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Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data

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Abstract

Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient outcomes using algorithmic measurements of tumor shape in magnetic resonance imaging (MRI). We analyzed preoperative imaging and genomic subtype data from 110 patients with lower-grade gliomas (WHO grade II and III) from The Cancer Genome Atlas. Computer algorithms were applied to analyze the imaging data and provided five quantitative measurements of tumor shape in two and three dimensions. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. Patient outcomes were quantified by overall survival. We found that there is a strong association between angular standard deviation (ASD), which measures irregularity of the tumor boundary, and the IDH-1p/19q subtype (p < 0.0017), RNASeq cluster (p < 0.0002), DNA copy number cluster (p < 0.001), and the cluster of clusters (p < 0.0002). The RNASeq cluster was also associated with bounding ellipsoid volume ratio (p < 0.0005). Tumors in the IDH wild type cluster and R2 RNASeq cluster which are associated with much poorer outcomes generally had higher ASD reflecting more irregular shape. ASD also showed association with patient overall survival (p = 0.006). Shape features in MRI were strongly associated with genomic subtypes and patient outcomes in lower-grade glioma.

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Funding

There are no funding sources, which supported this research, to be disclosed.

Author information

Authors and Affiliations

  1. Department of Radiology, Duke University, Durham, NC, USA

    Maciej A. Mazurowski, Kal Clark, Parisa Shamsesfandabadi & Ashirbani Saha

  2. Department of Electrical and Computer Engineering, Durham, NC, USA

    Maciej A. Mazurowski & Nicholas M. Czarnek

  3. Duke Medical Physics Program, Durham, NC, USA

    Maciej A. Mazurowski

  4. Department of Neurology, Durham, NC, USA

    Katherine B. Peters

Authors
  1. Maciej A. Mazurowski
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  2. Kal Clark
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  3. Nicholas M. Czarnek
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  4. Parisa Shamsesfandabadi
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  5. Katherine B. Peters
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  6. Ashirbani Saha
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Corresponding author

Correspondence to Maciej A. Mazurowski.

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Conflict of interest

Dr. Katherine B. Peters received research funding from the following companies: Agios, AMGEN, BioMimetix, Eisai, Genentech, Merck, VBL. Dr. Katherine B. Peters is on the Advisory Board of the following companies:Agios, Novocure. Other authors have nothing relevant to disclose.

Ethical approval

This study has been conducted using the publicly available data from The Cancer Genome Atlas (TCGA) and we secured an institutional review board exemption for studying this data at our institution.

Informed consent

This study has been conducted using the publicly available data from The Cancer Genome Atlas (TCGA) and the policies for informed consent of TCGA are available at http://cancergenome.nih.gov/abouttcga/policies/informedconsent and http://cancergenome.nih.gov/pdfs/TCGA_Human_Subjects_Protection_and_Data_Access_Policies_Rev_2014-01-16.pdf.

Appendix 1

Appendix 1

Full list of the 110 patient IDs used in this study (comma-separated):

TCGA-CS-4941, TCGA-CS-4942, TCGA-CS-4943, TCGA-CS-4944, TCGA-CS-5393, TCGA-CS-5395, TCGA-CS-5396, TCGA-CS-5397, TCGA-CS-6186, TCGA-CS-6188, TCGA-CS-6290, TCGA-CS-6665, TCGA-CS-6666, TCGA-CS-6667, TCGA-CS-6668, TCGA-CS-6669, TCGA-DU-5849, TCGA-DU-5851, TCGA-DU-5852, TCGA-DU-5853, TCGA-DU-5854, TCGA-DU-5855, TCGA-DU-5871, TCGA-DU-5872, TCGA-DU-5874, TCGA-DU-6399, TCGA-DU-6400, TCGA-DU-6401, TCGA-DU-6404, TCGA-DU-6405, TCGA-DU-6407, TCGA-DU-6408, TCGA-DU-7008, TCGA-DU-7010, TCGA-DU-7013, TCGA-DU-7014, TCGA-DU-7018, TCGA-DU-7019, TCGA-DU-7294, TCGA-DU-7298, TCGA-DU-7299, TCGA-DU-7300, TCGA-DU-7301, TCGA-DU-7302, TCGA-DU-7304, TCGA-DU-7306, TCGA-DU-7309, TCGA-DU-8162, TCGA-DU-8163, TCGA-DU-8164, TCGA-DU-8165, TCGA-DU-8166, TCGA-DU-8167, TCGA-DU-8168, TCGA-DU-A5TP, TCGA-DU-A5TR, TCGA-DU-A5TS, TCGA-DU-A5TT, TCGA-DU-A5TU, TCGA-DU-A5TW, TCGA-DU-A5TY, TCGA-EZ-7264, TCGA-FG-5962, TCGA-FG-5964, TCGA-FG-6688, TCGA-FG-6689, TCGA-FG-6690, TCGA-FG-6691, TCGA-FG-6692, TCGA-FG-7634, TCGA-FG-7637, TCGA-FG-7643, TCGA-FG-8189, TCGA-FG-A4MT, TCGA-FG-A4MU, TCGA-FG-A60K, TCGA-HT-7473, TCGA-HT-7475, TCGA-HT-7602, TCGA-HT-7605, TCGA-HT-7608, TCGA-HT-7616, TCGA-HT-7680, TCGA-HT-7684, TCGA-HT-7686, TCGA-HT-7690, TCGA-HT-7692, TCGA-HT-7693, TCGA-HT-7694, TCGA-HT-7855, TCGA-HT-7856, TCGA-HT-7860, TCGA-HT-7874, TCGA-HT-7877, TCGA-HT-7879, TCGA-HT-7881, TCGA-HT-7882, TCGA-HT-7884, TCGA-HT-8018, TCGA-HT-8105, TCGA-HT-8106, TCGA-HT-8107, TCGA-HT-8111, TCGA-HT-8113, TCGA-HT-8114, TCGA-HT-8563, TCGA-HT-A5RC, TCGA-HT-A616, TCGA-HT-A61A, TCGA-HT-A61B.

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Mazurowski, M.A., Clark, K., Czarnek, N.M. et al. Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data. J Neurooncol 133, 27–35 (2017). https://doi.org/10.1007/s11060-017-2420-1

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  • DOI: https://doi.org/10.1007/s11060-017-2420-1

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