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Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

ABSTRACT: Background:Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. Methods:We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. Results:We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. Conclusions:Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

SUBMITTER: Kersch CN

PROVIDER: S-EPMC7462280 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

Kersch Cymon N CN Claunch Cheryl J CJ Ambady Prakash P Bucher Elmar E Schwartz Daniel L DL Barajas Ramon F RF Iliff Jeffrey J JJ Risom Tyler T Heiser Laura L Muldoon Leslie L LL Korkola James E JE Gray Joe W JW Neuwelt Edward A EA

Neuro-oncology advances 20200101 1

<h4>Background</h4>Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of i ...[more]

PMID: 32904984

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Project description:Despite huge investments and major efforts to develop remedies for Alzheimer's disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1-3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Project description:This SuperSeries is composed of the SubSeries listed below. BACKGROUND: Gliomas evade current therapies through primary and acquired resistance and the effect of temozolomide is mainly restricted to the subgroup of methylguanin-O6-methyltransferase promoter (MGMT) hypermethylated tumors. Further resistance markers and pathways against chemotherapy and radiotherapy are largely unknown and would help for better stratification. METHODS: The diagnostic cohorts involved clinical data and methylation profiles of the NOA-08 (n = 104, elderly glioblastoma) and the EORTC 26101 (n = 297, glioblastoma) studies and 398 patients with glioblastoma from the Heidelberg center. Twenty-eight glioblastoma CpGs from 17 DNA damage response (DDR) genes that negatively correlate with expression derived from a 450 DDR gene list as well as telomerase reverse transcriptase (TERT) promoter mutations were investigated for outcome and interaction with therapy and classifier assignments. RESULTS: CpG methylation in IDH wildtype tumors had the highest association with the mesenchymal (MES) and receptor tyrosine kinase (RTK) I glioblastoma subgroup. MES tumors have lower tumor purity and differ in copy number variations on chromosomes 7 and 10 from the RTK I and II subtypes. CpG hypomethylation of DDR genes (TP73, CCND3, CSNK1E, EXO1, CUL4A and PRPF19) correlated with worse patient survival in particular in MGMT unmethylated tumors. TERT promoter mutation is most frequent in RTK I and II subtypes and associated with worse survival in primary glioblastoma. Primary glioma cells show methylation pattern that resemble RTK I and II glioma and differ from long term established cell lines. Knock-down of selected resistance genes PRPF19 and TERT increase sensitivity to temozolomide treatment in vitro. CONCLUSION: Methylation of DDR genes and TERT promoter mutations are associated with tumor prognosis, dependent on the methylation cluster and MGMT promoter methylation status in high-grade glioma.

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Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

Publications

Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

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