Frederik Otzen Bagger:
Acute Myeloid Leukaemia (AML) is an aggressive cancer of the bone marrow, affecting formation of blood cells during haematopoiesis. This thesis presents investigation of AML using mRNA gene expression profiles (GEP) of samples extracted from the bone marrow of healthy and diseased subjects. Here GEPs from purified healthy haematopoietic populations, with different levels of differentiation, form the basis for comparison with diseased samples. We present a mathematical transformation of mRNA microarray data to make it possible to compare AML samples, carrying expanded aberrant haematopoietic progenitor populations, with their closest normal cellular counterpart. Analysing > 1000 AML patients using this framework resulted in precise genetic signatures for known AML karyotypes and decomposition of the large group of patients with no such classification. Additionally, using a murine model to investigate the role of telomerase in AML, we were able to translate the observed effect into human AML patients and identify specific genes involved, which also predict survival patterns in AML patients. During these studies we have applied methods for investigating differentially expressed genes and genetic signatures and for reducing dimensionally of gene expression data. Next, we have used machine-learning methods to predict survival and to assess important predictors based on these results. General application of a number of these methods has been implemented into two public query-based gene-lookup webservices, called HemaExplorer and BloodSpot. These web-services support the aim of making data and analysis of haematopoietic cells from mouse and human accessible for researchers without bioinformatics expertise. Finally, in order to aid the analysis of the very limited number of haematopoietic progenitor cells obtainable from bone marrow aspirations, this thesis presents a method developed to investigate transcription factor binding and histone modifications by ChIP-Seq using pico-scale amounts of DNA.