These results provide additional evidence that there may be differences by sex in genetic risk for glioma. Additional analyses may further elucidate the biological processes through which this risk is conferred.
Minnesota peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/235408 doi: bioRxiv preprint first posted online UK10K data generation and access was organized by the UK10K consortium and funded by the Wellcome Trust.peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/235408 doi: bioRxiv preprint first posted online 5 Conflict of Interest: There are no conflicts of interest to report. Total word count: 5354/6000peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/235408 doi: bioRxiv preprint first posted online 6 ABSTRACT Background: Genome-wide association studies (GWAS) have identified 25 risk variants for glioma, which
The use of magnetic resonance imaging (MRI) in healthcare and the emergence of radiology as a practice are both relatively new compared with the classical specialties in medicine. Having its naissance in the 1970s and later adoption in the 1980s, the use of MRI has grown exponentially, consequently engendering exciting new areas of research. One such development is the use of computational techniques to analyze MRI images much like the way a radiologist would. With the advent of affordable, powerful computing hardware and parallel developments in computer vision, MRI image analysis has also witnessed unprecedented growth. Due to the interdisciplinary and complex nature of this subfield, it is important to survey the current landscape and examine the current approaches for analysis and trend trends moving forward.
NEURO-ONCOLOGY • NOVEMBER 2017those tumors according to the glioma methylome-based signatures. RESULTS: The majority of cancer samples fell into the glioma IDH-wtlike group (98.5%). Within it, glioma-IDH-wt-K3-like subtype (n=3147) had a significant better overall survival (OS) than glioma-mesenchymallike (n=3670) and glioma-classic-like (n=658) subgroups (p=2e-16), resembling the behavior of the correspondent glioma subtypes. In contrast to gliomas, in which G-CIMP-low subtype had poorer OS than G-CIMP-high and codels, no significant differences in OS were found among the IDHmutant subtypes. In the IDH-mut-like pan-cancer group, G-CIMP-high/ low subtypes and glioma-codel-like presented none or only 3% IDH mutations, respectively. CONCLUSION: The pan-cancer glioma-methylome signatures revealed that most of human tumors fall into the glioma IDH-wt subtype with similar OS pattern as in gliomas. Among the IDH-mutantlike pan-cancer subtype, the OS were similar among subgroups. IDH was not mutated in the CIMP-like cancer group, suggesting that although these tumors resemble the G-CIMP molecular profile, the molecular mechanisms that may drive the epigenetic phenotype and OS in those tumors are yet to be determined.
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