Computational study of cancer

• Main Author: Gundem, Gunes
• Other Authors: López Bigas, Núria
• Format: Doctoral Thesis
• Language: English
• Published: Universitat Pompeu Fabra 2011
• Subjects: oncogenomics; high-throughput data; bioinformatics; cancer; IntOGen; sample-level enrichment analysis; pronòstic; oncogenòmica; bioinformàtica; dades produïdes amb tècniques d'alt rendiment; 57
• Online Access: http://hdl.handle.net/10803/53575

In my thesis, I focused on integrative analysis of high-throughput oncogenomic data. This was done in two parts: In the first part, I describe IntOGen, an integrative data mining tool for the study of cancer. This system collates, annotates, pre-processes and analyzes large-scale data for transcriptomic, copy number aberration and mutational profiling of a large number of tumors in multiple cancer types. All oncogenomic data is annotated with ICD-O terms. We perform analysis at different levels of complexity: at the level of genes, at the level of modules, at the level of studies and finally combination of studies. The results are publicly available in a web service. I also present the Biomart interface of IntOGen for bulk download of data. In the final part, I propose a methodology based on sample-level enrichment analysis to identify patient subgroups from high-throughput profiling of tumors. I also apply this approach to a specific biological problem and characterize properties of worse prognosis tumor in multiple cancer types. This methodology can be used in the translational version of IntOGen.