Integrative Data Mining and Meta Analysis of Disease-Specific Large-Scale Genomic,Transcriptomic and Proteomic Data

During the past decades, large-scale microarray technologies have been applied to the field of genomics, transcriptomics and proteomics. DNA microarrays and mass spectrometry have been used as tools for identifying changes in gene- and protein expression and genomic alterations that can be linked to various stages of tumor development. Although these technologies have generated a deluge of data, bioinformatic algorithms still need to be improved to advance the understanding of many biological fundamental questions. In particular, most bioinformatic strategies are optimized for one of these technologies and only allow for an one dimensional view on the biological question. Within this thesis a bioinformatic tool was developed that combines the multidimensional information that can be obtained when analysing genomic, transcriptomic and proteomic data in an integrative manner. Neuroblastoma is a malignant pediatric tumor of the nervous system. The tumor is characterized by aberration patterns that correlate with patient outcome. aCGH (array comparative genomic hybridization) and DNA-microrarray gene expression analysis were choosen as appropriate methods to analyse the impact of DNA copy number variations on gene expression in 81 neuroblastoma samples. Within this thesis a novel bioinformatic strategy was used which identifies chromosomal aberrations that influence the expression of genes located at the same (cis-effects) and also at different (trans-effects) chromosomal positions in neuroblastoma. Sample specific cis-effects were identified for the paired data by a probe-matching procedure, gene expression discretization and a correlation score in combination with one-dimensional hierarchical clustering. The graphical representation revealed that tumors with an amplification of the oncogene MYCN had a gain of chromosome 17 whereas genes in cis-position were downregulated. Simultaneously, a loss of chromosome 1 and a downregulation of the corresponding genes hint towards a crossrelationship between chromosome 17 and 1. A Bayesian network (BN) as representation of joint probability distributions was adopted to detect neuroblastoma specific cis- and trans-effects. The strength of association between aCGH and gene expression data was represented by markov blankets, which where build up by mutual information. This gave rise to a graphical network that linked DNA copy number changes with genes and also gene-gene interactions. This method found chromosomal aberrations on 11q and 17q to have a major impact on neuroblastoma. A prominent trans-effect was identified by a gain of 17q.23.2 and an upregulation of CPT1B which is located at 22.q13.33. Further, to identify the effects of gene expression changes on the protein expression the bioinformatic tool was expanded to enable an integration of mass spectrometry and DNA-microrarray data of a set of 53 patients after lung transplantation. The tool was applied for early diagnosis of the Bronchiolitis Obliterans Syndrome (BOS) which occurs often in the second year after lung transplantation and leads to a repulsion of the lung transplant. Gene expression profiles were translated into virtual spectra and linked to their potential mass spectrometry peak. The correlation score between the virtual and real spectra did not exhibit significant patterns in relation to BOS. However, the metaanalysis approach resulted in 15 genes that could not be found in the seperate analysis of the two data types such as INSL4, CCL26 and FXYD3. These genes constitute potential biomarkers for the detection of BO

Array-based analysis of genomic DNA methylation patterns of the tumour suppressor gene p16(INK4A) promoter in colon carcinoma cell lines

Aberrant DNA methylation at CpG dinucleotides can result in epigenetic silencing of tumour suppressor genes and represents one of the earliest events in tumourigenesis. To date, however, high-throughput tools that are capable of surveying the methylation status of multiple gene promoters have been restricted to a limited number of cytosines. Here, we present an oligonucleotide microarray that permits the parallel analysis of the methylation status of individual cytosines, thus combining high throughput and high resolution. The approach was used to study the CpG island in the promoter region of the tumour suppressor gene p16(INK4A). In total, 876 oligonucleotide probes of 21 nt in length were used to inspect the methylation status of 53 CpG dinucleotides, producing correct signals in colorectal cancer cell lines as well as control samples with a defined methylation status. The information was validated by established alternative methods. The overall methylation pattern was consistent for each cell line, while different between them. At the level of individual cytosines, however, significant variations between individual cells of the same type were found, but also consistencies across the panel of cancer cell lines were observed

Histone 3.3 hotspot mutations in conventional osteosarcomas: a comprehensive clinical and molecular characterization of six H3F3A mutated cases

Background: Histone 3.3 (H3.3) hotspot mutations in bone tumors occur in the vast majority of giant cell tumors of bone (GCTBs; 96%), chondroblastomas (95%) and in a few cases of osteosarcomas. However, clinical presentation, histopathological features, and additional molecular characteristics of H3.3 mutant osteosarcomas are largely unknown. Methods: In this multicentre, retrospective study, a total of 106 conventional high-grade osteosarcomas, across all age groups were re-examined for hotspot mutations in the H3.3 coding genes H3F3A and H3F3B. H3.3 mutant osteosarcomas were re-evaluated in a multidisciplinary manner and analyzed for genome-wide DNA-methylation patterns and DNA copy number aberrations alongside H3.3 wild-type osteosarcomas and H3F3A G34W/L mutant GCTBs. Results: Six osteosarcomas (6/106) carried H3F3A hotspot mutations. No mutations were found in H3F3B. All patients with H3F3A mutant osteosarcoma were older than 30 years with a median age of 65 years. Copy number aberrations that are commonly encountered in high-grade osteosarcomas also occurred in H3F3A mutant osteosarcomas. Unlike a single osteosarcoma with a H3F3A K27M mutation, the DNA methylation profiles of H3F3A G34W/R mutant osteosarcomas were clearly different from H3.3 wild-type osteosarcomas, but more closely related to GCTBs. The most differentially methylated promoters between H3F3A G34W/R mutant and H3.3 wild-type osteosarcomas were in KLLN/PTEN (p < 0.00005) and HIST1H2BB (p < 0.0005). Conclusions: H3.3 mutations in osteosarcomas may occur in H3F3A at mutational hotspots. They are overall rare, but become more frequent in osteosarcoma patients older than 30 years. Osteosarcomas carrying H3F3A G34W/R mutations are associated with epigenetic dysregulation of KLLN/PTEN and HIST1H2BB

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications