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Multidimensional scaling of diffuse gliomas: application to the 2016 World Health Organization classification system with prognostically relevant molecular subtype discovery
© The Author(s). 2017
Received: 13 May 2017
Accepted: 13 May 2017
Published: 22 May 2017
Recent updating of the World Health Organization (WHO) classification of central nervous system (CNS) tumors in 2016 demonstrates the first organized effort to restructure brain tumor classification by incorporating histomorphologic features with recurrent molecular alterations. Revised CNS tumor diagnostic criteria also attempt to reduce interobserver variability of histological interpretation and provide more accurate stratification related to clinical outcome. As an example, diffuse gliomas (WHO grades II–IV) are now molecularly stratified based upon isocitrate dehydrogenase 1 or 2 (IDH) mutational status, with gliomas of WHO grades II and III being substratified according to 1p/19q codeletion status. For now, grading of diffuse gliomas is still dependent upon histological parameters. Independent of WHO classification criteria, multidimensional scaling analysis of molecular signatures for diffuse gliomas from The Cancer Genome Atlas (TCGA) has identified distinct molecular subgroups, and allows for their visualization in 2-dimensional (2D) space. Using the web-based platform Oncoscape as a tool, we applied multidimensional scaling-derived molecular groups to the 2D visualization of the 2016 WHO classification of diffuse gliomas. Here we show that molecular multidimensional scaling of TCGA data provides 2D clustering that represents the 2016 WHO classification of diffuse gliomas. Additionally, we used this platform to successfully identify and define novel copy-number alteration-based molecular subtypes, which are independent of WHO grading, as well as predictive of clinical outcome. The prognostic utility of these molecular subtypes was further validated using an independent data set of the German Glioma Network prospective glioblastoma patient cohort.
For nearly a century, classification of primary brain tumors has been based solely upon histomorphologic characteristics and presumed histogenesis of neoplastic cell types [2, 3]. Early classification systems for diffuse gliomas relied upon evaluating the histological subtype as either astrocytoma or oligodendroglioma, with further histological parameters such as nuclear atypia, mitotic figures, microvascular proliferation, and necrosis, to indicate aggressiveness, or higher-grades of gliomas [2, 20, 34, 38]. Today the most commonly used standard criteria for classifying gliomas is set forth by the World Health Organization (WHO). Originally presented in 1979, the WHO classification of central nervous system (CNS) tumors has been revised in 1993, 2000, 2007, and most recently in 2016 . Prior to the 2016 classification system, WHO glioma classification was based solely upon histopathological criteria, which contains an inherent amount of interobserver variability in interpretation, leading to less predictive clinical outcomes [10, 11, 13, 21, 24, 42]. More recently, large scale genomic efforts such as those from The Genome Cancer Atlas (TCGA) have led to a considerable increase in the identification and understanding of recurrent genetic and epigenetic alterations found in diffuse gliomas, WHO grades II–IV, and have helped to define molecular and prognostic subclasses of these tumors [7–9, 30, 31, 43, 45, 46]. Such molecular alterations include mutations in the isocitrate dehydrogenase (IDH) 1 and 2 genes, codeletion of chromosome arms 1p and 19q, or hypermethylation of the gene encoding O-6-methylguanine DNA methyltransferase (MGMT) [4, 8, 9, 12, 22, 31, 32, 46]. To reflect the understanding of genetic and genomic contributions to glioma biology, the 2016 WHO classification introduced revised classification criteria to incorporate traditional histopathology and molecular signatures into ‘integrated’ diagnostic entities [23, 25, 26, 33]. Special attention has also been made in this new version to conceptually restructure glioma classification to consider all diffuse gliomas (astrocytomas and oligodendrogliomas) under the common header of “diffuse astrocytic and oligodendroglial tumors” . Within this category, molecular alterations help to drive WHO grade II and III diagnoses, and diagnostic entities include diffuse astrocytoma designated as IDH-mutant or IDH-wildtype; anaplastic astrocytoma designated as IDH-mutant or IDH-wildtype; Oligodendroglioma, IDH-mutant, and 1p/19q-codeleted; and anaplastic oligodendroglioma, IDH-mutant, and 1p/19q-codeleted . Not otherwise specified (NOS) categories of these entities are also present, but should be reserved for cases where molecular testing is not possible or where the results are not conclusive . Another change in the WHO grade II and III diffuse glioma category is the discouragement of an oligoastrocytoma diagnosis . In most instances, oligoastrocytoma can be refined into either the astrocytoma or oligodendroglioma category based upon molecular information [23, 39]. Glioblastoma, WHO grade IV, is now also classified according to IDH status into either glioblastoma, IDH-mutant or glioblastoma, IDH-wildtype . Histological variants of glioblastoma, IDH-wildtype include gliosarcoma, giant cell glioblastoma, and epithelioid glioblastoma. Again, a NOS designation can be applied in cases of insufficient molecular information concerning the IDH mutation status.
A recent analysis of molecular signatures of TCGA diffuse glioma datasets by multidimensional scaling (MDS) showed that there are distinct groups of tumors that cluster together in 2-Dimensional (2D) space . This genomic analysis incorporated data from DNA methylation, DNA copy number alterations (CNAs), and DNA single nucleotide alterations (SNAs). Major genomic factors influencing non-biased large clustering of diffuse gliomas included IDH mutational status, CpG island methylator phenotype (CIMP), polysomy of chromosome 7, monosomy of chromosome 10, and codeletion of chromosome arms 1p and 19q . Regional grouping within larger clusters is also seen in association with specific genetic alterations, such as those in the genes NRAS, HER2, and TP53. Using this multidimensional scaling analysis, a web-based, interactive visualization platform termed Oncoscape  was developed for menu-driven investigation of heterogeneous clinical, pathological, and molecular parameters of various TCGA cancer datasets, including diffuse gliomas. Oncoscape, allows users to compare patients across multiple clinical and genetic features, define trait-based cohorts, align patients and cohorts along a clinical timeline, perform integrated statistical analyses, and create high-quality visualization of integrated clinical and molecular data . In the present study, we leverage Oncoscape to apply 2D multidimensional scaling analysis of TCGA data to visualize relevant molecular characteristics related to the 2016 WHO classification of diffuse glioma entities. In addition, we demonstrated the utility of Oncoscape for exploring genetically defined subgroups within major diffuse glioma categories.
Materials and methods
Oncoscape and TCGA data visualization
TCGA point mutation and copy number data for glioblastomas as well as WHO grade II and III astrocytic and oligodendroglial tumors were downloaded from the University of California Santa Cruz cancer browser (https://genome-cancer.ucsc.edu/). Classic multidimensional scaling (MDS) of molecular data was performed as previously described . The minimal TCGA tumor purity has been reported at 60-80%, which has been shown to be sufficient (>50% tumor purity) for robust detection of cancer-related copy number alterations via GISTIC 2.0 scores , and therefore undersampling of glioma copy number alterations is not likely to affect MDS in this study. Clinical data were obtained from the Genomic Data Commons (GDC) Data Portal from the National Institutes of Health (NIH) . Data were visualized and analyzed using the interactive browser-based platform, Oncoscape (https://oncoscape.sttrcancer.org). Diffuse gliomas from the ‘gliomas (TCGA)’ dataset were visualized in 2D space with available ‘Markers and Patients’ analysis tools. To be consistent with our prior description , the MDS layout utilizing ‘all genes’ was used. The current online version of Oncoscape (https://oncoscape.sttrcancer.org; accessed on May 12, 2017) offers several different 2D layout options, including 13 MDS layouts and 21 principle component analysis (PCA) layouts based upon differing combinations of molecular input (copy numbers alterations, somatic nucleotide mutations, and RNAseq gene expression) and nine different gene sets. Relevant genetic and genomic alterations used for 2016 WHO diffuse glioma classification were queried in Oncoscape . Also of interest for comparison, were previous 2007 histopathologic classifications and WHO grades of TCGA datasets . Three main clusters were identified by MDS, and individual patients were assigned to each based upon IDH1/2 mutational status and the presence or absence of 1p/19q codeletion (Additional file 1: Table S1).
TCGA copy number frequency
Using GISTIC2.0 scores, copy number frequencies of TCGA gliomas were plotted using R software (Version 3.3.2, RProject for Statistical Computing, http://www.r-project.org/) applying the ‘copynumber’ package (http://bioconductor.org/packages/copynumber) using 0.5 and −0.5 as thresholds.
German glioma network validation set
Glioblastoma sample molecular data (n = 284) from the German Glioma Network (http://www.gliomnetzwerk.de) were collected along with survival and gene methylation data as previously described . Copy number alterations of individual CpG sites were evaluated based on the R package ‘conumee’ (http://bioconductor.org/packages/conumee) applying an adapted algorithm for baseline-correction. For evaluation of chromosomal segments, the median of the states of the corresponding probes was computed. Gains and losses were called using thresholds at −0.1 and 0.1 on a log2-scale as cutoff. For calling of amplifications and homozygous deletions in genes of interest thresholds at 0.6 and −0.6 were used. Segment start- and end-positions refer to reference genome GRCh37/hg19. Results are restricted to chromosomes 1, 14 and 19 as well as CDKN2A, CDK4, and MDM2.
Plots and statistics
Statistical analyses were performed using R software (Version 3.3.2, RProject for Statistical Computing, http://www.r-project.org/). Kaplan-Meier analysis for overall survival was performed using the ‘survival’ package (https://cran.r-project.org/package=survival) with P-values determined by Cox proportional hazards regression. Multivariate Cox proportional hazards models including indicated variables were applied utilizing SPSS statistical software (Version 22.0, IBM). Linear regression was performed using GraphPad Prism software (Version 7.02, https://www.graphpad.com/scientific-software/prism).
Visualizing WHO diffuse glioma classification
Global copy number alterations of MDS clusters
Identification and characterization of cluster-derived molecular subtypes
Validation of cluster-derived molecular subtypes
Visualization of adult diffuse gliomas by multidimensional analysis represented in Oncoscape provides convenient graphical representation of the revised 2016 WHO classification system in 2D space. Three main Oncoscape clusters represent the new risk-stratification classification by IDH mutation and 1p/19q codeletion status. Subgroups based upon additional molecular alterations within these clusters are emerging, but are still yet to be completely defined . The molecular groups are not well-reflected by the 2007 WHO histopathological criteria alone. In fact, each molecular cluster shows variable heterogeneity of histopathological subtypes of diffuse gliomas. This observation reinforces the concept of interobserver variability of diagnoses based upon histology alone, and highlights why integrating molecular alterations in diffuse gliomas increases diagnostic accuracy.
In addition to illustrating the inherent interobserver variability of histopathologic only classification of diffuse gliomas, Oncoscape also reflects why the diagnosis of (mixed) oligoastrocytoma is discouraged in the new WHO system and how ‘oligoastrocytoma’ easily resolves into either oligodendroglioma or astrocytoma entities [13, 23, 33, 37]. If oligoastrocytoma truly existed as a specific biological entity, these cases would be expected to exist between the IDH-mutant oligodendroglial and astrocytic glioma clusters shown in Fig. 1b, however, no such cases are seen. Furthermore, histopathological ‘oligoastrocytoma’ cases are predominantly evenly distributed and completely embedded within the oligodendroglial tumor and astrocytic glioma/glioblastoma, IDH-mutant clusters, further arguing against oligoastrocytoma as a distinct entity. However, we do note that there are reported rare cases of true oligoastrocytomas with distinct regions of either molecular oligodendroglioma or astrocytoma features , and the WHO does allow for designation of these gliomas as oligoastrocytoma, NOS . However, where such rare molecular biphenotypic cases fall into place on the Oncoscape map has yet to be determined.
A current area for future clarification and refinement in the WHO classification system is that of grading . While molecular alterations were incorporated into the 2016 WHO classification system, grading of diffuse gliomas did not change from the prior 2007 edition [23, 24, 26]. It appears that molecular alterations are strong drivers of clinical behavior, and may be considered as a first stratifier, as IDH-mutant diffuse gliomas clinically behave better than IDH-wildtype diffuse gliomas across all grades [4, 8, 17, 23, 26, 31, 32]. For example, determination of IDH mutational or ‘Oncoscape’ cluster status, may be considered as baseline diagnostic criteria. After the baseline diagnosis is established, cluster-specific grading may be warranted, either histologically or molecularly. On the histologic side, there is some existing literature that supports this type of molecular stratification first, followed by grading. Using a specific mitotic index independent of WHO grading, mitotic counting has been shown to stratify IDH-wildtype, but not IDH-mutant astrocytomas . This suggests that there may be yet to be determined cluster-specific mitotic indices for future WHO grading of diffuse gliomas that better predict clinical outcome. On the molecular side, we present data in this study supporting prognostic heterogeneity within major diffuse glioma clusters, which in some aspects is identified by conventional grading, but is even better identified by an additional set of molecular markers. These results provide evidence of the utility for ‘molecular grading’ within major subgroups of diffuse gliomas.
Along with reflecting changes in WHO classification of diffuse gliomas, some patterns of genetic alterations become readily apparent by this visualization tool, as was the case for the bimodal age distribution of the IDH-mutant, and 1p/19q-codeleted Oligodendroglioma cluster (Fig. 4) and cluster-specific gene amplifications (Fig. 5). For example, the size of dots, representing the amount of genetic alterations in a single sample as shown in Fig. 1, demonstrates the amount of genetic variation within clusters. The IDH-mutant, and 1p/19q-codeleted oligodendroglial tumor cluster is enriched for the smallest points, indicating a genomically stable group, while the astrocytic glioma/glioblastoma, IDH-wildtype cluster has the largest points, representing more frequent mutations and copy number aberrations in this more aggressive type of glioma. The group with an intermediate clinical outcome, the astrocytic glioma/glioblastoma, IDH-mutant cluster, has the most variability in dot size, indicating a genomically more heterogeneous group. Perhaps in diffuse gliomas, the IDH mutational pathway of oncogenesis leads to some genomic stability with 1p/19q codeletion further potentiating relative genomic stability.
With the TCGA dataset, there are current limitations with regards to placement of some diffuse glioma histologic subtypes on the 2D Oncoscape map. For example, it is not clear where gemistocytic astrocytoma, IDH-mutant (a histological variant of diffuse astrocytoma, IDH-mutant) , would be distributed in the astrocytic glioma/glioblastoma, IDH-mutant cluster, or if regional clustering would occur at all. In addition, there are glioblastoma, IDH-wildtype histological variants that have an unknown cluster distribution, either because of lack of annotation or lack of sequencing. These glioblastoma variants include giant cell glioblastoma, gliosarcoma, and epithelioid glioblastoma . They are presumed to exist in the IDH-wildtype cluster, but any specific regionality or grouping is unknown. Gliosarcoma and giant cell glioblastoma tend to lack EGFR amplification, and are therefore likely not associated with EGFR-amplified cases in the IDH-wildtype cluster. Epithelioid glioblastomas harbor the BRAF-V600E mutation in about half of all cases . There are eleven cases of diffuse gliomas with BRAF single nucleotide alterations present exclusively in the IDH-wildtype cluster (not shown), however, they are of various grades (2 WHO grade II; 2 WHO grade III; 4 WHO grade IV; 2 not graded) and appear not to group together. Therefore, it will take increased numbers of properly annotated glioblastoma histological subtypes to resolve their spatial distribution, if any. Along these lines, the current datasets for brain tumors in Oncoscape are limited to the diffuse gliomas from the TCGA. Additional efforts are necessary to characterize other CNS tumor types (other glial, ependymal, glioneuronal, pineal, embryonal, meningeal, etc.) by multidimensional scaling analysis in order to compare molecular genetic structures and make associations between and amongst histologically disparate brain tumor types.
The ability to visualize brain tumor datasets in 2D space relative to pathologic diagnosis and molecular alterations with tools such as Oncoscape, affords the possibility of using such a tool as a reference point for clinical utility. On the clinical side, molecular information derived from a specific patient’s surgical material can be queried in the reference dataset, and survival and treatment strategies of similar patients can inform prognosis and risk-stratification in real-time. This may have further utility in interdisciplinary settings such as neuro-oncology tumor boards, where management decisions are discussed and planned. This has the potential to ease the decision making process and contribute to the application of precision medicine for individual patients. Given the time and financial constraints that can be associated with whole exome/genome 2D mapping for an individual patient, a more limited or targeted analysis may be more prudent. In this setting, cluster-derived molecular subtypes as the ones described in this study (W1–4, M1–3), may be a more appropriate way to risk-stratify in a time-sensitive and cost-effective manner. Beyond brain tumors, Oncoscape can be used to visualize and analyze additional cancer datasets for diagnostic and translational purposes. All 33 TCGA datasets encompassing several organ systems are available for analysis in Oncoscape. Perhaps 2D molecular analysis of this type can help visualize distinct clusters of various other cancers, and help to push the solid tumors from these organ systems to move into integrated diagnoses and risk-stratification, similar to that of neoplasms of the central nervous system.
The study was conceived and designed by PJC and ECH. Acquisition of data was performed by PJC, MZ, LM, HB, AvD, DJ, GR, and MW. Data was analyzed and interpreted by PJC, HGW, and BH. Drafting of manuscript was completed by PJC and ECH. Critical review of manuscript was additionally performed by GR and MW.
G. Reifenberger has received research grants from Roche and Merck (EMD, Darmstadt), as well as honoraria for lectures or advisory boards from Amgen, Celldex and Medac. The authors declare that there are no competing interest.
Sources of support
AvD, BH, GR, and MW represent the German Glioma Network (GGN) which was supported by the German Cancer Aid (Deutsche Krebshilfe, grant number 70-3163-Wi 3). The molecular profiling of glioblastomas from GGN patients was supported by the German Cancer Consortium (DKTK) joint funding project on “Next generation molecular diagnostics of malignant gliomas”. HGW was supported by the Swiss National Science Foundation.
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