Dataset Information

Fusion Genes Altered in Adult Malignant Gliomas.

ABSTRACT: Malignant gliomas are highly heterogeneous brain tumors in molecular genetic background. Despite the many recent advances in the understanding of this disease, patients with adult high-grade gliomas retain a notoriously poor prognosis. Fusions involving oncogenes have been reported in gliomas and may serve as novel therapeutic targets to date. Understanding the gene fusions and how they regulate oncogenesis and malignant progression will contribute to explore new approaches for personalized treatment. By now, studies on gene fusions in gliomas remain limited. However, some current clinical trials targeting fusion genes have presented exciting preliminary findings. The aim of this review is to summarize all the reported fusion genes in high-grade gliomas so far, discuss the characterization of some of the most popular gene fusions occurring in malignant gliomas, as well as their function in tumorigenesis, and the underlying clinical implication as therapeutic targets.

SUBMITTER: You G

PROVIDER: S-EPMC8520976 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Fusion Genes Altered in Adult Malignant Gliomas.

You Gan G Fan Xing X Hu Huimin H Jiang Tao T Chen Clark C CC

Frontiers in neurology 20211004

Malignant gliomas are highly heterogeneous brain tumors in molecular genetic background. Despite the many recent advances in the understanding of this disease, patients with adult high-grade gliomas retain a notoriously poor prognosis. Fusions involving oncogenes have been reported in gliomas and may serve as novel therapeutic targets to date. Understanding the gene fusions and how they regulate oncogenesis and malignant progression will contribute to explore new approaches for personalized trea ...[more]

PMID: 34671307

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Project description:Malignant gliomas represent an aggressive class of central nervous system neoplasms. Correlation of interventional outcomes with tumor morphometry data necessitates 3D segmentation of tumors (typically based on magnetic resonance imaging). Expert delineation is the long-held gold standard for tumor segmentation, but is exceptionally resource intensive and subject to intrarater and inter-rater variability. Automated tumor segmentation algorithms have been demonstrated for a variety of imaging modalities and tumor phenotypes, but translation of these methods across clinical study designs is problematic given variation in image acquisition, tumor characteristics, segmentation objectives, and validation criteria. Herein, the authors demonstrate an alternative approach for high-throughput tumor segmentation using Internet-based, collaborative labeling.In a study of 85 human raters and 98 tumor patients, raters were recruited from a general university campus population (i.e., no specific medical knowledge), given minimal training, and provided web-based tools to label MRI images based on 2D cross sections. The labeling goal was characterized as to extract the enhanced tumor cores on T1-weighted MRI and the bright abnormality on T2-weighted MRI. An experienced rater manually constructed the ground truth volumes of a randomly sampled subcohort of 48 tumor subjects (for both T1w and T2w). Raters' taskwise individual observations, as well as the volume wise truth estimates via statistical fusion method, were evaluated over the subjects having the ground truth.Individual raters were able to reliably characterize (with >0.8 dice similarity coefficient, DSC) the gadolinium-enhancing cores and extent of the edematous areas only slightly more than half of the time. Yet, human raters were efficient in terms of providing these highly variable segmentations (less than 20 s per slice). When statistical fusion was used to combine the results of seven raters per slice for all slices in the datasets, the 3D agreement of the fused results with expertly delineated segmentations was on par with the inter-rater reliability observed between experienced raters using traditional 3D tools (approximately 0.85 DSC). The cumulative time spent per tumor patient with the collaborative approach was equivalent to that with an experienced rater, but the collaborative approach could be achieved with less training time, fewer resources, and efficient parallelization.Hence, collaborative labeling is a promising technique with potentially wide applicability to cost-effective manual labeling of medical images.

Dataset Information

Fusion Genes Altered in Adult Malignant Gliomas.

Publications

Fusion Genes Altered in Adult Malignant Gliomas.

Similar Datasets

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