Diffusion Weighted Magnetic Resonance Imaging Radiophenotypes ...

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Diffusion Weighted Magnetic Resonance Imaging Radiophenotypes and Associated Molecular Pathways in Glioblastoma Pascal O. Zinn, MD, PhD*‡§ Masumeh Hatami, MD‡¶ Eslam Youssef, MD‡¶ Ginu A. Thomas, MBBS‡¶ Markus M. Luedi, MD, MBA‡¶k Sanjay K. Singh, PhD‡¶ Rivka R. Colen, MD‡¶ *Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; ‡Department of Cancer Systems Imaging, MD Anderson Cancer Center, Houston, Texas; §Department of Cancer Biology, MD Anderson Cancer Center, Houston, Texas; ¶Department of Diagnostic Radiology, MD Anderson Cancer Center, Houston, Texas; kDepartment of Anesthesiology, Inselspital Berne, Bern, Switzerland Correspondence: Pascal O. Zinn, MD, PhD, Departments of Neurosurgery, Cancer Systems Imaging, and Cancer Biology, MD Anderson Cancer Center and Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030. E-mail: zinn@bcm.edu

Copyright © 2016 by the Congress of Neurological Surgeons.

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iffusion-weighted magnetic resonance imaging (dMRI) was first described in the 1980s and subsequently validated under controlled settings using escalating temperature models to increase Brownian motion and thus water diffusion equivalents.1 Its use was first shown in patients with cerebral infarction, in whom dMRI readily identified small and early strokes.2 In the clinical neurosurgical setting, dMRI and its complementary apparent diffusion coefficient (ADC) sequence are most commonly used to identify acute stroke3,4 and assess brain masses such as abscesses,5,6 lymphoma,7,8 epidermoid cysts,9 and others.10 In addition, dMRI has a role in early detection of primary brain masses,11 differentiating primary from metastatic central nervous system (CNS) disease,12 and evaluating disease progression13 and pseudoprogression14; dMRI has also been found to correlate with histologic tumor grading.15 In glioblastoma (GBM), dMRI shows relative diffusion restriction in areas of the tumor, predominantly in the contrast-enhancing region, where cellular packing reaches the highest density and there is a high nuclear to cytoplasm (N:C) ratio.16 Fluid attenuated inversion recovery (FLAIR) is a T2-based MRI sequence that suppresses the cerebrospinal fluid signal and thus is appropriate for evaluating the peritumoral edema and cellular spread in CNS cancers.17 We have previously shown that a high-FLAIR GBM radiophenotype correlated with expression levels of periostin (POSTN), a protein with validated molecular pathways for cellular invasion and migration and which is involved in vasculogenesis, invasion, inflammation, and niche formation in GBM.18-21 Interestingly, it is still not clearly established to what extent edema and cellular invasion contrib-

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CLINICAL NEUROSURGERY

ute to the peritumoral FLAIR signal abnormality in GBM.19 However, it is clearly established that cellular invasion is associated with poor patient outcomes and also precludes gross total surgical resection.22 Gene signatures of cellular invasion are inherent in GBM and portend poor prognosis.22 Radiogenomics is a relatively novel field that links imaging with genomic data and aims to establish causality as well as predictive models based on qualitative, quantitative, and more complex radiomic texture data.19,22-27 To date, there has been no report on dMRI radiophenotype correlation with gene and microRNA (miRNA) expression data in GBM. Given that dMRI and ADC reflect tumor cellularity and a high N:C ratio in niches of restricted diffusion, we hypothesized that dMRI signal intensity beyond the region of enhancement can identify GBMs that are highly infiltrative within the nonenhancing peritumoral FLAIR area. Therefore, this study examined gene expression profiles associated with restricted diffusion in the peritumoral dMRI-FLAIR niche in GBM.

MATERIALS AND METHODS This Health Insurance Portability and Accountability Act-compliant study was approved by the institutional review board of The University of Texas MD Anderson Cancer Center, and informed consent was waived. Collection of the original data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) was conducted in compliance with all applicable laws, regulations, and policies for the protection of human subjects, and any necessary approvals, authorizations, human subject assurances, informed consent documents, and institutional review board approvals were obtained as per the National Institutes of Health guidelines.28

Patient Population We identified 102 treatment-naïve GBM patients from TCGA with complete clinical annotation and corresponding pretreatment MRI available in the TCIA. Of these, 37 patients had diffusion-weighted imaging (DWI) sequences available for analysis. Two of these 37 patients did not have complete gene and

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ZINN ET AL

miRNA expression profiles in TCGA, and a third patient did not have miRNA expression data (although gene expression analysis was available). Thus, a total of 35 patients were included for imaging-genomic analysis and 34 patients were included for imaging-miRNA analysis.

Image Acquisition and Analysis All imaging sets were downloaded from the National Cancer Institute’s TCIA site (http://cancerimagingarchive.net/). Image segmentation analysis was performed as previously published19 and is described below. Segmentation was used for radiographic phenotypic habitat and volume selection. ADC value extraction was performed using the region of interest (ROI) approach, as detailed below.

Imaging Habitat Feature Extraction Image Analysis and Software The open-access imaging analysis platform, 3D Slicer software, version 4.2 (www.slicer.org), was used for all aspects of imaging analysis, manipulation, and segmentation29-31 of the conventional imaging sequences. Images were reviewed in consensus (R.R.C., 7 years of experience; P.O.Z., 4 years) as previously described by our group.19,22

Phenotype Habitat Sequences and Model Making Two conventional MRI sequences were used to delineate the different phenotypic habitats within each tumor; 3 such radiographic habitats were extracted (edema/invasion [FLAIR], active proliferative enhancing tumor, and necrosis). These sequences were used as follows: (1) the postcontrast T1WI for segmentation of the enhancing region (defined as the active proliferative habitat) and the nonenhancing region (defined as the cell death/necrosis habitat) and (2) FLAIR for segmentation of the peritumoral edema/invasion habitat (Figure 1). Before segmentation, the FLAIR and postcontrast T1WI scans underwent rigid registration and alignment. Resampling of the FLAIR image to the matrix of the T1W1 series was performed if the voxel size varied between the FLAIR and T1W1 series. Registration was deemed adequate when error was 2 mm or less. Habitat phenotype selection was performed by using a hierarchical approach, proceeding from peripheral to central, for extraction of the 3 distinct imaging phenotypes. Standard imaging parameters for each of the sequences were as noted in the TCIA database.

ADC Mapping and Integration on Phenotypic Habitats Analysis and ROI Selection ADC maps were processed, analyzed, and quantified using the Food and Drug Administration-approved medical imaging software platform, Olea Sphere (Olea Medical, La Ciotat, France). Registration of the ADC maps on the conventional imaging (FLAIR; postcontrast T1WI) was performed to determine the regional anatomic locations of each phenotypic habitat in relation to the physiological ADC maps (Figure 2). The edema/invasion habitat was further divided into subhabitats based on the relative distance from the border of the contrast-enhancing tumor margin; these were 0 to 1 cm, 1 to 2 cm, and greater than 2 cm from the border of enhancement of the tumor. Subsequently, 3 ROIs (2.5 mm2) were selected per phenotypic habitat and subhabitat, based on the 3 regions of most restricted diffusion (decreased diffusion). Regions of most restricted diffusion were defined by the 3 lowest ADC values in each habitat and subhabitat per slice. ROIs were obtained on every slice to cover the entire tumor; thus, complete 3-D archetypal regions of most restricted diffusion were obtained. To this end, depending on the tumor size, approximately 12 ROIs were obtained per

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slice, and all slices covering the tumor were analyzed. Therefore, on average, a total of 96 ADC ROIs were obtained per tumor (12 ROIs/slice · 8 slices). An additional ROI was selected within the contralateral normalappearing white matter and used for patient-specific normalization and termed relative ADC (rADC).

rADC Map and Stratification Within each habitat and subhabitat, the mean ADC value was computed by averaging the ADC values of the 3 ROIs/habitats (subhabitats) per slice throughout all corresponding slices. These were then normalized to the ADC ROI of the contralateral normal-appearing white matter to obtain the rADC (Supplemental Digital Content 1, http://links.lww.com/NEU/A865). The FLAIR (edema/invasion) rADC habitat that was 0 to 1 cm beyond the area of contrast enhancement (termed rADCFL) was selected for this study, because this region is associated with the most densely infiltrated and invasive nonenhancing portion of the tumor if present22 and, furthermore, is typically not surgically resected. The median rADCFL was used as cutoff criterion for grouping the patients into restricted vs facilitated diffusion groups. These groups were then analyzed for differential genomic regulatory pathways.

Genomic and Radiogenomic Analysis Level 1 Affymetrix mRNA and Level 3 Agilent miRNA data were downloaded from the public TCGA data portal (http://cancergenome. nih.gov) in October 2015. A set of Affymetrix CEL files was then processed by the ExpressionFileCreator (version 12) module of GenePattern (http://www.broadinstitute.org). In each patient, a total of 13 628 genes (22 277 hybridization probes) and 555 miRNAs were analyzed for significance and differential fold regulation in high vs low rADCFL groups by Comparative Marker Selection (CMS) (http://www. broadinstitute.org). CMS is a statistical method that uses permutation testing to identify differentially regulated genomic events in one vs another predefined patient group as described previously by our group.19 The significant (P , .05) and top greater than 62.0-fold differentially expressed mRNAs and miRNAs were then analyzed for molecular pathways, cellular phenotypes, and disease processes with the online gene ontology tools Ingenuity Pathway Analysis (IPA) (http://www.ingenuity. com) and Gene Set Enrichment Analysis (GSEA) (http://software. broadinstitute.org/gsea). Gene sets representing the 4 molecular GBM subtypes described previously32 were curated and used in custom GSEA analysis. All calculations were performed in Microsoft Excel 2016 and SPSS version 22 software (IBM Inc, Armonk, New York).

Survival Analysis The top upstream regulated network IKBKG (Inhibitor of Kappa Light Polypeptide Gene Enhancer in B-Cells, Kinase Gamma) and its downstream targets (n = 10) associated with low rADCFL were analyzed for survival in the patient databases of TCGA and the Repository of Molecular Brain Neoplasia Data (REMBRANDT). The Cox regression coefficient of each gene was calculated by using a Cox hazard model. A risk score (RS) was calculated for each patient using the equation

RS 5

n X

bi xi ;

i51

where bi is the Cox regression coefficient and xi is the expression level of each gene of the signature.33 Using median risk scores as a cutoff,

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DIFFUSION WEIGHTED MRI RADIOPHENOTYPES

FIGURE 1. A 54-year-old male patient with a right parietal GBM. A, axial postcontrast T1WI demonstrates segmentation of the enhancing (proliferative habitat) portion of the tumor (yellow) and necrosis (orange). B, the peritumoral region of FLAIR hyperintensity corresponding to the area of edema/tumor infiltration habitat has also been segmented (blue). C, the label-mapped segmented edema/tumor infiltration (blue), enhancement (yellow), and necrosis (orange) are seen overlaid on a base postcontrast T1WI to obtain the comprehensive habitat landscape. FLAIR, fluid attenuated inversion recovery; GBM, glioblastoma.

patients were dichotomized into high- and low-risk groups. The difference in overall survival for the 2 gene signature groups was calculated by using the Kaplan-Meier method with a 2-sided log-rank test. Overall survival was defined as the time between the date of pathologic diagnosis and the date of death or the date of last clinical follow-up visit. To evaluate the effect of age and functional impairment on the prognostic effects of the gene signature, we calculated the age- and Karnofsky Performance Score-adjusted hazard ratio (HR) in a multivariable Cox regression model in the TCGA cohort. P # .05 was considered statistically significant. Statistical analyses were performed using Microsoft Excel 2016 (Microsoft, Redmond, Washington), R version 3.2.2, and SPSS version 22 (IBM Inc, Armonk, New York) software.

RESULTS Patient Population A total of 37 patients (15 women and 22 men, mean age 56.1 years, median age 56.0 years, range 17-84 years) were included in the study. The entire cohort was used to determine the rADCFL cutoff, and patients were grouped into facilitated diffusion (18 patients, 11 men and 7 women) and restricted diffusion (19 patients, 12 men and 7 women). Representative images are shown in Figure 2. Demographic details for each group are shown in Table 1, and a comparison of rADCFL quantitation between the 2 groups is shown in Table 2. There was no statistically significant difference in the demographic characteristics between the 2 groups. Imaging Analysis For the entire cohort, the median rADCFL was 1.04, the mean rADCFL was 1.15, and the range for rADCFL was 0.45 to 2.06.


The median was selected for grouping the patients. Distribution of the rADCFL values in the low rADC/restricted diffusion group was as follows: median rADCFL = 0.90, mean rADCFL = 0.85, range = 0.45 to 1.04. Distribution of values in the high rADC/facilitated group was as follows: median rADCFL = 1.41, mean rADCFL = 1.47, range = 1.041 to 2.06. The difference in the rADCFL between the groups was statistically significant (Table 2). Genomic and Radiogenomic Analysis A total of 35 patients were included for imaging-genomic analysis, and 34 patients were included for imaging-miRNA analysis. GSEA showed B lymphoma Mo-MLV insertion region 1 homolog (BMI1) with a net enrichment score of 1.65 and included 142 genes (P = .012) in low rADCFL (Figure 3). Other top oncogenic signatures enriched in low rADCFL patients included Yes-associated protein 1 (YAP1), cyclin D1, and transcription factor E2F3 (E2F3). Additionally, we observed concomitant enrichment of oncogenic signatures in patients with high rADCFL, where the b-catenin signature with 40 genes had an net enrichment score of 21.92 (P = 0). Other oncogenic signatures with significant P values included BMI1, Kirsten rat sarcoma viral oncogene homolog (KRAS), and mechanistic target of rapamycin (MTOR) (Figure 3). CMS yielded 1208 significantly and differentially expressed genes (50 upregulated and 50 downregulated as shown in Supplemental Digital Content 2, http://links.lww.com/NEU/A866, and top upregulated 100 genes and 12 significantly and differentially expressed miRNAs as shown in Supplemental Digital Content 3, http://links.lww.com/NEU/A867) between the low and high rADCFL groups. The top rADCFL upregulated and significantly associated molecular and cellular functions in



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FIGURE 2. A 56-year-old male patient with a right frontal GBM. Axial DWI (A) and ADC map (B) demonstrate mixed areas of restricted and facilitated diffusion, and postcontrast T1WI (C) and FLAIR (D) demonstrate a right frontal lobe heterogeneously enhancing lesion with peritumoral FLAIR hyperintensity. Registered ADC maps to the postcontrast T1WI (E) and FLAIR (F) with final merge (G) are shown. ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; FLAIR, fluid attenuated inversion recovery; GBM, glioblastoma.

patients with restricted diffusion as analyzed with Ingenuity Pathway Analysis (IPA) were cell movement, invasion of tumor cells, and migration (Figure 4A, Table 3, Supplemental Digital Content 4, http://links.lww.com/NEU/A868), whereas the top downregulated molecular pathways in the restricted diffusion radiophenotype patient group were apoptosis, tissue hypoplasia, and growth failure (Figure 4B, Table 3, Supplemental Digital Content 5, http://links.lww.com/NEU/A869). Upstream regu-

lator (including transcription factor, cytokine, growth factor, kinase, and others) analysis, in which the activation status call is made based on expression levels of downstream target genes in the database, showed 4 prominent gene regulatory clusters (Figure 5) associated with the diffusion restricted radiophenotype. As an additional filter, we prioritized the network in which the upstream

TABLE 2. Comparison of ADC Results Between Patients With Restricted and Facilitated Diffusiona TABLE 1. Comparison of Demographic Variables Between Patients With Restricted and Facilitated Diffusiona Facilitated Group (n = 18)

Restricted Group (n = 19)

P

52 10/8 16

59 12/7 12

.07 .6 .3

Age (y) Sex (male/female) KPS ($80)

rADCFL Mean 6 SD Median Range

Facilitated Group (n = 18)

Restricted Group (n = 19)

1.47 6 0.33 1.41 1.041-2.06

0.85 6 0.17 0.90 0.45-1.04

P ,.001 ,.001

a a

KPS, Karnofsky Performance Score.


t test was used for comparison of mean between the 2 groups. Wilcoxon test was used for comparison of median between 2 groups.




FIGURE 3. Enrichment of oncogenic signature in patients with low and high rADCFL. The top 5 enriched gene sets for low and high rADCFL are shown in enrichment plots (top row shows gene sets enriched in low rADCFL; bottom row shows enrichment of gene sets in high rADCFL). The bottom table shows the number of enriched genes (size) associated with each gene set, enrichment and net enrichment scores (ES and NES), and normalized P value for each gene set. Color bar: red, low rADCFL; blue, high rADCFL. Each gene and its association with low and high rADCFL is represented by black vertical lines. rADCFL, relative apparent diffusion coefficient habitat 0 to 1 cm beyond the area of contrast enhancement.

regulator itself was also activated in our data set. The top activated upstream regulator was a kinase, IKBKG. The second activated upstream regulated network was controlled by the cytokine “Colony Stimulating Factor 1” (CSF1), and the third was regulated by the transporter “B-cell CLL/Lymphoma 2” (BCL2), whereas the fourth contained 2 of the regulators activated in the diffusion restricted radiophenotype, “GLI family zinc finger 1” (GLI1, a transcription factor) and “Sonic Hedgehog” (SHH) (Figure 5). The top low rADCFL and thus restricted diffusion-associated miRNAs were miR-189, miR-99a, miR-140, miR-125a, miR150, miR-30c, miR-99b, miR-126, and miR-7e, whereas the top high rADCFL-associated miRNAs were miR-572, miR575, and miR-638 (Supplemental Digital Content 3, http://links.lww.com/NEU/A867, lower panel). Ten of these


miRNAs can potentially target 230 genes (from Supplemental Digital Content 3, http://links.lww.com/NEU/A867, upper panel) and showed inverse expression levels (considering miRNAs are largely considered negative regulators of gene expression). These miRNA-mRNA pairs, when evaluated by IPA, also showed that cellular movement is activated in low rADCFL (Supplemental Digital Content 6, http://links.lww.com/NEU/A870). On the basis of the expression levels of IKBKG and its downstream targets in the TCGA and REMBRANDT data sets, a risk score was calculated for each patient. Patients were dichotomized into low- and high-risk groups based on the median cutoff. In the TCGA dataset, patients who had a lower risk score (n = 240) had significantly longer survival than the high-risk group (n = 240) (Figure 6, left, P = .03). Similar results were derived from the REMBRANDT data set. Low-risk patients



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TABLE 3. Top rADCFL Upregulated and Significantly Associated Molecular and Cellular Functions in Patients With Restricted Diffusion as Analyzed With Ingenuity Pathway Analysis Diseases or Functions Annotation Homing of cells Chemotaxis Chemotaxis of cells Cell movement Invasion of tumor cell lines Invasion of cells Size of embryo Proliferation of epithelial cells Invasion of malignant tumor Migration of cells Formation of cilia Discomfort Apoptosis of epithelial cell lines Bleeding Growth failure Morbidity or mortality Organismal death Hypoplasia of organ Dysgenesis Hypoplasia

P

Predicted Activation State

Activation Z-Score

# Molecules

1.47E-05 2.25E-04 3.32E-04 2.05E-06 3.03E-04 8.53E-07 6.19E-05 5.02E-08 4.26E-04 1.86E-05 2.37E-05 3.39E-04 4.28E-05 2.28E-06 2.00E-06 4.97E-15 2.09E-14 3.65E-05 3.38E-06 2.25E-05

Increased Increased Increased Increased Increased Increased Increased Increased Increased Increased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased

4.276 4.27 4.262 4.176 3.904 3.607 3.478 3.477 3.375 3.265 22.425 22.611 22.684 23.178 23.412 23.721 23.957 24.149 24.233 24.312

62 55 53 194 65 93 40 62 19 171 25 31 28 51 67 255 250 49 61 55

(n = 89) had almost 7 months of longer survival than did the high-risk patients (P = .01) (Figure 6, right). To correct for the effect of age and Karnofsky Performance Score on survival, we calculated the adjusted HR for the TCGA data because full annotation was available and found that the rADCFL gene signature remained an independent prognosticator (Figure 6, adjusted HR 2.47; P = .001).

DISCUSSION In this study, we demonstrated that patients with a restricted diffusion (low rADCFL) MRI radiophenotype in the peritumoral area of FLAIR signal abnormality harbor distinct gene expression and miRNA profiles compared with patients with facilitated diffusion. In particular, the elucidated genomic networks (driven by both mRNA and miRNA-mRNA) were associated with increased migration of cells, invasion, chemotaxis, and cell movement. Furthermore, the restricted diffusion phenotype associated with the top regulatory subunit, IKBKG, a known indirect upstream activator of NF-kB signaling, was predicted to regulate an associated gene cluster likely driving the diffusion restriction phenotype. The latter regulatory gene cluster demonstrated prognostic significance across 2 independent clinicogenomic data sets. Diffusion signal correlates with Brownian movement of water molecules within tissue; in tumor, specifically GBM, the increase in tumor cell density and increase in N:C ratio are major contributors to the decrease in movement of water


molecules and thus the ADC signal. ADC is the quantitative parameter of DWI representing water diffusion in the extracellular, extravascular space and quantifies the amount of diffusion.34 Measuring diffusion, we categorized patients into facilitated (high ADC) vs restricted (low ADC) diffusion within the area of nonenhancing FLAIR signal abnormality within 1 cm of tumor-enhancement borders. This was chosen because this immediate area around the contrast-enhancing part is known to harbor potentially clinically relevant areas of tumor cell invasion.35 Our results demonstrated that patients can be segregated into statistically significant different groups of high vs low rADCFL based on the median cutoff and harbor distinct genomic networks involved in invasion. Although this is the first report (to our knowledge) correlating ADC with genomic networks, ADC is known as a noninvasive measure of hypercellularity and invasion, and our findings are concordant with the literature.35,36 Using GSEA, we obtained rADCFL differentially enriched gene sets between patients with restricted vs facilitated diffusion. Specifically, in the patients with restricted diffusion, we found that BMI1 and Cyclin D1 were upregulated and YAP1 and E2F3 were downregulated. Interestingly, BMI1 was the only consistently enriched and activated gene set in low rADCFL and concomitantly predicted to be downregulated in the facilitated diffusion radiophenotype group. BMI1 is a known regulator of stemlike states in cancer cells and is associated with migration, invasion, and poor prognosis,37-39 thus further supporting the linkage between rADCFL and invasive gene signatures.




FIGURE 4. Pathway analysis of genes significantly altered in low rADCFL patients compared with high rADCFL patients. A, gene network associated with cell movement, invasion of cell lines, homing of cells, and chemotaxis of cells is shown. (Green: downregulated; red: upregulated) and P # .05. Orange color shows activated status of cellular and biological function (z-score $ 2.0). B, gene network associated with hypoplasia of organ, apoptosis of epithelial cell lines, dysgenesis, hypoplasia, and growth failure is shown. (Green: downregulated; red: upregulated) and P # .05. Blue color shows inhibited status of cellular and biological function (z-score $ 2.0). rADCFL, relative apparent diffusion coefficient habitat 0 to 1 cm beyond the area of contrast enhancement.

We then ran a custom gene set enrichment analysis against the 4 subgroups of GBM32; in those patients with high rADCFL, only the “neural” signature was significantly enriched. Because

the “neural” GBM subtype is the most normal brain-like genomic signature, it is thought to be partially derived from noncancerous brain cell RNA (Supplemental Digital Content 7,

FIGURE 5. Significantly activated gene networks associated with specific upstream regulators in low rADCFL. The top 5 upstream regulators that are also upregulated (Exp. Fold Change) in patients with low rADCFL and their downstream target (# molecules) networks are shown. The numbers under each gene represent fold change (green: downregulated; red: upregulated) and P # .05. Orange color shows activated status of upstream regulator (z-score $ 2.0). Molecule type shows known molecular function of the upstream regulator. rADCFL, relative apparent diffusion coefficient habitat 0 to 1 cm beyond the area of contrast enhancement.




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FIGURE 6. Association of IKBKG network with survival of patients in 2 independent clinicogenomic databases (TCGA and REMBRANDT) evaluated by Kaplan-Meier analysis. Left, significant survival difference in patients with high or low IKBKG network status in TCGA database. Median survival, P value, age, and sex-adjusted hazard ratio (HR) of the risk scores are shown within the plot. Right, significant survival difference in patients with high or low IKBKG network status in REMBRANDT database. Median survival and P value are shown within the plot. Red line: patients with high IKBKG network risk score; green line: patients with low IKBKG network risk score. The center table shows the individual genes of the IKBKG network and their respective expression levels (fold change). IKBKG, Inhibitor of Kappa Light Polypeptide Gene Enhancer in B-Cells, Kinase Gamma; REMBRANDT, Repository of Molecular Brain Neoplasia Data; TCGA, The Cancer Genome Atlas.

http://links.lww.com/NEU/A871). Thus, it appears that the high rADCFL-associated gene signature is of lesser oncogenic potential, whereas low rADCFL was not significantly associated with any of the known GBM molecular subgroups, although it positively trended toward “mesenchymal” (data not shown). In patients with restricted diffusion compared with facilitated diffusion, we found significantly and differentially expressed genes (Supplemental Digital Content 3, http://links.lww.com/NEU/A867, upper) and miRNAs (Supplemental Digital Content 3, http://links.lww.com/NEU/A867, lower), further supporting the GSEA findings of low rADCFLassociated molecular pathways of cellular invasion and migration (Figure 4A, Table 3). Further analysis demonstrated the top upstream regulator to be IKBKG, an X-chromosome-bound activator of NFkB signaling. This finding is concordant with the published literature indicating that NF-kB regulates proliferation, invasion, and mesenchymal transdifferentiation in glioma.40,41 The 3 upstream regulatory gene clusters were also associated with features of GBM oncogenicity such as invasion and migration, thus further verifying the low rADCFL radiophenotype’s association with the initially hypothesized oncogenic molecular pathways (Figure 5). The latter finding was further substantiated by the fact that a low rADCFL-derived gene signature yielded prognostic significance across 2 independent clinicogenomic data sets (Figure 6). Despite the fact that our analysis yielded genomic pathways and molecular functions as hypothesized, there are a few inherent


limitations to this study. Data from the TCGA do not have spatial encoding of the biopsy location, and, hence, there is no exact corresponding imaging voxel matched to the biopsy site. Furthermore, causality needs to be evaluated in future studies, and MRIbased predictive radiogenomic models will be required. These studies are underway at our institution.

CONCLUSION In summary, our results substantiate the link of MRI phenotypes and genomics; specifically, patients with low rADCFL had genes and gene networks associated with invasion and trended toward a more “mesenchymal” subtype. As we move forward and the relatively young field of cancer radiogenomics takes shape, causality will need to be established to further these exciting discoveries, and precision-based biopsies will be needed to identify accurate radiogenomic linkage. Disclosures Dr Zinn receives funding from the Neurosurgery Research and Education Foundation and the R25 Neurosurgery Program Grant through Baylor College of Medicine. Dr Colen receives funding from the Radiological Society of North America Research and Educational Foundation, the John S. Dunn Sr Distinguished Chair in Diagnostic Imaging at MD Anderson Cancer Center, and MD Anderson Cancer Center startup funds. The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.




REFERENCES 1. Thomsen C, Henriksen O, Ring P. In vivo measurement of water self diffusion in the human brain by magnetic resonance imaging. Acta Radiol. 1987;28(3): 353-361. 2. Srinivasan A, Goyal M, Azri FA, Lum C. State-of-the-art imaging of acute stroke. Radiographics. 2006;26(suppl 1):S75-S95. 3. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci. 2003;4(6):469-480. 4. Tong DC, Yenari MA, Albers GW, O’Brien M, Marks MP, Moseley ME. Correlation of perfusion- and diffusion-weighted MRI with NIHSS score in acute (,6.5 hour) ischemic stroke. Neurology. 1998;50(4):864-870. 5. Chang SC, Lai PH, Chen WL, et al. Diffusion-weighted MRI features of brain abscess and cystic or necrotic brain tumors: comparison with conventional MRI. Clin Imaging. 2002;26(4):227-236. 6. Noguchi K, Watanabe N, Nagayoshi T, et al. Role of diffusion-weighted echoplanar MRI in distinguishing between brain abscess and tumour: a preliminary report. Neuroradiology. 1999;41(3):171-174. 7. Buhring U, Herrlinger U, Krings T, Thiex R, Weller M, Kuker W. MRI features of primary central nervous system lymphomas at presentation. Neurology. 2001;57 (3):393-396. 8. Kuker W, Nagele T, Korfel A, et al. Primary central nervous system lymphomas (PCNSL): MRI features at presentation in 100 patients. J Neurooncol. 2005;72(2): 169-177. 9. Dutt SN, Mirza S, Chavda SV, Irving RM. Radiologic differentiation of intracranial epidermoids from arachnoid cysts. Otol Neurotol. 2002;23(1):84-92. 10. Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology. 2000;217(2):331-345. 11. Baehring JM, Bi WL, Bannykh S, Piepmeier JM, Fulbright RK. Diffusion MRI in the early diagnosis of malignant glioma. J Neurooncol. 2007;82(2):221-225. 12. Lee EJ, terBrugge K, Mikulis D, et al. Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol. 2011;196(1):71-76. 13. Gerstner ER, Frosch MP, Batchelor TT. Diffusion magnetic resonance imaging detects pathologically confirmed, nonenhancing tumor progression in a patient with recurrent glioblastoma receiving bevacizumab. J Clin Oncol. 2010;28(6):e91-e93. 14. Hu X, Wong KK, Young GS, Guo L, Wong ST. Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging. 2011;33(2):296-305. 15. Yan R, Haopeng P, Xiaoyuan F, et al. Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology. 2016;58(2): 121-132. 16. Gauvain KM, McKinstry RC, Mukherjee P, et al. Evaluating pediatric brain tumor cellularity with diffusion-Tensor imaging. Am J Roentgenol. 2001;177(2):449-454. 17. de Groot JF, Fuller G, Kumar AJ, et al. Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice. Neuro Oncol. 2010;12(3):233-242. 18. Zhou W, Ke SQ, Huang Z, et al. Periostin secreted by glioblastoma stem cells recruits M2 tumour-associated macrophages and promotes malignant growth. Nat Cell Biol. 2015;17(2):170-182. 19. Zinn PO, Mahajan B, Sathyan P, et al. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One. 2011;6(10): e25451. 20. Wang H, Wang Y, Jiang C. Stromal protein periostin identified as a progression associated and prognostic biomarker in glioma via inducing an invasive and proliferative phenotype. Int J Oncol. 2013;42(5):1716-1724. 21. Tian B, Zhang Y, Zhang J. Periostin is a new potential prognostic biomarker for glioma. Tumour Biol. 2014;35(6):5877-5883.


22. Colen RR, Wang J, Singh SK, Gutman DA, Zinn PO. Glioblastoma: imaging genomic mapping reveals sex-specific oncogenic associations of cell death. Radiology. 2015;275(1):215-227. 23. Colen RR, Vangel M, Wang J, et al. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Med Genomics. 2014;7:30. 24. Zinn PO, Colen RR. Imaging genomic mapping in glioblastoma. Neurosurgery. 2013;60(suppl 1):126-130. 25. Zinn PO, Mahmood Z, Elbanan MG, Colen RR. Imaging genomics in gliomas. Cancer J. 2015;21(3):225-234. 26. Zinn PO, Sathyan P, Mahajan B, et al. A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature. PLoS One. 2012;7(8):e41522. 27. Gevaert O, Mitchell LA, Achrol AS, et al. Glioblastoma multiforme: Exploratory radiogenomic analysis by using quantitative image features. Radiology. 2015;276 (1):313. 28. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455 (7216):1061-1068. 29. Gering DT, Nabavi A, Kikinis R, et al. An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging. 2001;13(6):967-975. 30. Pichon E, Tannenbaum A, Kikinis R. A statistically based flow for image segmentation. Med Image Anal. 2004;8(3):267-274. 31. Archip N, Jolesz FA, Warfield SK. A validation framework for brain tumor segmentation. Acad Radiol. 2007;14(10):1242-1251. 32. Verhaak RGW, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98-110. 33. Srinivasan S, Patric IR, Somasundaram K. A ten-microRNA expression signature predicts survival in glioblastoma. PLoS One. 2011;6(3):e17438. 34. Bihan DL, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497-505. 35. Lu S, Ahn D, Johnson G, Law M, Zagzag D, Grossman RI. Diffusion-tensor MR imaging of intracranial Neoplasia and associated peritumoral edema: Introduction of the tumor infiltration index. Radiology. 2004;232(1):221-228. 36. Lutz K, Wiestler B, Graf M, et al. Infiltrative patterns of glioblastoma: identification of tumor progress using apparent diffusion coefficient histograms. J Magn Reson Imaging. 2014;39(5):1096-1103. 37. He Q, Liu Z, Zhao T, Zhao L, Zhou X, Wang A. Bmi1 drives stem-like properties and is associated with migration, invasion, and poor prognosis in tongue squamous cell carcinoma. Int J Biol Sci. 2015;11(1):1-10. 38. Venugopal C, Li N, Wang X, et al. Bmi1 marks intermediate precursors during differentiation of human brain tumor initiating cells. Stem Cell Res. 2012;8(2):141-153. 39. Fasano CA, Phoenix TN, Kokovay E, et al. Bmi-1 cooperates with Foxg1 to maintain neural stem cell self-renewal in the forebrain. Genes Devel. 2009;23(5):561-574. 40. Nagai S, Washiyama K, Kurimoto M, Takaku A, Endo S, Kumanishi T. Aberrant nuclear factor-kB activity and its participation in the growth of human malignant astrocytoma. J Neurosurg. 2002;96(5):909-917. 41. Bhat KPL, Balasubramaniyan V, Vaillant B, et al. Mesenchymal differentiation Mediated by NF-kB promotes Radiation Resistance in glioblastoma. Cancer cell. 2013;24(3):331-346. doi: 10.1016/j.ccr.2013.1008.1001.

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