Dataset Information

EGFR Amplification and Glioblastoma Stem-Like Cells.

ABSTRACT: Glioblastoma (GBM), the most common malignant brain tumor in adults, contains a subpopulation of cells with a stem-like phenotype (GS-cells). GS-cells can be maintained in vitro using serum-free medium supplemented with epidermal growth factor, basic fibroblast growth factor-2, and heparin. However, this method does not conserve amplification of the Epidermal Growth Factor Receptor (EGFR) gene, which is present in over 50% of all newly diagnosed GBM cases. GS-cells with retained EGFR amplification could overcome the limitations of current in vitro model systems and contribute significantly to preclinical research on EGFR-targeted therapy. This review recapitulates recent methodological approaches to expand stem-like cells from GBM with different EGFR status in order to maintain EGFR-dependent intratumoral heterogeneity in vitro. Further, it will summarize the current knowledge about the impact of EGFR amplification and overexpression on the stem-like phenotype of GBM-derived GS-cells and different approaches to target the EGFR-dependent GS-cell compartment of GBM.

SUBMITTER: Liffers K

PROVIDER: S-EPMC4468289 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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EGFR Amplification and Glioblastoma Stem-Like Cells.

Liffers Katrin K Lamszus Katrin K Schulte Alexander A

Stem cells international 20150602

Glioblastoma (GBM), the most common malignant brain tumor in adults, contains a subpopulation of cells with a stem-like phenotype (GS-cells). GS-cells can be maintained in vitro using serum-free medium supplemented with epidermal growth factor, basic fibroblast growth factor-2, and heparin. However, this method does not conserve amplification of the Epidermal Growth Factor Receptor (EGFR) gene, which is present in over 50% of all newly diagnosed GBM cases. GS-cells with retained EGFR amplificati ...[more]

PMID: 26136784

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Project description:Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Dataset Information

EGFR Amplification and Glioblastoma Stem-Like Cells.

Publications

EGFR Amplification and Glioblastoma Stem-Like Cells.

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