Multigene Sets for Clinical Application in Glioma

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  • a From the Brain Tumor Center and the Departments of Neuro-Oncology, Radiation Oncology, and Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas.

Diffuse gliomas are a heterogeneous group of malignancies with highly variable outcomes, and diagnosis is largely based on histologic appearance. Tumor classification according to cell type and grade provides some prognostic information. However, significant clinical and biologic heterogeneity exists in glioma, even after accounting for known clinicopathologic variables. Significant advances in knowledge of the molecular genetics of brain tumors have occurred in the past decade, largely because of the availability of high-throughput profiling techniques, including new sequencing methodologies and multidimensional profiling by The Cancer Genome Atlas project. The large amount of data generated from these efforts has enabled the identification of prognostic and predictive factors and helped to identify pathways driving tumor growth. Implementing these signatures into the clinic to personalize therapy presents a new challenge. Identification of relevant biomarkers, especially when coupled with clinical trials of newer targeted therapies, will enable better patient stratification and individualization of treatment for patients with glioma.

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Release date: April 1, 2011; Expiration date: April 1, 2012

Learning Objectives

Upon completion of this activity, participants will be able to:

  • Evaluate modern techniques of molecular genetic analysis for cancer
  • Analyze potential benefits of whole transcriptome sequencing
  • Distinguish genetic characteristics of mesenchymal and proneural gliomas
  • Describe clinical applications of the genetic analysis of gliomas

The development of gene expression profiles in gliomas provides the opportunity to revolutionize the ways in which tumors are classified, and may lead to individualized treatment algorithms. Until recently, glioblastoma was thought to be a single disease entity confirmed histologically based on objective pathologic hallmarks such as vascular proliferation and necrosis.1 Although histologic classification allowed patients with a single disease to be enrolled into clinical trials, and treatment protocols for these patients to be standardized,2 outcomes were variable and could not be attributed to clinical characteristics alone. Recently, gene expression profiling revealed multiple glioblastoma subtypes of what was originally assumed to be a homogeneous disease. These genetic differences may explain the diverse outcomes among patients with the same histologic diagnosis. Molecular profiling offers the opportunity to improve diagnostic precision and may improve treatment outcomes through directing treatment decisions.

Methods of Genomic Analysis

Although multiple techniques can be used to evaluate gene expression, the most widely used has been the microarray platform. A DNA microarray consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides containing picomoles of a specific DNA sequence, known as probes. Probe-target hybridization is usually detected and quantified by detection of chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The advent of high-throughput microarray technologies using oligonucleotide probes, high-density arrays, and precision manufacturing techniques, such as the photolithographic techniques used to produce the modern Affymetrix GeneChip and similar types of whole-genome arrays, allows for comparison of expression among large numbers of tumors with little evidence of chip-to-chip variation.3,4

The explosion of large genomic datasets generated by this method brought about challenges in the organization of these data into meaningful and useful information (structures). Cluster analysis is a commonly used computational approach for analyzing microarray data, because the number of genes is usually very high compared with the number of samples. Unsupervised clustering is a statistical method that determines correlation in expression among probes to bin targets into groups with similar patterns of expression.5 Experts assume that groups that share molecular signatures often share an underlying biology, and hence this method can potentially identify either new members of pathways or genes that impart a particular phenotype to the sample.

Alternatively, incorporating response variables into the grouping process yields a supervised clustering algorithm and can be used to identify gene signatures for known classes of samples.6 Tumors can be grouped, for example, according to patient outcome (survival vs. death) or known validated molecular changes (e.g., 1p/19q allelic co-deletion vs. non-deletion in oligodendroglioma7). Furthermore, molecular signatures can be identified for different tumor types or observed clinical variables or outcomes. The groundbreaking study by Alizadeh et al.8 profiling diffuse large B-cell lymphoma showed the potential role of gene signatures in the molecular subclassification of tumors that are indistinguishable histologically but have very different clinical outcomes. Extensive work of this type has also been reported for breast cancer, in which gene expression profiling has identified 5 subtypes of tumors, each with different clinical outcomes, and these gene expression signatures are now used as clinical diagnostic tools.9,10

To optimize the reliability of gene expression profiling, several limitations must be overcome. First, to guard against the inevitable false-positives observed in large datasets, validation of the results is necessary.6 Techniques such as the leave-one-out cross-validation method11 are useful to estimate the accuracy of a predictive data set. Data partition into a training set and test or validation set can help verify the accuracy of the analysis. The use of independent sample sets minimizes false-positives resulting from testing a large number of variables in a limited number of samples and is a critical and an often-overlooked aspect of microarray analysis. Furthermore, multiple, large sample sets can generate results from which valid gene signatures can be identified.

A second limitation of the technique is whether complementary DNA arrays provide equally reliable measures of messenger RNA compared with real-time polymerase chain reaction (RT-PCR). This issue underscores the need to verify microarray results through testing gene expression at the individual gene level. Validation of individual gene expression can be performed using techniques such as RT-PCR. Newer methods to detect mRNA expression, such as nuclease protection, represent promising alternatives worthy of further exploration.

To minimize false discovery and improve the diagnostic accuracy of a molecular signature, expression profiling can be performed using whole transcriptome or “deep” sequencing. This approach uses an unbiased survey of all transcripts, improves the accuracy of quantitation, allows for the detection of mutations, and can quantitate low-level transcripts. The emergence of this technology has enabled experts to not only better understand the landscape of the cancer genome but also explore the epigenome through determining mechanisms such as DNA methylation, microRNA expression, and histone modification on a global scale. Gene expression profiling, deep sequencing, and epigenomic profiling are increasingly being combined with techniques that address the amplification or deletion of genetic material, such as array comparative genomic hybridization and single nucleotide polymorphism analysis. Together, these techniques allow cross-validation of novel expression profiles using separate platforms, and further enhance the discovery of biologically relevant molecular changes.

Multigene Sets in Glioma

Phillips et al.12 performed the first study to define multigene sets predictive of glioblastoma prognosis. Analysis of 76 high-grade gliomas separated by survival (less than and greater than 2 years) identified gene clusters that separated into 3 groups—proneural, mesenchymal, and proliferative—based on gene ontology analysis to classify the genes most highly expressed in each group of tumors (Figure 1). Separation of patients into these 3 classes proved to be highly prognostic in independent dataset analyses.

The 2 main groups identified in the original study were the proneural and mesenchymal groups, which were subsequently identified by other groups, including The Cancer Genome Atlas.13 A clinical test was established to predict outcome for patients with glioblastoma based on the strong prognostic significance of the proneural or mesenchymal signature genes, despite the fact that the predictor was developed using a different statistical strategy.14 Although various molecular classification systems have been proposed, the collective evidence suggests that 2 major groups of gliomas exist. One group shows relative overexpression of genes relating to cell motility, extracellular matrix, and cell adhesion (mesenchymal), such as CHI3L1, LGALS3, and FN1, and the second shows relative overexpression of genes associated with neural development (proneural), such as olig2. Further subclassifications or different refinements have been proposed.15,16

The methodology for developing classification systems differ between these studies but the optimal classification has not been determined. With respect to the 2 major subtypes, tumor grade is clearly associated with subtype. Although glioblastomas are found to represent a mix of mesenchymal and proneural subtypes, grade II and grade III diffuse gliomas are almost invariably proneural. Consistent with these findings, proneural glioblastomas tend to have independent clinical and molecular evidence of “secondary” glioblastoma, including younger patient age, higher rates of p53 and IDH1 mutation, and lower rates of epidermal growth factor receptor (EGFR) amplification and chromosome 10 loss.12,16,17

Based on an examination of microarray studies from several independent studies, followed by validation in formalin-fixed paraffin-embedded (FFPE) samples, a 9-gene set was validated with an independent sample set and was shown to be an independent predictor of clinical outcome after adjusting for clinical factors and 06-methylguanine methyl transferase (MGMT) methylation status, a known prognostic molecular marker in glioblastoma.18 Interestingly, this 9-gene profile was also positively associated with the glioma stem-like cell markers CD133 and nestin. Further validation and improvements on this assay are underway.

Global genomic analysis is a promising approach to improve understanding of glioma biology and for the development of multigene predictors. The Cancer Genome Atlas project, sponsored by the NCI, is perhaps the most well-known example of multipleplatform profiling. In this project, a large cohort of glioblastomas (∼ 500) was profiled at the DNA, mRNA, microRNA, and epigenetic (DNA methylation) levels.15 Preliminary analysis of the first 206 glioblastomas validated known genes and pathways previously implicated in glioblastoma, but also identified potential new targets for therapy.15,17 More recently, this work was extended by identifying the previously described proneural and mesenchymal subtypes, and describing additional subtypes (neural and classical). In this dataset, the subtype assignment was not associated with patient outcome, and further work is required to address the clinical significance of these findings (Figure 2).19

Figure 1
Figure 1Figure 1Figure 1

Classification of high-grade gliomas into proneural, mesenchymal, or proliferative subtypes. (A) Oligonucleotide-based gene expression profiles were obtained in a set of WHO grade III and IV gliomas and those genes most highly correlated with survival used to classify the tumors by hierarchical clustering. A unique expression pattern emerges separating the tumors into 3 distinct molecular subtypes. Genes with high expression are shown in red, whereas those with low expression are shown in green. Gene ontology analysis (i.e., analysis of gene function) showed that the highly expressed genes in each tumor group have either a neurodevelopmental (proneural), mesenchymal, or proliferative function. These subtypes show statistically significant associations with patient outcome in both grade III and IV glioma (B) and in an independent dataset consisting only of grade IV glioblastoma with necrosis (C). Adapted from Phillips HS, Kharbanda S, Chen R, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 2006;9:157–173; with permission from Elsevier.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 9, 4; 10.6004/jnccn.2011.0040

A significant departure from the previously mentioned glioma classification schemes, which have relied on mRNA expression or genetic alterations, has been the recent analysis of epigenetic changes, namely DNA promoter CpG island methylation. In the first study of this modality, analysis of methylation profiles with 272 GBMs from The Cancer Genome Atlas project found that a distinct subset of samples displayed concordant hypermethylation at a large number of loci, thus demonstrating a glioma-CpG methylator phenotype (G-CIMP; Figure 3).20 This study showed that G-CIMP tumors are associated with IDH1 somatic mutations, belong to the proneural subgroup, display distinct copy-number alterations, and are more prevalent in low-grade gliomas. Furthermore, patients with G-CIMP tumors were found to be younger at diagnosis and experienced significantly improved outcome across all grades.

Figure 2
Figure 2

The Cancer Genome Atlas (TCGA) classification of glioblastoma based on gene expression based and correlation to known genetic alterations and patient outcome. Gene expression data from 202 glioblastoma samples were used to identify 4 molecular subtypes, the previously reported proneural and mesenchymal types (n = 52), and 2 additional subtypes: neural and classical. For the subset of 116 cases with both mutation and copy number data, correlations to TP53, IDH1, PDGFRA, EGFR, NF1, and CDKN2A were reported. (A, B, C, D) Kaplan-Meier graphs showing the correlation between molecular subtype and survival based on intensity of therapy. In the molecular classification reported by the TCGA, only the classical and mesenchymal groups showed a significant association with survival.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 9, 4; 10.6004/jnccn.2011.0040

Gene expression profiling may provide the opportunity to identify novel targets and pathways involved in glioblastoma pathogenesis and progression. The use of multiplatform genomic analyses has provided several novel insights into glioma biology, including the potential role of ERBB2, NF1, and TP53, and mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1.15 This work has provided a network view of multiple pathways involved in glioma biology and will improve the ability to develop novel treatments to target these pathways.

However, an important limitation of gene expression profiling is the inability to account for the heterogeneous population of nontumor cells found within the tumor microenvironment. Up to 40% of a glioblastoma tumor mass may contain nontumor cells, such as bone marrow–derived cells, reactive astrocytes, fibroblasts, and microglia, which may influence gene expression levels and play an important role in influencing the genes expressed by tumor cells. Furthermore, identification of individual or sets of genes does not necessarily define the role of these specific markers in the biology of the tumor. That is, the observed gene expression levels may indicate changes in pathways, with less importance placed on the role of the individual genes in tumor biology. Interestingly, 2 commercialized multigene expression profiles commonly used in breast cancer (MammaPrint and Oncotype DX) only share one common gene, although the remaining gene changes are all related to 3 main pathways central to breast cancer biology. These findings suggest that individual gene changes may be less important than the pathways that these groups of genes represent, and that tumors have a multitude of redundant activated pathways to maintain the malignant phenotype. Finally, interactions between multiple genes and the proteins that are expressed may be important, although it is uncommon for multigene interactions to be validated in the laboratory.

Figure 3
Figure 3

Identification of the CpG island methylator phenotype (CIMP) in glioblastoma (GBM). DNA methylation data from 91 tumors profiled by The Cancer Genome Atlas (TCGA) was subjected to unsupervised clustering. Three distinct methylation clusters were identified, with cluster #1 (red in top bar) designated as G-CIMP+ because of the high-frequency of methylation. The genes whose promoters are methylated in that cluster constitute the G-CIMP signature. The gene expression classification and gene mutation status (EGFR, IDH1, NF1, PTEN, and TP53) is shown above each tumor signature. G-CIMP+ tumors (labeled below the heatmap) were correlated with IDH1 mutations and exhibited a significantly improved survival. Controls include fully methylated DNA (by SssI methyltransferase treatment) and whole-genome amplified (WGA) unmethylated DNA. Adapted from Noushmehr H, Weisenberger DJ, Diefes K, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 2010;17:510–522; with permission from Elsevier.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 9, 4; 10.6004/jnccn.2011.0040

Clinical Applications

One of the most important initiatives in cancer therapy today is the development of treatments that are personalized or tailored to the biology of individual patients and their cancer. The development of predictive markers (i.e., biomarkers that are predictive of a response to a particular therapy) is critical to the development of personalized therapy. Although several predictive markers have been proposed for glioblastoma, including MGMT methylation, EGFRvIII/PTEN, and 1p/19q status, these markers are associated with prognosis and do not predict response to individual therapies.

To date, multigene profiles in glioma have only been shown to be prognostic and to correlate with more or less favorable tumor biology. However, a strong rationale exists for using multigene predictors, and ultimately these have the potential to directly improve treatment decisions, particularly in the setting of a randomized clinical trial. For example, a subset of patients with glioblastoma, such as those whose tumors have a proneural phenotype, are known to have an improved outcome. A multigene set that can accurately predict patients most likely to do well could be used to balance the numbers of those with good and bad prognosis in a clinical trial so that the benefit of the treatment itself is not overestimated.

In glioblastoma, a multigene profile compatible for FFPE samples is currently being used as one of several stratification factors in a large phase III clinical trial (RTOG 0825).14 Although the efficacy of this gene profile in independently predicting prognosis and outcome is not yet known, RTOG 0825 is an important first step toward integrating molecular markers into the clinical arena.

Finally, perhaps the most significant limitation is the quality of the tumor tissue available for clinical testing. The standard method for tissue collection and preservation is based on the time-honored process of collecting blocks from FFPE tissue, which, although preserving morphology, has significant deleterious effects to the analytes currently used for molecular subclassification (DNA, RNA, and protein). Although efforts to alter fixation or collect unfixed frozen tissue are laudable, these efforts are unlikely to be successful on a larger scale until a clinical need exists to provide the proper incentive. The ability to accurately detect analytes in FFPE material is currently somewhat empiric. For example, although mRNA-based signatures that are detectable in FFPE material are possible to identify, huge variation exists in the reliability of RT-PCR assays of specific RNA species. This variation in reliability seems empiric and is based on a trial-and-error approach.

Methylation-based assays, which are most commonly performed using methylation-specific PCR, represent additional challenges because the required bisulfite conversion step results in additional degradation of already compromised DNA. The reproducibility of MGMT methylation has been questioned, perhaps because of these technical issues.21 One approach that could address this problem is based on the use of a methylation panel of multiple markers that collectively results in a single “call” (e.g., the CIMP assay, interrogating 8 markers with an overall readout of “positive or “negative”), which could improve reliability through minimizing the impact of any single assay. Additional investigations that could result in improvements in the reliability of quantitative assays from FFPE-derived analytes would represent major steps forward for clinical application.

Finally, prospective incorporation of tissue-based analyses into clinical trials will allow additional insights to be gained with respect to personalized therapy. For example, agents that are efficacious against a molecularly defined subset of glioma will only be identified if the samples are tested for relevant biomarkers. In a similar vein, promising data could be gleaned from overall “negative” trials if a subset of patients could be defined for whom the therapeutic agent seems promising. Significant logistical hurdles exist with respect to incorporation of tissue-based analyses in clinical trials.22 However, progress has been slow with respect to therapeutic advances in glioma, and the incorporation of integral and integrated biomarker analyses represents one avenue to accelerate improvements in therapy.

Future Directions

The prognosis for patients with glioblastoma remains poor despite efforts to improve understanding of its eccentric biology. The past several years have shown the potential use of gene expression analysis in improving understanding of glioma biology, the oncogenic pathways that are activated, and the molecular subclassification of these tumors. Armed with this information, experts are better prepared to interrogate the oncogenic pathways in each individual tumor to align treatments that target these pathways. However, until predictive markers of response are identified, prognostic gene sets can provide important information for clinical practice.

For practice to move toward personalized medicine in neuro-oncology, individual tumor expression profiles must be correlated with outcomes to treatment. Currently, few clinical trials in glioma prospectively analyze gene expression changes and response to therapy. For personalized medicine to become a reality, multiple complementary platforms must be integrated into phase II clinical trials for prospective profiling, where the molecular information can be used to stratify patients into risk groups relative to the therapy under study and potentially point to new avenues for treatment options.

EDITOR

Kerrin M. Green, MA, Assistant Managing Editor, Journal of the National Comprehensive Cancer Network

Disclosure: Kerrin M. Green, MA, has disclosed no relevant financial relationships.

CME AUTHOR

Charles P. Vega, MD, Associate Professor; Residency Director, Department of Family Medicine, University of California, Irvine

Disclosure: Charles P. Vega, MD, has disclosed no relevant financial relationships.

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Correspondence: Kenneth D. Aldape, MD, Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 85, Houston, TX 77030. E-mail: kaldape@mdanderson.org

Disclosure: John F. de Groot, MD, has disclosed no relevant financial relationships.

Disclosure: Erik P. Sulman, MD, PhD, has disclosed no relevant financial relationships.

Disclosure: Kenneth D. Aldape, MD, has disclosed no relevant financial relationships.

Supplementary Materials

  • View in gallery View in gallery View in gallery

    Classification of high-grade gliomas into proneural, mesenchymal, or proliferative subtypes. (A) Oligonucleotide-based gene expression profiles were obtained in a set of WHO grade III and IV gliomas and those genes most highly correlated with survival used to classify the tumors by hierarchical clustering. A unique expression pattern emerges separating the tumors into 3 distinct molecular subtypes. Genes with high expression are shown in red, whereas those with low expression are shown in green. Gene ontology analysis (i.e., analysis of gene function) showed that the highly expressed genes in each tumor group have either a neurodevelopmental (proneural), mesenchymal, or proliferative function. These subtypes show statistically significant associations with patient outcome in both grade III and IV glioma (B) and in an independent dataset consisting only of grade IV glioblastoma with necrosis (C). Adapted from Phillips HS, Kharbanda S, Chen R, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 2006;9:157–173; with permission from Elsevier.

  • View in gallery

    The Cancer Genome Atlas (TCGA) classification of glioblastoma based on gene expression based and correlation to known genetic alterations and patient outcome. Gene expression data from 202 glioblastoma samples were used to identify 4 molecular subtypes, the previously reported proneural and mesenchymal types (n = 52), and 2 additional subtypes: neural and classical. For the subset of 116 cases with both mutation and copy number data, correlations to TP53, IDH1, PDGFRA, EGFR, NF1, and CDKN2A were reported. (A, B, C, D) Kaplan-Meier graphs showing the correlation between molecular subtype and survival based on intensity of therapy. In the molecular classification reported by the TCGA, only the classical and mesenchymal groups showed a significant association with survival.

  • View in gallery

    Identification of the CpG island methylator phenotype (CIMP) in glioblastoma (GBM). DNA methylation data from 91 tumors profiled by The Cancer Genome Atlas (TCGA) was subjected to unsupervised clustering. Three distinct methylation clusters were identified, with cluster #1 (red in top bar) designated as G-CIMP+ because of the high-frequency of methylation. The genes whose promoters are methylated in that cluster constitute the G-CIMP signature. The gene expression classification and gene mutation status (EGFR, IDH1, NF1, PTEN, and TP53) is shown above each tumor signature. G-CIMP+ tumors (labeled below the heatmap) were correlated with IDH1 mutations and exhibited a significantly improved survival. Controls include fully methylated DNA (by SssI methyltransferase treatment) and whole-genome amplified (WGA) unmethylated DNA. Adapted from Noushmehr H, Weisenberger DJ, Diefes K, et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 2010;17:510–522; with permission from Elsevier.

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