Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

<p>Abstract</p> <p>Background</p> <p>Complex microarray gene expression datasets can be used for many independent analyses and are particularly interesting for the validation of potential biomarkers and multi-gene classifiers. This article presents a novel method to perform correlations between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools.</p> <p>Results</p> <p>We developed Survival Online (available at <url>http://ada.dist.unige.it:8080/enginframe/bioinf/bioinf.xml</url>), a Web-based system that allows for the analysis of Affymetrix GeneChip microarrays by using a parallel version of dChip. The user is first enabled to select pre-loaded datasets or single samples thereof, as well as single genes or lists of genes. Expression values of selected genes are then correlated with sample annotation data by uni- or multi-variate Cox regression and survival analyses. The system was tested using publicly available breast cancer datasets and GO (Gene Ontology) derived gene lists or single genes for survival analyses.</p> <p>Conclusion</p> <p>The system can be used by bio-medical researchers without specific computation skills to validate potential biomarkers or multi-gene classifiers. The design of the service, the parallelization of pre-processing tasks and the implementation on an HPC (High Performance Computing) environment make this system a useful tool for validation on several independent datasets.</p

A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

<p>Abstract</p> <p>Background</p> <p>Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible for serious diseases, with a great scientific impact and a wide application area. Several standalone applications had been developed in order to analyze microarray data. Two of the most known free analysis software packages are the R-based Bioconductor and dChip. The part of dChip software concerning the calculation and the analysis of gene expression has been modified to permit its execution on both cluster environments (supercomputers) and Grid infrastructures (distributed computing).</p> <p>This work is not aimed at replacing existing tools, but it provides researchers with a method to analyze large datasets without any hardware or software constraints.</p> <p>Results</p> <p>An application able to perform the computation and the analysis of gene expression on large datasets has been developed using algorithms provided by dChip. Different tests have been carried out in order to validate the results and to compare the performances obtained on different infrastructures. Validation tests have been performed using a small dataset related to the comparison of HUVEC (Human Umbilical Vein Endothelial Cells) and Fibroblasts, derived from same donors, treated with IFN-α.</p> <p>Moreover performance tests have been executed just to compare performances on different environments using a large dataset including about 1000 samples related to Breast Cancer patients.</p> <p>Conclusion</p> <p>A Grid-enabled software application for the analysis of large Microarray datasets has been proposed. DChip software has been ported on Linux platform and modified, using appropriate parallelization strategies, to permit its execution on both cluster environments and Grid infrastructures. The added value provided by the use of Grid technologies is the possibility to exploit both computational and data Grid infrastructures to analyze large datasets of distributed data. The software has been validated and performances on cluster and Grid environments have been compared obtaining quite good scalability results.</p

Prediction of the Thromboembolic Syndrome: an Application of Artificial Neural Networks in Gene Expression Data Analysis

The aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of ‎ gene expression data using artificial neural networks. The studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in Durham in 2009. Of all the studied patients, 66 patients were suffering from thromboembolic syndrome and 51 people were enrolled in the study as the control group. The gene expression level of 22277 was measured for all the samples and was entered into the model as the main variable. Due to the high number of variables, principal components analysis and auto-encoder neural network methods were used in order to reduce the dimension of data. The results showed that when using auto-encoder networks, the classification accuracy was 93.12. When using the PCA method to reduce the size of the data, the obtained accuracy was 78.26, and hence a significant difference in the accuracy of classification was observed. If auto-encoder network method is used, the sensitivity and specificity will be 92.58 and 93.68 and when PCA method is used, they will be 0.77 and 0.78 respectively. The results suggested that auto-encoder networks, compared with the PCA method, had a higher level of accuracy for the classification of thromboembolic syndrome status

DEFINITION OF BIOLOGICAL RESPONSES THROUGH THE ANALYSIS OF GENE EXPRESSION PROFILES

The aim of this PhD project was the development of a pipeline for the analysis of expression data and a set of of different strategies to extract biological informations from micrarray experiments. The computational pipeline for processing raw microarray data (images) to define gene expression levels, to provide experiment quality assessment and significativity statistical tests, was implemented in R, using mostly Bioconductor packages. The first fase had as purpose the determination of the gene function combining experiments of silecing with the gene expression analysis. Caspase-2 is a member of a cystein-protease family that carry out important roles in the apoptosis and in the inflammation. Altough it is highly conserved from the evolutionary point of view, in the literature several contradictory results are found. Being expressed at high level during the neurological development and with a strong involvement in the apoptotic processes in the adult central nervous system, we decided to proceed with the silecing of the gene that codifies for this enzyme using glioblastoma cells, a very aggressive cerebral tumor. The comparative analysis of expression profiles of silenced cells respect to the control ones, highlighted the relation between CASP2 and genes involved in the cholesterol metabolism. Previuos studies have suggested for this enzime a role in the control of intracellular level of this metabolite. Therefor, we decided to use data stored in public databases in order to to extend the investigation, including all the other caspases and all the genes in same way connected to cholesterol. After we had obtained the data related to several different experiments, we went ahead with the computation of the correlation between expression levels and, then, based of these values, with the clustring analysis in order to see which among the caspases has the same corralational profile. After that, the analysis was expanded to normal brain and liver tissues, in order to know whether the situation observed in the patological condition is unique or if it can be overlayed to that present in normal tissues. In the second phase, I performed an analysis of expression data with a completely different purpose. The aim of this project was the definition of the signaling pathways and of the resistence mechanisms induced by the treatment of cancer cells obtained from patients affected by cronic lymphocytic leukemia and treated with a new category of ubiquitin proteasome system (UPS) inhibitors. Through the comparison of trascriptional profiles before and after the treatment, many genes connected with the drug action at cellular level, whose expression was altered by the UPS inhibitor, were identified. Furthermore, considering the difference in terms of responsiveness of the analized patients, we could determine some genes responsible of the different efficacy of the farmacological treatment

Antibody microarray-based cellular oncoproteomics

Seit ihrer kürzlichen Einführung haben sich Antikörper-Microarrays schnell als leistungsstarke, robuste und empfindliche Methode zur Untersuchung von Veränderungen des Proteoms etabliert. Sie erlauben ein breites Spektrum an Anwendungen, mit Schwerpunkt im Bereich biomedizinischer Wissenschaft und klinischer Forschung. Allerdings waren Antikörper-Microarrays bisher hauptsächlich für die Analyse von Serum- und Urinproben ausgelegt. Zell-Lysate und Gewebe-Extrakte stellten eine große Herausforderung hinsichtlich Qualität und Verlässlichkeit dar. In der hier präsentierten Arbeit wurden eine Reihe technischer Aspekte zur Anwendungsreife gebracht, die für eine Analyse von Zell-Extrakten essenziell sind, und für Studien an Tumorzellen und Krebsgeweben genutzt. Die Extraktion einer möglichst vollständigen Repräsentation eines zellulären Proteoms ist ein kritischer Schritt für jede Art der Proteinanalyse. Zwar gab es bereits Methoden und entsprechende Reagenzien sind kommerziell erhältlich, aber die vorhandenen Verfahren für ein Proteinextraktion aus Zellen und Geweben waren völlig unzureichend für Studien mit Antikörper-Microarrays und wurden deshalb detailliert bearbeitet. Als Ergebnis wurde ein effektives Ein-Schritt-Extraktionsverfahren von hoher Reproduzierbarkeit entwickelt, das es erlaubt, Proteine unter nativen Bedingungen aus Zellen und Geweben zu isolieren. Im Vergleich zu bekannten Methoden sind die Ausbeuten signifikant besser, speziell auch für membran-assoziierte Proteine und Moleküle aus Zellkompartimenten. Gleichzeitig wird die Funktionalität der Proteine wesentlich besser erhalten. Die mit dem Protokoll gewonnenen Proteinextrakte zeigen eine hohe Kompatibilität mit Studien auf Antikörper-Microarrays. Zusätzlich wurden Parameter der Array-Analyse untersucht und optimiert, so dass eine robuste Analyse von komplexen zellulären Proteinextrakten möglich wurde. Unter anderem wurden Faktoren wie (i) die Pufferzusammensetzung zur Blockierung der Oberflächen gegen unspezifische Bindung, (ii) die Dauer der Blockierung, (iii) Proteinhandhabung und Prozessierung, (iv) Parameter des arkierungsprozesses und des Entfernens überschüssigen Farbstoffs, als auch (v) Inkubationsparameter wie etwa Pufferzusammensetzung, Dauer, Temperatur und Probenmischung analysiert und optimiert. Mit den entwickelten Verfahren wurden die Proteome von 24 Pankreaskrebs Zelllinien und zwei Kontroll-Zelllinien mittels eines Microarrays untersucht, der 810 Antikörper gegen 741 Proteine trug, die mit Krebserkrankungen assoziiert sind. Weiterhin wurden in einer Studie zum Verständnis der molekularen Hintergründe von Pankreaskrebs mehr als vierhundert Gewebeproben von Tumorpatienten und aus gesundem Gewebe analysiert

Development of targeted therapeutic strategies for metastatic lung cancer

El cáncer de pulmón es el cáncer que se diagnostica con más frecuencia y la principal causa de muerte por cáncer en todo el mundo. Es importante destacar que alrededor del 75% de los pacientes son diagnosticados en estadios metastásicos avanzados, cuando la cirugía ya no es posible, lo que supone una caída dramática de la tasa de supervivencia a 5 años al 6%. El principal objetivo de esta tesis es definir nuevas estrategias terapéuticas inspiradas en la biología para pacientes con cáncer de pulmón metastásico. Para ello, se exploraron diferentes estrategias terapéuticas inspiradas en la biología del tumor, una de ellas proviene de los exosomas tumorales y la otra de las células que diseminan desde el tumor primario para formar las metástasis

Defining the role of LRIG1 dependent EGFR signalling on airway homeostasis and lung cancer development

Aberrations of EGFR signalling drive cancer development. In squamous cell lung cancer, EGFR is overexpressed. LRIG1 is a negative regulator of EGFR and patient pre-invasive squamous cell lung cancer samples show LRIG1 loss, suggesting involvement in early disease pathogenesis. In skin and gut homeostasis, LRIG1 regulates stem cells. In the upper airway, basal cells act as stem cells and are the putative origin of squamous cell lung cancer. I hypothesise LRIG1 has a key role in the airway homeostasis and its loss tilts this towards pre-invasive squamous cell lung cancer development. Lrig1 EGFP-ires-CreERT2 mice delineated airway LRIG1 expression. Flow sorted LRIG1-positive and -negative murine basal cells were used in 2D and 3D colony-forming, spheroid and proliferation assays. A murine squamous cell lung cancer model was set up through application of N-Nitrosotris-(2-chloroethyl)urea (NTCU). Pre-invasive lesions and tumour development were compared between wild-type (WT), LRIG1-heterozygous and LRIG1-null animals. Human basal cells obtained from bronchoscopy were sorted according to LRIG1 expression and used directly in colony-forming assays or maintained in primary culture to assess the effect of shRNA knockdown of LRIG1. LRIG1 is expressed by 50% of airway basal cells. LRIG1-expressing murine basal cells exhibit increased colony-forming capacity (p=0.0133), spheroid formation (p=0.0020) and proliferation (p=0.0011) compared to LRIG1-negative cells. Similarly, LRIG1-expressing human airway basal cells isolated from endobronchial brush biopsy samples exhibit increased colony-forming capacity (p=0.0067) and proliferation (p=0.0153). Topical application of NTCU to mice recapitulates the development of human pre-invasive and squamous cell lung cancer lesions after 23 weeks. Results show lesions in LRIG1-null mice to be larger than those of WT animals. shRNA knockdown of LRIG1 in cultured human airway basal cells alters cell phenotype, leading to increased colony-forming efficiency and greater proliferation at cell confluence. LRIG1 has an important role in stem cell homeostasis of the human and murine airway epithelium. Loss of LRIG1 promotes lesion development in a murine squamous cell lung cancer mouse model and alters behaviour of human epithelial cells in culture, indicating a potential target for the treatment of squamous cell lung cancer in humans

Full genome analysis of microglial activation; ramifications of TREM2

Neuroinflammation is a pathological hallmark of Alzheimer's disease (AD) and it is well established that microglia, the brain's resident phagocytes, are pivotal for the immune response observed in AD. In the healthy brain, microglia attack and remove pathogens and cell debris, but have been shown to become reactive in AD. An apparent link between microglia and AD is Amyloid β (Aβ), which accumulates in the plaques observed in the brains of AD patients and has been reported as a microglia activator. Genome Wide Association Studies (GWAS) have allowed the identification of more than 20 genetic risk associations to AD. Many of these associations highlight the importance of immune pathways (and others) in AD. More recently, the identification of mutations in TREM2 (Triggering Receptor Expressed on Myeloid Cells 2), a gene exclusively expressed by microglia in the brain, has brought microglial activation and dysfunction back to the attention of the AD community. The main focus of this study is to understand microglial activation elicited by different stimuli including Aβ1-42 monomers, oligomers and fibrils- with regards to their inflammatory activation status (M1, M2 or other) and whole-genome expression profile. To this end, the mouse-derived BV2 cell line was used to assess gene expression changes during microglial activation. Data shows that M1 and M2 activators alter gene expression of AD-associated genes in a manner that is potentially detrimental for AD progression. A second objective of this thesis was to use the CRISPR/Cas9 gene editing technology for the generation of Trem2-deficient BV2 cell lines. As a result, Trem2 +/- (haploinsufficient) and Trem2 -/- (knockout) BV2 cell lines were generated. Subsequently, these cell lines were characterised in terms of their phagocytic, proliferation, migration, cytokine release capacities and whole genome expression. In consequence, this study provides new and wellcharacterised in vitro models for the study of Trem2 function