parameters and histopathologic diagnosis, especially gliomas, ependymomas and medulloblastomas in the 2016 revision of the WHO classification (Zhang et al, 2019b). As we know, some molecular markers, such as MGMT (O6-methylguanine DNA methyltransferase) (Binabaj et al, 2018), isocitrate dehydrogenase (IDH) (Kwon et al, 2019), epidermal growth factor receptor (EGFR) (Chistiakov, Chekhonin & Chekhonin, 2017) and phosphatase and tensin homolog (PTEN) (Koshiyama et al, 2017) that have contributed to personalized therapeutic approaches and targeted anti-glioblastoma therapies have been routinely tested in glioblastoma patients clinically (Yin et al, 2019). However, there are few specific clinical indicators and therapeutic targets for LGGs compared to glioblastoma, so there is an urgent need to elucidate the mechanism of glioma development and progression, which can provide potential treatment targets for LGGs.…”
Background
Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments.
Methods
The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed.
Results
In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.
“…So many central nervous system tumors were named according to molecular parameters and histopathologic diagnosis, especially gliomas,ependymomas,and medulloblastomas in the 2016 revision of the WHO classification (Zhang et al 2019b). As we know,some molecular markers,such as MGMT(O6methylguanine DNA methyltransferase) (Binabaj et al 2018),IDH(isocitrate dehydrogenase) (Kwon et al 2019),EGFR(epidermal growth factor receptor) (Chistiakov et al 2017),and PTEN(phosphatase and tensin homolog) (Koshiyama et al 2017)that have contributed to personalized therapeutic approaches and targeted anti-glioblastoma therapies have been routinely tested in glioblastoma patients clinically (Yin et al 2019). However there are few specific clinical indicators and therapeutic targets for LGGs compared to glioblastoma,so it is urgent to elucidate the mechanism of glioma development and progression,which can provide potential treatment targets for LGGs.…”
Background Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lowergrade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. Methods The RNA-seq data for LGG was downloaded from the UCSC Xena and Chinese Glioma Genome Atlas (CGGA). By robust likelihood-based survival model, LASSO regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender, and pathologic grade. The prognostic signature was validated in CGGA dataset. Finally, Weighted Gene Co-expression Network Analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, timedependent ROC curves of our model and other models are constructed. Results In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio[HR]=0.198, 95%CI= 0.120-0.325) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore,the model was validated in CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3-and 5-year survival of LGG patients compared to other models and may be promising independent biomarker of LGG.
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