Integrated molecular analysis suggests a three-class model for low-grade gliomas: a proof-of-concept study

Recent Molecular Advances in Our Understanding of Glioma

Our molecular understanding of glioma has undergone a sea change over the last decade. In this review, we discuss two recent articles that employed whole genome sequencing to subclassify gliomas vis-à-vis known molecular alterations. We further discuss the relevance of these findings vis-à-vis current treatment paradigms.

Outcomes in Reoperated Low-Grade Gliomas

BACKGROUND:

Low-grade gliomas (LGGs) comprise a diverse set of intrinsic brain tumors that correlate strongly with survival. Data on the effect of reoperation are sparse.

OBJECTIVE:

To evaluate the effect of reoperation on patients with LGG.

METHODS:

Fifty-two consecutive patients with reoperated LGGs treated at the University of Washington between 1986 and 2004 were identified and evaluated in a retrospective analysis.

RESULTS:

The average overall survival (OS) for this cohort was 12.95 ± 0.96 years. The overall 10-year survival rate was 57%. The absence of any residual tumor at either the first or second operation was associated with significantly increased OS. Negative prognostic variables for OS included the use of upfront radiation and pathology at recurrence. The average overall progression-free survival to the first recurrence (PFS1) was 6.23 ± 0.51 years. Positive prognostic factors for improved PFS1 included the use of upfront radiation therapy. Variables not associated with differences in PFS1 included the use of upfront chemotherapy, enhancement, pathology, extent of resection, the presence of residual tumor, and Karnofsky Performance Scale score <80. The average overall progression-free survival to the second recurrence was 2.73 ± 0.39 years. Pathology at recurrence was associated with significant differences in progression-free survival to the second recurrence, as was extent of resection at time of first recurrence, and Karnofsky Performance Scale score <80.

CONCLUSION:

This is among the largest studies to assess variables associated with outcome in patients with reoperated LGG. Reresection appears to provide significant benefit, and extent of resection remains the strongest predictor of OS.

The molecular biology of WHO grade II gliomas

The WHO grading scheme for glial neoplasms assigns Grade II to 5 distinct tumors of astrocytic or oligodendroglial lineage: diffuse astrocytoma, oligodendroglioma, oligoastrocytoma, pleomorphic xanthoastrocytoma, and pilomyxoid astrocytoma. Although commonly referred to collectively as among the "low-grade gliomas," these 5 tumors represent molecularly and clinically unique entities. Each is the subject of active basic research aimed at developing a more complete understanding of its molecular biology, and the pace of such research continues to accelerate. Additionally, because managing and predicting the course of these tumors has historically proven challenging, translational research regarding Grade II gliomas continues in the hopes of identifying novel molecular features that can better inform diagnostic, prognostic, and therapeutic strategies. Unfortunately, the basic and translational literature regarding the molecular biology of WHO Grade II gliomas remains nebulous. The authors' goal for this review was to present a comprehensive discussion of current knowledge regarding the molecular characteristics of these 5 WHO Grade II tumors on the chromosomal, genomic, and epigenomic levels. Additionally, they discuss the emerging evidence suggesting molecular differences between adult and pediatric Grade II gliomas. Finally, they present an overview of current strategies for using molecular data to classify low-grade gliomas into clinically relevant categories based on tumor biology.

Genetic changes observed in a case of adult pilocytic astrocytoma revealed by array CGH analysis

BACKGROUND

A palette of copy number changes in a case of adult pilocytic astrocytoma analyzed by Array Comparative Genomic Hybridization (aCGH) is presented. Pilocytic astrocytomas are specific gliomas that are benign and biologically distinct and the molecular mechanisms responsible for their development remain unexplained. The aCGH was performed using SurePrint G3 Human CGH microarrays 4 × 180 K (Agilent Technologies). To ascertain whether some of the aberrations were of constitutive nature, we also analyzed the blood sample from the same patient.

RESULTS

The result of aCGH analysis demonstrated differences in the tumor tissue when compared to normal control on the array and also to autologous DNA from patient's blood. The total number of aberrations found in our case was 41 including 37 deletions and 4 amplifications. Whole chromosomal gains and losses were not observed. Collectively, our results showed three deletions and one amplification at 1p, two deletions at 2q, two deletions at 4q, two deletion at 5q, two deletions at 7p and two deletions at 7q; there were also three deletions at 8q, one deletion at 9p, one deletion at 10p, three deletions and one amplification at 10q. Chromosome 11 showed two deletions at 11p, while there was one deletion at 12p and one at 12q. Four deletions at 14q; two deletions at 15q, one amplification at 17q and one deletion at 17q; one deletion at 18p, two deletions at 22q and finally one deletion at Xp and one deletion and one amplification at Xq. Among the signaling pathways, olfactory transduction, Fc gamma R-mediated phagocytosis and p53 signaling pathway showed significant enrichment ascertained by gene ontology (GO) analysis using the DAVID software.

CONCLUSIONS

Our aCGH analysis is bringing subtle genomic alterations thus broadening genetic spectrum of adult pilocytic astrocytoma in order to offer new molecular biomarkers that will help in diagnostics and therapeutic decision-making.

Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas

BACKGROUND

Paediatric low-grade gliomas (LGGs) encompass a heterogeneous set of tumours of different histologies, site of lesion, age and gender distribution, growth potential, morphological features, tendency to progression and clinical course. Among LGGs, Pilocytic astrocytomas (PAs) are the most common central nervous system (CNS) tumours in children. They are typically well-circumscribed, classified as grade I by the World Health Organization (WHO), but recurrence or progressive disease occurs in about 10-20% of cases. Despite radiological and neuropathological features deemed as classic are acknowledged, PA may present a bewildering variety of microscopic features. Indeed, tumours containing both neoplastic ganglion and astrocytic cells occur at a lower frequency.

METHODS

Gene expression profiling on 40 primary LGGs including PAs and mixed glial-neuronal tumours comprising gangliogliomas (GG) and desmoplastic infantile gangliogliomas (DIG) using Affymetrix array platform was performed. A biologically validated machine learning workflow for the identification of microarray-based gene signatures was devised. The method is based on a sparsity inducing regularization algorithm l₁l₂ that selects relevant variables and takes into account their correlation. The most significant genetic signatures emerging from gene-chip analysis were confirmed and validated by qPCR.

RESULTS

We identified an expression signature composed by a biologically validated list of 15 genes, able to distinguish infratentorial from supratentorial LGGs. In addition, a specific molecular fingerprinting distinguishes the supratentorial PAs from those originating in the posterior fossa. Lastly, within supratentorial tumours, we also identified a gene expression pattern composed by neurogenesis, cell motility and cell growth genes which dichotomize mixed glial-neuronal tumours versus PAs. Our results reinforce previous observations about aberrant activation of the mitogen-activated protein kinase (MAPK) pathway in LGGs, but still point to an active involvement of TGF-beta signaling pathway in the PA development and pick out some hitherto unreported genes worthy of further investigation for the mixed glial-neuronal tumours.

CONCLUSIONS

The identification of a brain region-specific gene signature suggests that LGGs, with similar pathological features but located at different sites, may be distinguishable on the basis of cancer genetics. Molecular fingerprinting seems to be able to better sub-classify such morphologically heterogeneous tumours and it is remarkable that mixed glial-neuronal tumours are strikingly separated from PAs.

Distinct genomic aberrations between low-grade and high-grade gliomas of Chinese patients

Background

Glioma is a type of tumor that develops in the central nerve system, mainly the brain. Alterations of genomic sequence and sequence segments (such as copy number variations or CNV and copy neutral loss of heterozygosities or cnLOH) are thought to be a major determinant of the tumor grade.

Methods

We mapped genomic variations between low-grade and high-grade gliomas (LGG and HGG) in Chinese population based on Illumina’s Beadchip and validated the results using real-time qPCR.

Results

At the cytoband level, we discovered: (1) unique losses in LGG on 5q, 8p and 11q, and in HGG on 6q, 11p, 13q and 19q; (2) unique gains in the LGG on 1p and in HGG at 5p, 7p, 7q and 20q; and (3) cnLOH in HGG only on 3q, 8q, 10p, 14q, 15q, 17p, 17q, 18q and 21q. Subsequently, we confirmed well-characterized oncogenes among tumor-related loci (such as EGFR and KIT) and detected novel genes that gained chromosome sequences (such as AASS, HYAL4, NDUFA5 and SPAM1) in both LGG and HGG. In addition, we found gains, losses, and cnLOH in several genes, including VN1R2, VN1R4, and ZNF677, in multiple samples. Mapping grade-associated pathways and their related gene ontology (GO) terms, we classified LGG-associated functions as “arachidonic acid metabolism”, “DNA binding” and “regulation of DNA-dependent transcription” and the HGG-associated as “neuroactive ligand-receptor interaction”, “neuronal cell body” and “defense response to bacterium”.

Conclusion

LGG and HGG appear to have different molecular signatures in genomic variations and our results provide invaluable information for the diagnosis and treatment of gliomas in patients with variable duration or diverse tumor differentiation.

The molecular biology of WHO grade I astrocytomas

World Health Organization (WHO) grade I astrocytomas include pilocytic astrocytoma (PA) and subependymal giant cell astrocytoma (SEGA). As technologies in pharmacologic neo-adjuvant therapy continue to progress and as molecular characteristics are progressively recognized as potential markers of both clinically significant tumor subtypes and response to therapy, interest in the biology of these tumors has surged. An updated review of the current knowledge of the molecular biology of these tumors is needed. We conducted a Medline search to identify published literature discussing the molecular biology of grade I astrocytomas. We then summarized this literature and discuss it in a logical framework through which the complex biology of these tumors can be clearly understood. A comprehensive review of the molecular biology of WHO grade I astrocytomas is presented. The past several years have seen rapid progress in the level of understanding of PA in particular, but the molecular literature regarding both PA and SEGA remains nebulous, ambiguous, and occasionally contradictory. In this review we provide a comprehensive discussion of the current understanding of the chromosomal, genomic, and epigenomic features of both PA and SEGA and provide a logical framework in which these data can be more readily understood.

Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes

INTRODUCTION

Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.

METHODS

We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.

RESULTS

Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect.

CONCLUSIONS

Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that "small" departures from normality in the expression data distributions are analytically-insignificant and that "robust" gene-calling algorithms can fully compensate for these effects.

Towards an integrated molecular and clinical strategy to predict early recurrence in surgically resected non-functional pituitary adenomas

Pituitary adenomas (PA) are histologically benign tumors of the sella that are capable of recurrence following resection. No mechanism exists to predict accurately the risk of recurrence in patients with PA following successful gross total surgical resection. We used microarray-based gene expression profiling to search for genotypically distinct subgroups of non-functional PA associated with the early recurrent phenotype. Rigorous phenotypic controls were used to select four patients with PA with early (<12 months) recurrence and seven patients with non-recurrent PA for comparative molecular analysis. Seventy genes with differential expression patterns between the phenotypic groups were identified, although this required some relaxation of rigid multiple-testing corrections. While some of these genes may therefore represent statistical false discoveries attributable to limited sample size, the CHL1 gene has a differential expression patterns that suggests a potential role as a predictor of recurrence phenotype. Transcriptome-level differences between early recurrent and non-recurrent non-functional PA appear to be subtle, although CHL1 expression may be a candidate for further study as a class discriminator. This suggests two possibilities with regard to recurrence; (i) that microscopic residual disease unidentifiable either by the surgeon or by current neuroimaging strategies may serve as a focus for early recurrence or that biological differences in recurrence phenotypes may occur outside of the transcriptome. These findings are useful for focusing future investigations into the clinical and biological mechanisms of PA recurrence as well as for development of strategies designed to predict prospectively these recurrence phenotypes.

Why is there a lack of consensus on molecular subgroups of glioblastoma? Understanding the nature of biological and statistical variability in glioblastoma expression data

INTRODUCTION

Gene expression patterns characterizing clinically-relevant molecular subgroups of glioblastoma are difficult to reproduce. We suspect a combination of biological and analytic factors confounds interpretation of glioblastoma expression data. We seek to clarify the nature and relative contributions of these factors, to focus additional investigations, and to improve the accuracy and consistency of translational glioblastoma analyses.

METHODS

We analyzed gene expression and clinical data for 340 glioblastomas in The Cancer Genome Atlas (TCGA). We developed a logic model to analyze potential sources of biological, technical, and analytic variability and used standard linear classifiers and linear dimensional reduction algorithms to investigate the nature and relative contributions of each factor.

RESULTS

Commonly-described sources of classification error, including individual sample characteristics, batch effects, and analytic and technical noise make measurable but proportionally minor contributions to inconsistent molecular classification. Our analysis suggests that three, previously underappreciated factors may account for a larger fraction of classification errors: inherent non-linear/non-orthogonal relationships among the genes used in conjunction with classification algorithms that assume linearity; skewed data distributions assumed to be Gaussian; and biologic variability (noise) among tumors, of which we propose three types.

CONCLUSIONS

Our analysis of the TCGA data demonstrates a contributory role for technical factors in molecular classification inconsistencies in glioblastoma but also suggests that biological variability, abnormal data distribution, and non-linear relationships among genes may be responsible for a proportionally larger component of classification error. These findings may have important implications for both glioblastoma research and for translational application of other large-volume biological databases.

Small intestinal neuroendocrine cell pathobiology: 'carcinoid' tumors

PURPOSE OF REVIEW

Neuroendocrine tumors, particularly small intestinal tumors, also grouped as 'carcinoids', are defined by an increasing incidence and prevalence, a poor response to current therapies, and confusion regarding appropriate models for drug development. Despite these issues, approximately 350 studies were published in the last year.

RECENT FINDINGS

Two sources of confusion are clearly apparent. First, pharmacotherapeutic studies using pancreatic tumor cell lines as models for small intestinal or 'carcinoid' tumor biology are considered appropriate. Second, there is continued inclusion and analysis of pancreatic endocrine tumors with small intestinal neuroendocrine tumors in clinical studies. One highlight of this year is additional data confirming the significant differences between pancreatic tumor cell lines and small intestinal cell lines, the different gene expressions, for example, PAX8, between these two tumor types, and the observations that these two tumors respond differently in clinical trials, for example, to mammalian target of rapamycin (mTOR) inhibitors. Other highlights include delineating the role of the tumor microenvironment in the development of fibrosis and developing a minimum pathology dataset and a prognostic nomogram that may have utility in stratifying patients for clinical studies.

SUMMARY

A number of interesting studies have been published during 2009-2010, but critical areas remain that require resolution. Current data, for the most part, reflect amplification of previously held concepts with modest advances in novel information.

p14ARF promoter region methylation as a marker for gliomas diagnosis

Methylation in the promoter region is one of the mechanisms through which tumor suppressors are inactivated, resulting in tumorigenesis and/or tumor progression. Herein, we studied the methylation status in the promoter region of the p14ARF tumor suppressor gene in 33 brain tissues isolated from glioma patients (astrocytomas) and compared to 12 brain tissues isolated from autopsy donors using methylation-specific polymerase chain reaction (MSP). The correlation between the expression of P14 and P53 was investigated using immunohistochemistry (IHC). The average percentage of methylation in the promoter region of p14ARF gene in brain samples from glioma patients is 39.4%, while 0 from autopsy donors. No difference in the methylation level between low-grade and high-grade gliomas was detected. The methylation status has no correlation with the prognosis in glioma patients. A significant correlation between the expression of mutant form of TP53 and the grade of the glioma was established. Furthermore, there was a negative correlation between methylation of the p14ARF promoter and the expression of the mutant form of TP53. Therefore, our data suggest that methylation in the promoter region of the p14ARF gene may be used as a biomarker for the diagnosis of gliomas.