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Open Access 01-12-2016 | Research article

Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma

Authors: Patrick Grossmann, David A. Gutman, William D. Dunn Jr, Chad A. Holder, Hugo J. W. L. Aerts

Published in: BMC Cancer | Issue 1/2016

Abstract

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.

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Thakkar JP, Dolecek TA, Horbinski C, Ostrom QT, Lightner DD, Barnholtz-Sloan JS, Villano JL. Epidemiologic and Molecular Prognostic Review of Glioblastoma. Cancer Epidemiol Biomarkers Prev. 2014;23:1985–96.CrossRefPubMedPubMedCentral

Omuro A, DeAngelis LM. Glioblastoma and other malignant gliomas: a clinical review. JAMA. 2013;310:1842–50.CrossRefPubMed

Zhu J-J, Wong ET. Personalized medicine for glioblastoma: current challenges and future opportunities. Curr Mol Med. 2013;13:358–67.PubMed

Hegi ME, Diserens A-C, Gorlia T, Hamou M-F, de Tribolet N, Weller M, Kros JM, Hainfellner JA, Mason W, Mariani L, Bromberg JEC, Hau P, Mirimanoff RO, Cairncross JG, Janzer RC, Stupp R. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352:997–1003.CrossRefPubMed

Drabycz S, Roldán G, de Robles P, Adler D, McIntyre JB, Magliocco AM, Cairncross JG, Mitchell JR. An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage. 2010;49:1398–405.CrossRefPubMed

Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, Aldape K, Cha S, Kuo MD. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A. 2008;105:5213–8.CrossRefPubMedPubMedCentral

Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782–90.CrossRefPubMed

Wangaryattawanich P, Hatami M, Wang J, Thomas G, Flanders A, Kirby J, Wintermark M, Huang ES, Bakhtiari AS, Luedi MM, Hashmi SS, Rubin DL, Chen JY, Hwang SN, Freymann J, Holder CA, Zinn PO, Colen RR. Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro Oncol. 2015;17(11):1525–37.CrossRefPubMed

Wang Y, Wang K, Li H, Wang J, Wang L, Dai J, Jiang T, Ma J. Identifying the association of contrast enhancement with vascular endothelia growth factor expression in anaplastic gliomas: a volumetric magnetic resonance imaging analysis. PLoS One. 2015;10:e0121380.CrossRefPubMedPubMedCentral

10.

Naeini KM, Pope WB, Cloughesy TF, Harris RJ, Lai A, Eskin A, Chowdhury R, Phillips HS, Nghiemphu PL, Behbahanian Y, Ellingson BM. Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. Neuro Oncol. 2013;15:626–34.CrossRefPubMedPubMedCentral

11.

Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci Signal. 2013;6:l1–pl1.CrossRef

12.

Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, Miller CR, Ding L, Golub T, Mesirov JP, Alexe G, Lawrence M, O’Kelly M, Tamayo P, Weir BA, Gabriel S, Winckler W, Gupta S, Jakkula L, Feiler HS, Hodgson JG, James CD, Sarkaria JN, Brennan C, Kahn A, Spellman PT, Wilson RK, Speed TP, Gray JW, Meyerson M, et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17:98–110.CrossRefPubMedPubMedCentral

13.

Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861–74 [ROC Analysis in Pattern Recognition].CrossRef

14.

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.CrossRefPubMedPubMedCentral

15.

Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–40.CrossRefPubMedPubMedCentral

16.

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.CrossRefPubMedPubMedCentral

17.

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Series B Stat Methodol. 1995;57:289–300.

18.

Schröder MS, Culhane AC, Quackenbush J, Haibe-Kains B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics. 2011;27(22):3206–8.CrossRefPubMedPubMedCentral

19.

R Development Core Team: R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013. URL http://www.R-project.org. ISBN 3-900051-07-0.

20.

Harrell Jr FE, Califf RM, Pryor DB, Lee KL, Rosati RA. EValuating the yield of medical tests. JAMA. 1982;247:2543–6.CrossRefPubMed

21.

Lowe SW, Lin AW. Apoptosis in cancer. Carcinogenesis. 2000;21:485–95.CrossRefPubMed

22.

Gajewski TF, Schreiber H, Fu Y-X. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14:1014–22.CrossRefPubMedPubMedCentral

23.

Levitzki A, Klein S. Signal transduction therapy of cancer. Mol Aspects Med. 2010;31:287–329 [Signal Transduction Therapy of Cancer].CrossRefPubMed

24.

Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Cheshier SH, Napel S, Zaharchuk G, Plevritis SK. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2014;273:168–74.CrossRefPubMedPubMedCentral

25.

Kaufmann WK, Kaufman DG. Cell cycle control, DNA repair and initiation of carcinogenesis. FASEB J. 1993;7:1188–91.PubMed

26.

Gutman DA, Cooper LAD, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn Jr WD, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology. 2013;267:560–9.CrossRefPubMedPubMedCentral

27.

Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95:190–8.CrossRefPubMed

28.

Jamshidi N, Diehn M, Bredel M, Kuo MD. Illuminating Radiogenomic Characteristics of Glioblastoma Multiforme through Integration of MR Imaging, Messenger RNA Expression, and DNA Copy Number Variation. Radiology. 2013;270:1–2.CrossRefPubMed

29.

Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology. 2013;267:212–20.CrossRefPubMedPubMedCentral

30.

Gutman DA, Dunn Jr WD, Grossmann P, Cooper LAD, Holder CA, Ligon KL, Alexander BM, Aerts HJWL. Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology. 2015;57(12):1227–37.CrossRefPubMedPubMedCentral

31.

Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J, Freymann J, Jaffe CC, Mikkelsen T, Flanders A. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology. 2014;272:484–93.CrossRefPubMedPubMedCentral

32.

Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, Jain R, Colen R, Rubin DL, Zinn PO, Hwang SN, Raghavan P, Hammoud DA, Scarpace LM, Mikkelsen T, Chen J, Gevaert O, Buetow K, Freymann J, Kirby J, Flanders AE, Wintermark M, TCGA Glioma Phenotype Research Group. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol. 2015;42:212–21.CrossRefPubMed

33.

Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, Jolesz FA, Colen RR. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS One. 2011;6:e25451.CrossRefPubMedPubMedCentral

34.

Pope WB, Chen JH, Dong J, Carlson MRJ, Perlina A, Cloughesy TF, Liau LM, Mischel PS, Nghiemphu P, Lai A, Nelson SF. Relationship between Gene Expression and Enhancement in Glioblastoma Multiforme: Exploratory DNA Microarray Analysis. Radiology. 2008;249:268–77.CrossRefPubMedPubMedCentral

35.

Kim H, Zheng S, Amini SS, Virk SM, Mikkelsen T, Brat DJ, Grimsby J, Sougnez C, Muller F, Hu J, Sloan AE, Cohen ML, Van Meir EG, Scarpace L, Laird PW, Weinstein JN, Lander ES, Gabriel S, Getz G, Meyerson M, Chin L, Barnholtz-Sloan JS, Verhaak RGW. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 2015;25:316–27.CrossRefPubMedPubMedCentral

36.

Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suvà ML, Regev A, Bernstein BE. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344:1396–401.CrossRefPubMedPubMedCentral

37.

Sottoriva A, Spiteri I, Piccirillo SGM, Touloumis A, Collins VP, Marioni JC, Curtis C, Watts C, Tavaré S. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013;110:4009–14.CrossRefPubMedPubMedCentral

38.

Kumar N, Kumar P, Angurana SL, Khosla D, Mukherjee KK, Aggarwal R, Kumar R, Bera A, Sharma SC. Evaluation of outcome and prognostic factors in patients of glioblastoma multiforme: A single institution experience. J Neurosci Rural Pract. 2013;4 Suppl 1:S46–55.CrossRefPubMedPubMedCentral

39.

Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiol. 2016;278:563–77.

Title: Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma
Authors: Patrick Grossmann
David A. Gutman
William D. Dunn Jr
Chad A. Holder
Hugo J. W. L. Aerts
Publication date: 01-12-2016
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2016
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-016-2659-5

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