Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns?
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ABSTRACT:
SUBMITTER: Soike MH
PROVIDER: S-EPMC7193682 | biostudies-literature | 2018 Oct
REPOSITORIES: biostudies-literature
Publications
Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns?
Soike Michael H MH McTyre Emory R ER Shah Nameeta N Puchalski Ralph B RB Holmes Jordan A JA Paulsson Anna K AK Miller Lance D LD Cramer Christina K CK Lesser Glenn J GJ Strowd Roy E RE Hinson William H WH Mott Ryan T RT Johnson Annette J AJ Lo Hui-Wen HW Laxton Adrian W AW Tatter Stephen B SB Debinski Waldemar W Chan Michael D MD
Neuroradiology 20180810 10
<h4>Purpose</h4>For glioblastoma (GBM), imaging response (IR) or pseudoprogression (PSP) is frequently observed after chemoradiation and may connote a favorable prognosis. With tumors categorized by the Cancer Genome Atlas Project (mesenchymal, classical, neural, and proneural) and by methylguanine-methyltransferase (MGMT) methylation status, we attempted to determine if certain genomic or molecular subtypes of GBM were specifically associated with IR or PSP.<h4>Methods</h4>Patients with GBM tre ...[more]
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Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.
2018-01-05 | GSE103659 | GEO
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| S-EPMC8579096 | biostudies-literature
Project description:BackgroundMRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).MethodsOne hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients.ResultsSurvival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy.ConclusionsBy employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.
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Project description:Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.
2012-05-20 | E-GEOD-32374 | biostudies-arrayexpress
Project description:Congenital glioblastoma (cGBM) is an uncommon tumor of infancy with a reported variable but often poor cure rate, even with intensive therapy. Five patients with cGBMs, arising de novo and not in familial tumor predisposition kindreds, were studied for histological and biological features, using Affymetrix microarray. Tumors were large, often associated with hemorrhage, extended into the thalamus, and often bulged into the ventricles. One patient died acutely from bleeding at the time of operation. The 4 surviving patients underwent surgery (1 gross total resection, 3 subtotal resections or biopsies) and moderate intensity chemotherapy without radiation, and remain progression-free at a median time of 36 months (range, 30-110 months). Affymetrix microarrays measured gene expression on the 3 cGBMs from which frozen tissue was available. Unsupervised hierarchical clustering of cGBMs versus 168 other central nervous system tumors demonstrated that cGBMs clustered most closely with other high-grade gliomas. Gene expression profiles of cGBMs were compared with non-congenital pediatric and adult GBMs. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only 31 differentially expressed genes identified (false discovery rate, <0.05). Unique molecular features of cGBMs included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia. cGBMs show histological and biological overlap with pediatric and adult GBMs but appear to have a more favorable outcome, with good response to moderate intensity chemotherapy with only subtotal resection or biopsy. Further study may determine whether identified gene expression differences contribute to the improved survival seen in these tumors.
| S-EPMC3379807 | biostudies-other
Project description:Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray
2012-05-21 | GSE32374 | GEO
Project description:Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.
| S-ECPF-GEOD-32374 | biostudies-other
Project description:We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis ("heterogenous enhancing", "rim-enhancing necrotic", and "cystic" subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation.
| S-EPMC7408408 | biostudies-literature
Project description:Background:The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods:Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results:The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ? 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ? 0.01). Conclusions:Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.
| S-EPMC5961168 | biostudies-literature