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Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma
BACKGROUND: Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic as...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977720/ https://www.ncbi.nlm.nih.gov/pubmed/27502180 http://dx.doi.org/10.1186/s12885-016-2659-5 |
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author | Grossmann, Patrick Gutman, David A. Dunn, William D. Holder, Chad A. Aerts, Hugo J. W. L. |
author_facet | Grossmann, Patrick Gutman, David A. Dunn, William D. Holder, Chad A. Aerts, Hugo J. W. L. |
author_sort | Grossmann, Patrick |
collection | PubMed |
description | BACKGROUND: Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways. METHODS: One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication. RESULTS: Volumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10(−4)). CONCLUSION: GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-016-2659-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4977720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49777202016-08-10 Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma Grossmann, Patrick Gutman, David A. Dunn, William D. Holder, Chad A. Aerts, Hugo J. W. L. BMC Cancer Research Article BACKGROUND: Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways. METHODS: One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication. RESULTS: Volumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10(−4)). CONCLUSION: GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-016-2659-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-08 /pmc/articles/PMC4977720/ /pubmed/27502180 http://dx.doi.org/10.1186/s12885-016-2659-5 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Grossmann, Patrick Gutman, David A. Dunn, William D. Holder, Chad A. Aerts, Hugo J. W. L. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title | Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title_full | Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title_fullStr | Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title_full_unstemmed | Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title_short | Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma |
title_sort | imaging-genomics reveals driving pathways of mri derived volumetric tumor phenotype features in glioblastoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977720/ https://www.ncbi.nlm.nih.gov/pubmed/27502180 http://dx.doi.org/10.1186/s12885-016-2659-5 |
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