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Background: The frequency of the most observed cancer, Non Hodgkin Lymphoma (NHL), is further rising. Diffuse large B-cell lymphoma (DLBCL) is the most common of the NHLs. There are two subgroups of DLBCL with different gene expression patterns: ABC (“Activated B-like DLBCL”) and GCB (“Germinal Center B-like DLBCL”). Without therapy the patients often die within a few months, the ABC type exhibits the more aggressive behaviour. A further B-cell lymphoma is the Mantle cell lymphoma (MCL). It is rare and shows very poor prognosis. There is no cure yet. Methods: In this project these B-cell lymphomas were examined with methods from bioinformatics, to find new characteristics or undiscovered events on the molecular level. This would improve understanding and therapy of lymphomas. For this purpose we used survival, gene expression and comparative genomic hybridization (CGH) data. In some clinical studies, you get large data sets, from which one can reveal yet unknown trends. Results (MCL): The published proliferation signature correlates directly with survival. Exploratory analyses of gene expression and CGH data of MCL samples (n=71) revealed a valid grouping according to the median of the proliferation signature values. The second axis of correspondence analysis distinguishes between good and bad prognosis. Statistical testing (moderate t-test, Wilcoxon rank-sum test) showed differences in the cell cycle and delivered a network of kinases, which are responsible for the difference between good and bad prognosis. A set of seven genes (CENPE, CDC20, HPRT1, CDC2, BIRC5, ASPM, IGF2BP3) predicted, similarly well, survival patterns as proliferation signature with 20 genes. Furthermore, some bands could be associated with prognosis in the explorative analysis (chromosome 9: 9p24, 9p23, 9p22, 9p21, 9q33 and 9q34). Results (DLBCL): New normalization of gene expression data of DLBCL patients revealed better separation of risk groups by the 2002 published signature based predictor. We could achieve, similarly well, a separation with six genes. Exploratory analysis of gene expression data could confirm the subgroups ABC and GCB. We recognized a clear difference in early and late cell cycle stages of cell cycle genes, which can separate ABC and GCB. Classical lymphoma and best separating genes form a network, which can classify and explain the ABC and GCB groups. Together with gene sets which identify ABC and GCB we get a network, which can classify and explain the ABC and GCB groups (ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5; Altogether these findings are useful for diagnosis, prognosis and therapy (cytostatic drugs).
In this thesis, the development of a phylogenetic DNA microarray, the analysis of several gene expression microarray datasets and new approaches for improved data analysis and interpretation are described. In the first publication, the development and analysis of a phylogenetic microarray is presented. I could show that species detection with phylogenetic DNA microarrays can be significantly improved when the microarray data is analyzed with a linear regression modeling approach. Standard methods have so far relied on pure signal intensities of the array spots and a simple cutoff criterion was applied to call a species present or absent. This procedure is not applicable to very closely related species with high sequence similarity because cross-hybridization of non-target DNA renders species detection impossible based on signal intensities alone. By modeling hybridization and cross-hybridization with linear regression, as I have presented in this thesis, even species with a sequence similarity of 97% in the marker gene can be detected and distinguished from related species. Another advantage of the modeling approach over existing methods is that the model also performs well on mixtures of different species. In principle, also quantitative predictions can be made. To make better use of the large amounts of microarray data stored in public databases, meta-analysis approaches need to be developed. In the second publication, an explorative meta-analysis exemplified on Arabidopsis thaliana gene expression datasets is presented. Integrating datasets studying effects such as the influence of plant hormones, pathogens and different mutations on gene expression levels, clusters of similarly treated datasets could be found. From the clusters of pathogen-treated and indole-3-acetic acid (IAA) treated datasets, representative genes were selected which pointed to functions which had been associated with pathogen attack or IAA effects previously. Additionally, hypotheses about the functions of so far uncharacterized genes could be set up. Thus, this kind of meta-analysis could be used to propose gene functions and their regulation under different conditions. In this work, also primary data analysis of Arabidopsis thaliana datasets is presented. In the third publication, an experiment which was conducted to find out if microwave irradiation has an effect on the gene expression of a plant cell culture is described. During the first steps, the data analysis was carried out blinded and exploratory analysis methods were applied to find out if the irradiation had an effect on gene expression of plant cells. Small but statistically significant changes in a few genes were found and could be experimentally confirmed. From the functions of the regulated genes and a meta-analysis with publicly available microarray data, it could be suspected that the plant cell culture somehow perceived the irradiation as energy, similar to perceiving light rays. The fourth publication describes the functional analysis of another Arabidopsis thaliana gene expression dataset. The gene expression data of the plant tumor dataset pointed to a switch from a mainly aerobic, auxotrophic to an anaerobic and heterotrophic metabolism in the plant tumor. Genes involved in photosynthesis were found to be repressed in tumors; genes of amino acid and lipid metabolism, cell wall and solute transporters were regulated in a way that sustains tumor growth and development. Furthermore, in the fifth publication, GEPAT (Genome Expression Pathway Analysis Tool), a tool for the analysis and integration of microarray data with other data types, is described. It consists of a web application and database which allows comfortable data upload and data analysis. In later chapters of this thesis (publication 6 and publication 7), GEPAT is used to analyze human microarray datasets and to integrate results from gene expression analysis with other datatypes. Gene expression and comparative genomic hybridization data from 71 Mantle Cell Lymphoma (MCL) patients was analyzed and allowed proposing a seven gene predictor which facilitates survival predictions for patients compared to existing predictors. In this study, it was shown that CGH data can be used for survival predictions. For the dataset of Diffuse Large B-cell lymphoma (DLBCL) patients, an improved survival predictor could be found based on the gene expression data. From the genes differentially expressed between long and short surviving MCL patients as well as for regulated genes of DLBCL patients, interaction networks could be set up. They point to differences in regulation for cell cycle and proliferation genes between patients with good and bad prognosis.
Ibrutinib serves as an efficient second-line therapy in relapsed/refractory mantle cell lymphoma. However, resistance to the BTK inhibitor results in a poor prognosis for patients. Since the mechanisms leading to resistance in initially responding tumor cells are poorly understood, this work aimed to decipher acquired features in ibrutinib-surviving cells of a sensitive mantle cell lymphoma cell line and evaluate these potential therapeutic targets in ibrutinib-treated mantle cell lymphoma.
Time-resolved single-cell RNA sequencing was performed to track the transcriptomic evolution of REC-1 cells across 6 and 48 hours of treatment. Single-cell analysis uncovered a subpopulation of REC-1 with potentially greater aggressiveness and survival advantage by benefiting from interaction with the tumor microenvironment. Upregulation of B-cell receptor genes, elevated surface antigen expression of CD52 and metabolic rewiring to higher dependence on oxidative phosphorylation were identified as further potential resistance features of ibrutinib-surviving cells. RNA sequencing after prolonged incubation corroborated the increase in CD52 and oxidative phosphorylation as dominant characteristics of the cells surviving the 4-day treatment, highlighting their potential as therapeutic targets in combination with ibrutinib treatment. Concomitant use of ibrutinib and the oxidative phosphorylation inhibitor IACS-010759 increased toxicity compared to ibrutinib monotherapy due to higher apoptosis and greater inhibition of proliferation. For anti-CD52 therapy, a consecutive approach with ibrutinib pretreatment followed by incubation of surviving cells with a CD52 monoclonal antibody and human serum yielded a synergistic effect, as ibrutinib-surviving mantle cell lymphoma cells were rapidly depleted by complement-dependent cytotoxicity. Regarding the effects on primary tumor cells from mantle cell lymphoma patients, ibrutinib induced upregulation of CD52 in some cases, and increased toxicity of anti-CD52 therapy was observed in ibrutinib-sensitive patient samples after pretreatment with the BTK inhibitor. The likely favorable in vivo efficacy of an anti-CD52 therapy might therefore be restricted to a subgroup of mantle cell lymphoma patients, also in view of the associated side effects.
Given the need for new therapeutic options in mantle cell lymphoma to overcome resistance to ibrutinib, this work highlights the potentially beneficial use of an oxidative phosphorylation inhibitor as add-on therapy. In addition, the findings suggest to further assess the value of anti-CD52 therapy as consolidation to ibrutinib in ibrutinib-sensitive patients with elevated CD52 surface levels on tumor cells to target resistant clones and minimize risk of minimal residual disease and relapse.
Burkitt lymphoma (BL) is a highly aggressive B cell malignancy. Rituximab, a humanized antibody against CD20, in a combination with chemotherapy is a current treatment of choice for B-cell lymphomas including BL. However, certain group of BL patients are resistant to Rituximab therapy. Therefore, alternative treatments targeting survival pathways of BL are needed.
In BL deregulation of MYC expression, together with additional mutations, inhibits differentiation of germinal centre (GC) B cells and drives proliferation of tumor cells. Pro-apoptotic properties of MYC are counteracted through the B-cell receptor (BCR) and phosphoinositide-3-kinase (PI3K) pathway to ensure survival of BL cells. In normal B-cells BCR triggering activates both NF-κB and NFAT-dependent survival signals. Since BL cells do not exhibit constitutive NF-κB activity, we hypothesized that anti-apoptotic NFATc1A isoform might provide a major survival signal for BL cells.
We show that NFATc1 is constitutively expressed in nuclei of BL, in BL cell lines and in Eµ-Myc–induced B cell lymphoma (BCL) cells. Nuclear residence of NFATc1 in these entities depends on intracellular Ca2+ levels but is largely insensitive to cyclosporine A (CsA) treatment and therefore independent from calcineurine (CN) activity. The protein/protein interaction between the regulatory domain of NFATc1 and DNA binding domain of BCL6 likely contributes to sustained nuclear residence of NFATc1 and to the regulation of proposed NFATc1-MYC-BCL6-PRDM1 network in B-cell lymphomas.
Our data revealed lack of strict correlation between the expression of six NFATc1 isoforms in different BL-related entities suggesting that both NFATc1/alphaA and -betaA isoforms provide survival functions and that NFATc1alpha/betaB and -alpha/betaC isoforms either do not possess pro-apoptotic properties in BL cells or these properties are counterbalanced. In addition, we show that in BL entities expression of NFATc1 protein is largely regulated at post-transcriptional level, including MYC dependent increase of protein stability.
Functionally we show that conditional inactivation of Nfatc1 gene in Eµ-Myc mice prevents development of BCL tumors with mature B cell immunophenotype (IgD+). Loss of NFATc1 expression in BCL cells ex vivo results in apoptosis of tumor cells.
Together our results identify NFATc1 as an important survival factor in BL cells and, hence, as a promising target for alternative therapeutic strategies for BL.