Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms

A classification model for distinguishing copy number variants from cancer-related alterations

<p>Abstract</p> <p>Background</p> <p>Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.</p> <p>Results</p> <p>We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays.</p> <p>Conclusions</p> <p>Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone.</p

TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays

<p>Abstract</p> <p>Background</p> <p>High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.</p> <p>Results</p> <p>We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances.</p> <p>Conclusions</p> <p>TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package <it>aroma.cn</it>, which is part of the Aroma Project (<url>http://www.aroma-project.org/</url>).</p

Identification of proteins involved in neural progenitor cell targeting of gliomas

<p>Abstract</p> <p>Background</p> <p>Glioblastoma are highly aggressive tumors with an average survival time of 12 months with currently available treatment. We have previously shown that specific embryonic neural progenitor cells (NPC) have the potential to target glioma growth in the CNS of rats. The neural progenitor cell treatment can cure approximately 40% of the animals with malignant gliomas with no trace of a tumor burden 6 months after finishing the experiment. Furthermore, the NPCs have been shown to respond to signals from the tumor environment resulting in specific migration towards the tumor. Based on these results we wanted to investigate what factors could influence the growth and progression of gliomas in our rodent model.</p> <p>Methods</p> <p>Using microarrays we screened for candidate genes involved in the functional mechanism of tumor inhibition by comparing glioma cell lines to neural progenitor cells with or without anti-tumor activity. The expression of candidate genes was confirmed at RNA level by quantitative RT-PCR and at the protein level by Western blots and immunocytochemistry. Moreover, we have developed <it>in vitro </it>assays to mimic the antitumor effect seen <it>in vivo</it>.</p> <p>Results</p> <p>We identified several targets involved in glioma growth and migration, specifically CXCL1, CD81, TPT1, Gas6 and AXL proteins. We further showed that follistatin secretion from the NPC has the potential to decrease tumor proliferation. <it>In vitro </it>co-cultures of NPC and tumor cells resulted in the inhibition of tumor growth. The addition of antibodies against proteins selected by gene and protein expression analysis either increased or decreased the proliferation rate of the glioma cell lines <it>in vitro</it>.</p> <p>Conclusion</p> <p>These results suggest that these identified factors might be useful starting points for performing future experiments directed towards a potential therapy against malignant gliomas.</p

Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme

Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM.Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution.The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001).Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM

Different behaviour of DVL1, DVL2, DVL3 in astrocytoma malignancy grades and their association to TCF1 and LEF1 upregulation

Key regulators of the Wnt signalling, DVL1, DVL2 and DVL3, in astrocytomas of different malignancy grades were investigated. Markers for DVL1, DVL2 and DVL3 were used to detect microsatellite instability (MSI) and gross deletions (LOH), while immunohistochemistry and immunoreactivity score were used to determine the signal strengths of the three DVL proteins and transcription factors of the pathway, TCF1 and LEF1. Our findings demonstrated that MSI at all three DVL loci was constantly found across tumour grades with the highest number in grade II (P = 0.008). Collectively, LOHs were more frequent in high-grade tumours than in low grade ones. LOHs of DVL3 gene were significantly associated with grade IV tumours (P = 0.007). The results on protein expressions indicated that high-grade tumours expressed less DVL1 protein as compared with low grade ones. A significant negative correlation was established between DVL1 expression and malignancy grades (P < 0.001). The expression of DVL2 protein was found similar across grades, while DVL3 expression significantly increased with malignancy grades (P < 0.001). The signal strengths of expressed DVL1 and DVL3 were negatively correlated (P = 0.002). However, TCF1 and LEF1 were both significantly upregulated and increasing with astrocytoma grades (P = 0.001). A positive correlation was established between DVL3 and both TCF1 (P = 0.020) and LEF1 (P = 0.006) suggesting their joint involvement in malignant progression. Our findings suggest that DVL1 and DVL2 may be involved during early stages of the disease, while DVL3 may have a role in later phases and together with TCF1 and LEF1 promotes the activation of Wnt signalling