Brain Tumor Typing and Therapy Using Combined Ex Vivo Magnetic Resonance Spectroscopy and Molecular Genomics
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Loukas G. Astrakas and A. Aria Tzika
Abstract
Contents Abstract...................................................................
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Introduction ............................................................
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MRS ........................................................................ MRS Biomarkers ..................................................... Classification and Statistical Analysis of MRS .......
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Genomics................................................................. Gene Expression Profiling in Cancer ....................... Analysis of Microarray-Based Gene Expression Data ..............................................
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Combining MRS and Genomics ........................... Experimental Design................................................
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Results .....................................................................
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Discussion................................................................
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References ...............................................................
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L.G. Astrakas (*) Department of Medical Physics, Medical School, University of Ioannina, 45110 loannina, Greece e-mail:
[email protected] A.A. Tzika, Ph.D NMR Surgical Laboratory, Department of Surgery, Massachusetts General Hospital and Shriners Burns Institute Harvard Medical School, 02114 MA, Boston
A novel approach was developed that combines biomarkers detected with magnetic resonance spectroscopy (MRS) and molecular genomics to improve the typing and prognostication of biospecimens in clinical medicine. Metabolite and genome wide profiles from 55 biopsies from subjects with brain tumors were analyzed with a classification algorithm that produces unique tumor fingerprints. We found that the fusion of 15 gene expressions and 15 MRS metabolites were able to distinguish tumor categories and predict survival better than when either dataset was used alone. Our approach improves the typing and understanding of the complexity of human brain tumors, generates testable hypotheses regarding neoplasia and promises to guide human brain tumor therapy. Our results further elucidate the biology of brain malignancy subtypes in brain tumor patients, and increase the overall potential for success of future studies that combine clinical MRI, MRS and MR imaging of gene expression in vivo.
Introduction According to the Central Brain Tumor Registry of the United States (www.cbtrus.org) 24,070 malignant and 40,470 non-malignant new cases of primary brain tumors are expected to be diagnosed in 2012. After leukemia, brain tumors are
M.A. Hayat (ed.), Tumors of the Central Nervous System, Volume 12, DOI 10.1007/978-94-007-7217-5_13, © Springer Science+Business Media Dordrecht 2014
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the second leading cause of cancer-related deaths in children under age 20 and in males of ages 20–39. Worldwide the incidence rate of primary malignant central nervous system (CNS) tumors is 2.6 in females and 3.7 in males per 100,000 person-years. Generally, primary malignant brain tumors are lethal leaving only 30 % of adult patients alive, 5 years after the initial diagnosis. Therefore, early and accurate diagnosis and grading are extremely important for a successful prognosis and an optimum therapeutic intervention. Currently, the golden standard for the brain tumor diagnosis, as described by the 2007 WHO classification scheme, is based on histopathological criteria related to morphological changes, growth pattern and molecular profiles of tissue specimens. However, in many cases (e.g., neuroepithelial tumors, subgroups of diffuse large B-cell lymphoma), tumors often do not follow classic histology and the diagnosis becomes challenging and often controversial among clinicians and neuropathologists (Zarbo et al. 2005). New advanced techniques in the fields of radiology, genetics and molecular biology have been developed to provide additional biomarkers for better tumor typing. The diagnostic utility of these biomarkers lies in their biological relevance with different genetic and metabolic pathways implicated in tumor processes, namely differentiation, proliferation, angiogenesis and apoptosis. Magnetic resonance spectroscopy (MRS) is a powerful tool of biochemical analysis capable to detect and quantify important metabolites implicated in brain tumor pathology. Recent advents in ex-vivo MRS allow subsequent genetic analysis over the entire human transcriptome in the same tissue biopsies. However, highly informative biomarker profiles are difficult to establish, due to current technical limitations and since the small sample sizes of tissue biopsies pose challenges for producing accurate metabolic and transcriptome data. In this chapter it is shown that fusing genomics and MRS results to improved tumor fingerprints that improve typing and prognosis of brain tumors.
MRS Nuclear magnetic resonance (NMR) spectroscopy is a analytical and diagnostic tool that detects and quantifies multiple tissue-specific metabolites of the tissue of interest. In vitro NMR uses biofluids, (e.g., urine, serum, tissue extracts) and provides high quality spectra with several dozen metabolites, but it has been accused with metabolite degradation and incomplete recovery in processed samples (Duarte and Gil 2012). On the other hand, in vivo NMR, also called magnetic resonance spectroscopy (MRS) is totally noninvasive, but compared to in vitro NMR has low sensitivity and poor spectral resolution (Glunde and Bhujwalla 2011). Ex vivo MRS, also called high resolution magic angle spinning (HRMAS) is an established solid state NMR technique that uses intact tissue specimens, (e.g. biopsies) and provides high resolution quality spectra without the destruction of tissue histopathological structures (DeFeo and Cheng 2010). HRMAS combines the analytical strength of in vitro NMR with the non-destructive nature of MRS and allows the quantitative evaluation of tumor morphology, biochemistry, or genetic profile on the same surgical specimen.
MRS Biomarkers Proton MRS has identified several biomarkers of tumor growth and apoptosis (Horska and Barker 2010). Studies of brain tumors using proton MRS have demonstrated: (1) edema and necrosis are associated with reduced or absent n-acetylaspartate (NAA) and total creatine (tCr), (2) increased levels of Cho-containing compounds, possibly due to cell membrane disruption and altered phospholipid metabolism and (3) increased lactate due to metabolic acidosis. Reduced NAA is expected in glial tumors, since NAA is primarily localized in neurons. Therefore, NAA detection within glial tumors corresponds to either partial volume averaging with adjacent normal tissue or tumor infiltration of normal tissue. Since NAA is present in
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Brain Tumor Typing and Therapy Using Combined Ex Vivo Magnetic Resonance…
cell cultures of oligodendroglia progenitors, the NAA in childhood tumors may reflect immature oligodendroglia (Urenjak et al. 1992). A reduction in tCr resonance may indicate cell loss due to necrosis and correspond to exhausted energy reserves resulting from rapid cell proliferation and ischemia. Measurement of tCr may be a valuable independent predictor of tumor response to therapy (Tzika et al. 2001). The Cho peak consists of water-soluble Chocontaining compounds, such as phosphocholine (PCho), glycerophosphocholine (GPC), and free choline, but contains no membrane-bound phosphatidylcholine (PtdCho). In vivo MRS showed that phosphomonoesters (PME), such as PCho and phosphoethanolamine (PEth), are elevated in tumors and rapidly proliferating tissues (Daly and Cohen 1989). Furthermore, PCho and PEth elevation were correlated with increased cell growth or degradation in tumors in humans and animal models and in cell lines. Especially PCho, which can be measured with either phosphorous or proton MRS, is elevated in actively proliferating cells. In vivo proton MRS studies suggest that the Cho peak reflects proliferative activity in gliomas. The PCho concentration was shown to correlate with the number of S-phase cells, and the PCho/GPC ratio correlated with oncogenic transformation. The PCho-produced Cho signal has also been proposed to depend on local cellularity (Chang et al. 1995). Recently, using an HRMAS proton MRS technique, we found that PCho levels correlated with the percentage of highly cellular malignant glioma in glioblastoma multiforme patients (Cheng et al. 2000). PCho and GPC accumulation reflected early stages of growth arrest or apoptosis (Cheng et al. 2000). In addition, GPC levels increased in cultured mammalian cells that exhibited perturbed energetic metabolism during acidosis. Tissues with a high proliferative potential and tissues that were oncogenically transformed are typically highly cellular when compensating apoptotic mechanisms are absent and there are no limitations in the vascular supply. An elevated Cho peak, detected by in vivo MRS, may indicate that the tissue of interest is highly cellular, has an increased proliferative potential, or includes oncogenically transformed cells.
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Cancer cells are apoptotic, and thus typically die upon treatment with conventional chemotherapy, radiation antiangiogenic drugs and ganciclovir. In vivo proton MRS detected a substantial accumulation of polyunsaturated fatty acids during gene therapy-induced apoptosis, and PCho depletion coincides with growth arrest. Prior to volume loss, the treatment response was associated with an increase in tissue water diffusion and T2 relaxation time, which suggested that water content and bulk diffusibility increased. Gliomas undergoing apoptosis exhibited reduced diffusion of Cho-containing compounds. These observations imply an increased viscosity and restriction within cells, perhaps via cell shrinkage. Flow-cytometric studies demonstrated that gene therapy-induced apoptosis is preceded by an irreversible arrest in the late S or G2 phase of the cell cycle. MRS-detected lipids not only correlated with necrosis or apoptosis , but also with the proportion of cells in the S and G2 stages (Wei et al. 1998). Finally, the ceramide resonance region has been associated with the differential diagnosis of brain gliomas with high or low malignancy. This observation deserves further investigation, since apoptotic stimuli such as ceramide, a second messenger related to apoptosis, disrupts electron transport in mitochondria and acts as an important site for apoptosis initiation.
Classification and Statistical Analysis of MRS Many studies on classification and statistical analysis for both in vivo, ex vivo and in vitro NMR spectra have been reported. Variability of the spectra even in samples of the same type is a major difficulty in such analyses along with the large number of detected metabolites. Another difficulty appears in the cases of extensive heterogeneity of the sample where for example infiltrative tumor tissue might coexist with normal tissue and necrotic areas. Nonetheless, MRS based classification according to histological type and grade has been performed using a variety of supervised or unsupervised methods of
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statistical analysis, pattern recognition and machine learning. Examples are the linear discriminant analysis (LDA) after feature extraction with independent components analysis (ICA) in a Bayesian framework (Huang et al. 2003). correlation analysis and stepwise LDA (Tate et al. 2003), belief networks (Reynolds et al. 2007) and support vector machines (Andronesi et al. 2008). For in vivo MRS multivariate analysis techniques have been applied with so far limited clinical use primarily due to the low spectral resolution. The INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance) consortium provides a helpful decision support system based on large database of single voxel spectra. For the richer in vitro or ex vivo spectra the tumor type or grade classification results are better especially when they are combined with the in vivo MR spectroscopic or imaging findings.
Genomics Gene Expression Profiling in Cancer Cancer is a genetic disease resulting from mutation in genes regulating cell growth and proliferation. Many times histologically similar tumors present different clinical manifestation resulting from different upstream processes due to diverse gene expression patterns. Therefore, understanding of the genetic substrate in tumors could greatly improve their diagnosis and treatment. Projects like the NCI's Cancer Genome Anatomy Project (CGAP) or the Cancer Genome Characterization Initiative (CGCI) try to better understand the underlying genetic changes leading to cancer, leading eventually to improved detection, diagnosis, and treatment for the patient. DNA-microarray technology allows us to to examine the expression of thousands of genes at once and has found great utility in tumor typing and grading. The most common microarray technologies are divided, according to the type of probe used, to oligonucleotide microarrays and complementary DNA (cDNA) arrays, each one with advantages and disadvantages (Schulze and Downward 2001). Oligonucleotide
L.G. Astrakas and A.A. Tzika
microarrays can be used for gene expression, rapid mutation analysis, single nucleotide polymorphism and genotyping analyses. They have also been used in the diagnosis of genetic diseases and gene polymorphism studies. On the other hand cDNA arrays provide a less specific but easier method for large scale screening and expression studies. The application of photolithography techniques in situ on glass wafers by Affymetrix® resulted to GeneChip® containing in an area of 1.6 cm2more than 65,000 different oligonucleotides. Non specific cross-hybridization is eliminated by pairs of probes, one that is perfectly complementary to a target sequence (Perfect Match, PM) and one that is identical except for a single base mismatch in its center (Mismatch, MM). The GeneChip Human Genome U133 Plus® 2.0 array, contains 1.3 million distinct oligonucleotides and can be used to analyze the expression levels of over 47,000 transcripts as well as variants, including over 30,000 well-characterized human genes. It can utilize al low as 50 ng of total RNA, minimizing sample extraction requirements. Small samples also avoid contamination of the solid tumor sample from infiltrating tissue, such as stroma, endothelial or lymphoid cells. Other advantages of the GeneChip® DNA microarray platform are the access to probe sequences, probe redundancy (11 sequences per gene) to optimize fidelity of the signal-to noise ratio, ready commercial availability, washing, staining and scanning processes, quality control built into the manufacturing processes, available technical support, and a relatively low cost per investigated gene.
Analysis of Microarray-Based Gene Expression Data Organization, storage, and especially analysis of gene expression data are challenging (Ermolaeva et al. 1998). The use of DNA microarrays to measure genome-wide RNA expression levels has become an established research methodology in genomics to simultaneously measure the expression of tens of thousands of genes from a single sample (Quackenbush 2001). Cancer research, in particular, has benefited enormously from the use
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Brain Tumor Typing and Therapy Using Combined Ex Vivo Magnetic Resonance…
of high-throughput gene expression studies. Much effort has been devoted to developing data analysis techniques, including application of different pattern recognition algorithms to classify cancers (Macgregor and Squire 2002). Many methods have been used to identify genes that are differentially expressed in transformed cells. To improve the accuracy of cancer diagnosis and prediction of patient response to different treatment options, research has focused on the use of gene expression profile databases collected from different cancer types (Golub et al. 1999). Unsupervised learning can be used to gather gene expression data from a collection of tumor samples, and cluster the samples into groups. Clustering can be based on aggregate expression profile similarity, or genes can be clustered that share similar expression patterns in different biological contexts. Supervised learning techniques based on linear Support Vector Machines (SVMs) have also proven to be both popular and accurate; however, this learning is dependent on accurate sample labels, which are limited by histopathology. An important concern for use of either learning approach is that microarray experiments typically yield expression data for thousands of genes from a relatively small number of samples. Thus, gene-class correlations can arise by chance alone. This issue can be addressed by collecting more samples, although this is often difficult with clinical cancer samples. Another approach is to perform exploratory analysis on an initial data set and apply the results to an independent data set. Confirmed findings will be less likely to reflect chance. Permutation testing, which involves randomly permuting class labels and determining gene-class correlations, can also be used to determine statistical significance. Observed gene-class correlations are considered statistically significant, if they are stronger than those seen in permuted data (Golub et al. 1999).
Combining MRS and Genomics Previous studies have shown that the combination of different techniques that provide complementary information enhance the specificity of cancer diagnosis in clinical medicine (Garzon et al. 2011).
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Along this line PET-CT and MRI-PET scanners have already beeen produced whereas new hybtid schemes are currently in the developing process. HRMAS spectroscopy is an ideal candidate for a multiparametric approach on the tumor typing problem, because it leaves the sample intact for subsequent analysis with other techniques. To this end we have combined ex vivo MRS and whole genome expression profiling in order to produce superior biomarkers which provide unique tumor fingerprints (Astrakas et al. 2011).
Experimental Design We carried out experiments on a dataset of 55 gene expression profiles derived from normal (9 cases) and tumor (46 cases) classes from subjects ranged in age from 17 to 54 years. The tumor class samples belonged to three categories: high grade (H) [20 cases: 12 glioblastoma multiforme (GBM); 8 anaplastic astrocytoma (AA)], low grade (L) (17 cases: 7 meningioma; 7 schwanoma; 7 pylocitic astrocytoma) and metastasized (M) (11 cases: 5 adenocarcinoma; 3 breast cancer metastasis; 3 other metastasis). Ex vivo 2D TOBSY HRMAS spectra were acquired on a Bruker BioSpin Avance NMR spectrometer (600.13 MHz) using a 4-mm triple resonance (1H, 13C, 2H) HRMAS probe (Bruker) at −8 °C with 3 kHz MAS speed to minimize tissue degradation (Fig. 13.1). Specimens were preweighed and transferred to a ZrO2 rotor tube (4 mm diameter, 50 μl), containing an external standard [trimethylsilyl propionic-2,2,3,3-d4 acid (TSP), Mw = 172, d = 0.00 ppm] that functioned as a reference both for resonance chemical shift and quantification. TOBSY spectra of intact specimens were analyzed using the XWINNMR 3.5 software package (Bruker Biospin Corp, Billerica, MA). Following the standard procedures of fourier transformation, phasing, apodization, baseline correction and peak fitting. Relative quantification of the brain metabolites, we calculated by dividing the ratio of the cross peak volumes of the metabolites to the TSP diagonal peak volume by the biopsy weight. The impacts of each the following 15 NMR features on the tumor classification were examined: choline (Cho),
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Fig. 13.1 2D [1H,1H] HRMAS spectra from controls (left) and GBM (right) tumor biopsies, acquired with a 600 MHz (1H) NMR spectrometer at an MAS rate of 3 kHz and at −8 °C. Assigned: Alanine (Ala), γ-amino-butyric acid
(GABA), Choline (Cho), Glutamine (Gln), Glutamate (Glu), Glycerophosphocholine (GPC), Lipids (Lip), Myoinositol (Myo), Phosphocholine (PC), Phosphorylethanolamine (PE), N-acetyl-aspartate (NAA), and Taurine (Tau)
phosphocholine (PC), glycerophosphocholine (GPC), phosphoethanolamine (PE), ethanolamine (Etn) γ-amino-butyric acid (GABA), n-acetylaspartate (NAA), aspartate (Asp), alanine (Ala), polyunsaturated fatty acids (PUFA), glutamine (Gnl), glutamate (Glu), lactate (Lac), taurine (Tau) and lipids (Lip). The microscale genome array studies were performed with the commercially available Affymetrix U133Plus® array (Santa Clara, CA). Total experimental RNA were isolated using the modified protocol of the RNeasy purification kit (Qiagen). The Ribo-SPIA protocol (www. nugeninc.com) was used for mRNA labeling and amplification. We used 20 ng total RNA for first strand cDNA synthesis, and the entire procedure for amplification, fragmentation and labeling was performed in 1 day. Normalization and analysis of the expression values was performed using both dChip (http://biosun1.harvard.edu/complab/ dchip/) and GC-RMA. Comparison of the expression profiles between tumor biopsies and control tissue microarrays was performed using significant analysis of microarrays (SAMs) (http:// www-stat.stanford.edu/~tibs/SAM/) to obtain a list of differentially expressed genes with a false
discovery rate (q-value)
