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. Author manuscript; available in PMC: 2020 Oct 26.
Published in final edited form as: Neuroradiology. 2019 May 27;61(9):1023–1031. doi: 10.1007/s00234-019-02219-8

MR Imaging phenotype correlates with extent of genome-wide copy number abundance in IDH mutant astrocytomas

Chih-Chin Wu 1,2, Rajan Jain 3,4,*, Lucidio Neto 3, Seema Patel 5, Laila M Poisson 6, Jonathan Serrano 5, Victor Ng 3, Sohil H Patel 7, Dimitris G Placantonakis 4, David Zagzag 5, John Golfinos 4, Andrew S Chi 8, Matija Snuderl 5
PMCID: PMC7587301  NIHMSID: NIHMS1594191  PMID: 31134296

Abstract

Purpose:

There is variability in survival within IDH-mutant gliomas determined by chromosomal events. Copy number variation (CNV) abundance associated with survival in low-grade and IDH mutant astrocytoma have been reported. Our purpose was to correlate the extent of genome-wide CNV abundance in IDH-mutant astrocytomas with MRI features.

Methods:

Presurgical MRI and CNV plots derived from Illumina 850k EPIC DNA methylation arrays of 18 cases of WHO grade II-IV IDH-mutant astrocytomas were reviewed. IDH-mutant astrocytomas were divided into CNV stable group (CNV-S) with </=3 chromosomal gains or losses and lack of focal gene amplifications and CNV unstable group (CNV-U) with >3 large chromosomal gains/losses and/or focal amplifications. The associations between MR features, rCBV, CNV abundance and time to progression were assessed. Tumor relative cerebral blood volume estimates (rCBV) were obtained using DSC T2* perfusion analysis.

Results:

There were 9 (50%) CNV-S and 9 (50%) CNV-U IDH-mutant astrocytomas. CNV-U tumors showed larger mean tumor size (P = .004) and maximum diameter on FLAIR (P = .004), and also demonstrated significantly higher median rCBV than CNV-S tumors (2.62 vs 0.78, P = .019). CNV-U tumors tended to have shorter time to progression although without statistical significance (P = .393).

Conclusions:

Larger size/diameter and higher rCBV were seen associated CNV-U astrocytomas, suggesting a correlation of aggressive imaging phenotype with unstable and aggressive genotype in IDH-mutant astrocytomas.

Keywords: Glioma, genomics, imaging, radio-genomics

INTRODUCTION

Gliomas are a heterogeneous group of tumors with diverse aggressiveness and survival, and are traditionally classified into 4 grades based predominantly on histopathological features according to the World Health Organization (WHO) classification. It is now clear that the correlations between genetics and survival are more robust than traditional morphology, and the 2016 CNS WHO classification, an update of the 2007 4th Edition of CNS WHO classification, led to integration of some genomic markers into glioma classification [1]. One of the most significant and robust prognostic genetic markers in glioma recently discovered are recurrent mutations in the isocitrate dehydrogenase 1 (IDH1) and IDH2 genes. IDH1/2-mutant (henceforth referred to as IDH-mutant) gliomas have a better prognosis as compared with their wild-type (IDHwt) counterparts, independent of WHO grade [2–5]. However, tumorigenesis in glioma is a complicated polygene-related process which has not been fully elucidated, and patients with gliomas may have diverse prognosis even among the similar histological and genetic subgroups.

Genomic instability with copy number variations (CNVs), defined as structural variations involving a large-scale (>1 kilobase) genomic DNA changes [6], have been identified as potential susceptibility loci for a range of diseases including cancer in recent studies [7–10]. It has been reported that high level of genomic instability with CNV abundance is associated with poor survival across seven distinct cancer type, including low-grade gliomas [11]. A recent study demonstrated malignant progression of IDH-mutant gliomas to glioblastomas (GBM) was associated with increased genomic instability [12], and a study using multiple large datasets showed that using CNV abundance as a grading algorithm performed better in predicting overall survival among patients with IDH mutant astrocytoma than the 2016 CNS WHO [13]. Jamshidi et al. has constructed an MR imaging, mRNA, and CNV radiogenomic association map of GBM patients, and they demonstrated the association between gene dose changes and MR features in GBM [14]. However, the correlation between MRI features and genomic instability in IDH-mutant gliomas has not previously been reported. The purpose of this study was to investigate the association of the extent of genome-wide CNV abundance in IDH-mutant gliomas and specific MRI features to enhance the knowledge of potential causal links between imaging phenotypes and copy number variation/genomic instability.

METHODS

We retrospectively reviewed WHO grade II-IV IDH-mutant, 1p/19q non-codeleted gliomas (IDH-mutant astrocytomas) in which surgical resection and CNV plots had been performed at our institute from March 2004 to September 2016. The CNV plots were derived using data generated from Illumina 850k EPIC DNA methylation array analyses (MethylationEPIC BeadChip 850k microarray, Illumina, San Diego, USA) performed on tumor specimens. DNA was extracted from formalin-fixed paraffin embedded tumor specimens. Areas with the highest available tumor content were selected. Extraction was carried out using the automated Maxwell system (Promega, Madison, USA). DNA methylation was analyzed by the Illumina EPIC Human Methylation array, which assesses 850,000 CpG sites, according to the manufacturer’s instructions at the NYU Molecular Pathology laboratory as described previously [15]. Molecular diagnosis of Astrocytoma, IDH mutated was confirmed utilizing cloud-based DNA methylation classifier as descried previously [16]. The copy number analysis was performed using conumee [17] as shown previously [18, 19]. Copy number plots were visually inspected. For each case we calculated the number of large chromosomal gains or losses, defined as at least 1 copy number gain or loss involving at least 50% of a chromosomal arm. Based on our previous work [18, 20], gliomas were then divided into two groups based on the degree of genome-wide CNV abundance into CNV stable (CNV-S) with </=3 chromosomal gains or losses and lack of focal gene amplifications and CNV unstable (CNV-U) with >3 large chromosomal gains/losses and/or focal gene amplifications. After excluding 8 cases due to unavailability of pre-operative imaging, a total of 22 cases (9 women, 13 men; median age 38.8 years, range 24.5 – 81.2 years) were included in the study.

MR images were acquired during routine clinical work-up using a 1.5T or 3T MR imaging system, and included pre-and postcontrast T1WI, T2WI, FLAIR, DWI, SWI and DSC T2* perfusion MRI. For each patient, imaging parameters were scored based on VASARI (Visually AcceSAble Rembrandt Images) MR feature set [21] with modification, including (1) location of tumor geographic epicenter, side of tumor center (right, left or center/bilateral), (2) enhancement quality (none, minimal/mild or marked/avid), (3) proportion of non-enhancing area (< 50% or > 50%), (4) thickness of enhancing margin (no contrast enhancement/minimal or thick/solid), (5) enhancing margin (no contrast enhancement, completely, or mixed), (6) necrotic tissue (absence or presence), (7) relative proportion of edema (none/minimal, moderate/marked of entire signal abnormality), (8) cysts (defined as well defined and rounded regions of very bright T2W signal and low T1W signal essentially matching CSF signal intensity, with very thin, regular, smooth, non-enhancing or regularly enhancing walls, scored as absence or presence), (9) enhancing tumor crossing midline (no contrast enhancement, absence or presence), (10) satellites lesion (absence or presence), (11) tumor heterogeneity (homogenous, heterogeneous, or mixed), (12) number of lesions (solitary or multiple, while multiple lesions are defined separate lesions without continuity of the T2/FLAIR envelope), (13) T1 to FLAIR area ratio (defined as T1 equal to FLAIR when pre-contrast T1 signal intensity abnormality is approximates size of FLAIR abnormality, or T1 less than FLAIR when the size of T1 abnormality is moderately smaller than the surrounding FLAIR envelope), (14) diffusion (whether there are foci within the entire lesion which refers to total T2/FLAIR envelope, including edema, non-enhancing and enhancing tumor and necrosis, demonstrating signal intensities below or the same as that of normal-appearing white matter on ADC map), (15) intratumoral hemorrhage (absence or presence), (16) cortical involvement (by non-enhancing or enhancing tumor or both), (17) leptomeningeal reaction (absence or presence), (18) ependymal contact (none, by non-enhancing or enhancing tumor), (19) calvarial remodeling (absence or presence), (20) maximal diameter of the tumor on a single axial FLAIR image that demonstrating the largest cross sectional area, and (21) size of tumor (the products of maximal diameter and its perpendicular diameter of the tumor on a single axial FLAIR image that demonstrating the largest cross sectional area). All MR parameters and measurement were reviewed in consensus by two trained neuroradiologists (CCW & LN).

Tumor blood volume estimation was also obtained using DSC T2* perfusion analysis from manually placing 4 regions of interests within the highest perfusion areas including enhancing and non-enhancing segments of each tumor, utilizing Olea Sphere software (Olea Medical, LaCiotat) and obtaining a mean of these 4 ROIs. Relative CBV (rCBV) for each tumor was generated by dividing the mean CBV of each tumor with that of normal appearing white matter obtained from contralateral hemisphere.

Tumor progression assessment by follow-up MR imaging was based on Response Assessment in Neuro-Oncology (RANO) criteria [22, 23]. The starting point of time to progression (TTP) was the day of surgery, and the end point was the day of MR imaging showing tumor progression.

The correlation of MR imaging features and mean differences in rCBV with CNV was determined by independent t-test or ANOVA, and Chi-square tests. Optimal cut-off value of each rCBV to discriminate CNV-S and CNV-U was obtained from receiver operating characteristic curve (ROC) analysis. Probabilities of TTP were estimated and compared using Kaplan-Meier graphs and log-rank test. Two-sided P-values less than 0.05 were considered to indicate statistical significance for all tests. All statistics analyses were performed using SPSS 23.0 for Windows software (SPSS, Chicago, IL, USA).

RESULTS

There were 9 CNV-S and 9 CNV-U IDH-mutant astrocytoma in the current study. Of the 17 cases with available WHO grading, there were 9 (100%) lower grade gliomas (7 grade II and 2 grade III tumors) and no grade IV GBM in CNV-S group, while there were 4 (50%) lower grade gliomas (only one grade II and 3 grade III) and 4 (50%) grade IV GBM in CNV-U group. The tumors in CNV-S group were more likely to be lower grade glioma than the CNV-U group (P = .029). All tumors were solitary and had area of abnormal signal intensities on T1WI similar to that on FLAIR, and on the other hand, no tumor had T2 or FLAIR abnormal signal intensities across midline, leptomeningeal reaction nor calvarial remodeling. In our analysis, men were more likely to have CNV-U than women (7 out of 10 men [70%] vs. 2 out of 8 women [25%]), but the differences did not reach statistically significance (P = .153). Mean maximum diameter and mean tumor size were significantly smaller in CNV-S than in CNV-U tumors (P = .004). None of the tumors with CNV-S had intratumoral cysts while most tumors with CNV-U (6 out of 9, 67%) had intratumoral cyst (P = .009). All tumors with CNV-S had larger proportion (>50%) of non-enhancing area, while two third tumors with CNV-U did, and the difference didn’t reach statistical significance (P = .169). Tumors with CNV-S were more likely to show no enhancement, and tumors with CNV-U were more likely to have necrosis and foci of low or equal signal intensities on ADC map, but the difference didn’t reach statistical significance (Table 1).

Table 1.

MRI features and P values of CNV-S and CNV-U IDH-mutant gliomas

MRI features CNV-S CNV-U P
Max diameter (mean ± std) mm 48.3 ± 12.7 72.2 ± 17.6 0.004*
Size (mean ± std) cm2 17.7 ± 7.7 36.2 ± 14.3 0.004*
No enhancement (N [% in subgroup]) 7 (77.8%) 3 (33.3%) 0.153
> 50% nonenhancing area (N [% in subgroup]) 9 (100%) 6 (66.7%) 0.206
Presence of necrosis (N [% in subgroup]) 1 (11.1%) 4 (44.4%) 0.294
Presence of ADC foci ≦ normal-appearing WM (N [% in subgroup]) 2 (22.2%) 6 (66.7%) 0.153
rCBV (median) 0.78 2.62 0.019*
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*

Statistically significant (P < 0.05)

All 18 cases had DSC T2* perfusion data available for analysis. CNV-S IDH-mutant astrocytomas demonstrated significantly lower median rCBV (0.78) than CNV-U tumors (2.62, P = .019) (Figures 1, 2). By ROC, the optimal cut-off value of 1.05 for rCBV could differentiate CNV-S and CNV-U IDH-mutant astrocytomas, with the best combination of sensitivity (77.8%) and specificity (88.9%), and area under the curve was 0.827 (95% confidence interval: 0.607 – 1, P = .019). There were no significant differences of the remaining MR features and CNV sub-groups.

Fig. 1. CNV-S IDH-mutant glioma.

Fig. 1

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a Axial post-contrast T1WI and b FLAIR images showing no contrast enhancement or necrosis in a small tumor measuring 6.9 cm2. c rCBV map showing low rCBV of 0.8

Fig. 2. CNV-U IDH-mutant glioma.

Fig. 2

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a Axial post-contrast T1WI and b FLAIR images showing contrast enhancement and necrosis in a larger tumor measuring 44.3 cm2. c rCBV map showing much higher rCBV of 5.7, suggesting an aggressive imaging phenotype

Follow up data was available in 18 cases, and there were 6 tumor progression events during the follow-up period (median follow-up 16.1 months, range 2.2–70.8 months). There were slightly but not significantly more tumor progression events in CNV-S group in the follow up period (44.4%) than in the CNV-U group (33.3%) (P= 1). However, there was a trend for shorter TTP for the CNV-U group than that for CNV-S group (mean TTP 23 months vs. 43.3, respectively), although the difference did not reach statistical significance (P = .393, Figure 3).

Fig. 3. Kaplan-Meier curves of time to progression stratified by CNV-S and CNV-U gliomas.

Fig. 3

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DISCUSSION

There has been increasing evidence that genomic instability is associated with poor prognosis in genetically otherwise favorable tumors [20,24]. To date, several studies have revealed associations between conventional and advanced MR imaging features and molecular characteristics, mainly focusing on IDH mutational and 1p19q codeletion status [25–28]. Because IDH mutation and 1p/19q codeletion are validated prognostic markers in glioma, such non-invasive radiographic correlates have significant value for patient management. However, not all IDH-mutant astrocytomas behave similarly and radiographic features that can distinguish between more and less aggressive tumors within this molecular subgroup have yet to be completely described. In this study, we identified a significant correlation between MR imaging features and CNV status in IDH-mutant astrocytic (1p/19q non-codeleted) gliomas.

Recently some of the differences between individual gliomas have been attributed to genomic instability in these tumors. A recent study of TCGA glioblastoma dataset indicated that there was weak correlation between imaging features and copy number variation of glioblastoma [29]. In that study, alterations in CDKN2A, EGFR and PDGFRA were used to sub-classify tumors, and copy number variation was classified into 0–5%, 6–33% and 34–95% according to percentage of total abnormal tissue. In current study, we focused only on IDH-mutant astrocytomas and divided them into CNV-S and CNV-U groups based on genome-wide copy number variance abundance. We demonstrated that IDH-mutant astrocytomas with more genetic instability (CNV-U) have imaging features consistent with more aggressive tumors, e.g. larger size and necrosis seen on conventional MRI. We also demonstrated that these CNV-U astrocytomas show higher blood volume compared to CNV-S astrocytomas, suggesting more cellular and more vascular tumors. Therefore, we have identified MRI correlates of higher CNV abundance, which is robustly associated with worse prognosis in IDH mutant astrocytomas [13].

Perfusion imaging is able to characterize and quantify tumor blood volume, which is a surrogate marker for vascular proliferation, tumor microvascular density [30, 31] and hence, tumor angiogenesis [32, 33]. As the degree of vascular proliferation is one of the critical factors in histopathologic grading and malignant potential, there is significant evidence from the literature about the perfusion imaging using tumor blood volume as an imaging surrogate marker of glioma grading, tumor behavior and patient prognosis. Barajas et al. showed that regions with high blood volume express gene profiles associated with angiogenesis and tumor aggressiveness in glioblastoma [34]. Jain et al. also demonstrated that perfusion parameters could be correlated with genes regulating angiogenesis in glioblastomas and hence, suggesting a genomic basis for these perfusion parameters [35]. By using DSC MR perfusion imaging, Law et al. demonstrated that the prognosis for patients with low-grade but highly perfused tumors was worse than that in patients with high-grade tumors with low perfusion [33]. Another study by Jain et al. demonstrated that high-grade gliomas with higher tumor blood volume and higher tumor leakiness have poor overall survival compared with those with lower perfusion parameters [36]. These data indicate that perfusion parameters may contain prognostic information in gliomas beyond WHO histological grade and some of this additional physiologic information could be traced back to genomic markers. Taking the previously published work one step further, our current study demonstrates a correlation of the degree of genomic instability in IDH-mutant astrocytomas with tumor blood volume. CNV-U IDH-mutant astrocytomas demonstrated higher rCBV, corresponding to increased tumor vascularity and IDH-mutant astrocytomas with greater CNV abundance have worse survival [13].

Diffusion-weighted imaging is another clinically useful imaging sequence, which has shown utility as an imaging biomarker for pre-operative grading of gliomas and treatment response in gliomas [37–43]. Areas showing decreased signal intensity in diffusion-weighted imaging (restricted diffusion) are suggested to represent foci with decreased interstitial water mobility, and hence, increased cellularity in intracranial tumors [40, 41, 43]. Many prior studies have demonstrated an inverse correlation between ADC and tumor cellularity as well as tumor grade [40, 44–45]. We found that IDH-mutant astrocytomas with CVN-U tended to have foci of restricted diffusion, which suggests increased cellularity in these genomically more unstable astrocytomas, although the statistics did not show significant difference.

Contrast enhancement in brain MR imaging is primarily caused by blood-brain barrier breakdown, which is presumed to be related to tumor infiltration and angiogenesis in glioma. It is known that the enhancement pattern on MRI correlates with different subtypes of gliomas, and also correlates with different gene expression, mainly genes associated with angiogenesis [46–48]. According to literature, about one-third of nonehancing gliomas are malignant, whereas 26–46% of the low-grade gliomas shows contrast enhancement. Although the use of contrast enhancement to discriminate low grade from malignant glioma is not satisfactory, in a study evaluating image-guided biopsy specimens, WHO grade showed statistically significant correlation with contrast enhancement [49]. Previous studies have shown that the portion of contrast enhancement on MRI was associated with survival of glioblastoma patients [29, 50]. However, we did not find significant differences in enhancing pattern and proportion of non-enhancing area between CNV-U and CNV-S astrocytomas to establish enhancement pattern correlates with geomic instability levels. One possible reason is that the overall sample size in current study is relatively small. Another possible reason is that the mechanism by which CNV status affects tumor behavior and prognosis is not completely reflected in tumor angiogenesis or blood-brain-barrier breakdown, which enhancement on MRI mainly reflects.

Our study is limited due to small sample size as well as lack of correlation with image guided specimens. The lack of statistical significance in our TTP analysis is likely due to the small number of patients and relatively short follow up time for IDH mutant gliomas. Due to the small sample size, our findings of associations may have limited prognostic value and would benefit from external validation. However, our findings support mounting evidence of the importance of genomic instability in IDH-astrocytoma patients and also identify non-invasive MRI correlates of an important prognostic biomarker. CNV abundance has recently been validated as a robust prognostic biomarker in IDH-mutant astrocytomas [13], a molecularly well-defined group of gliomas known to harbor a subset of more aggressive tumors. Determination of CNV abundance is a specialized assay that is not available in the majority of clinical settings, however, a non-invasive and clinically available imaging surrogate of CNV abundance could assist in management decisions in these patients, as we show that MR imaging may identify the small subset of IDH mutant astrocytomas that behave more aggressively than would be predicted based in their IDH mutation status.

CONCLUSIONS

MRI features, including larger tumor size and higher tumor blood volume, were observed more frequently in IDH-mutant astrocytomas with higher genomic instability. Hence, for IDH-mutant astrocytomas we establish a genomic basis for some commonly used imaging features associated with more aggressive behavior. Importantly, our results go beyond the currently established genomic markers such as IDH mutational status and identify non-invasive imaging biomarkers that correlate with genomic markers associated with worse prognosis even within IDH mutant astrocytomas.

Acknowledgement

The molecular profiling was supported in part by a grant from the Friedberg Charitable Foundation (to M.S.).

Funding: The molecular profiling part of this study was supported in part by a grant from the Friedberg Charitable Foundation (to M. Snuderl).

Footnotes

Compliance with ethical standards

Conflict of Interest: D. G. Placantonakis had unrelated grants from NIH/NINDS R01 NS102665, NY State Stem Cell Program (money paid to the institution). All other authors declare that they have no conflict of interest.

Ethical approval:

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent:

This is a retrospective study. For this type of study formal consent is not required.

Conflict of interest statement

We declare that we have no conflict of interest.

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