Development and application of analytical tools to study the origin, fate and impact of the oncometabolite 2-hydroxyglutarate and its lactone

This thesis investigated the impact and metabolism of the known oncometabolite D 2 hydroxyglutarate and its recently recognized lactone R-5-oxo-2-tetrahydro-furancarboxylic acid. Production and biological effects of 2 hydoxyglutarate are well described in the literature. However, accumulation of this metabolite as a result of insufficient degradation was studied for the first time in this thesis. Therefore, a cell-based enzyme assay was developed to measure D 2-hydoxyglutarate degradation by D 2 hydroxyglutarate dehydrogenase. Quantification of 2-hydroxyglutarate was accomplished by LC-MS/MS. Applying this assay, it was found that D 2 hydroxyglutarate dehydrogenase activity differs across cell lines but is not upregulated in response to elevated 2-hydroxyglutarate levels. Moreover, in preliminary investigations on 2 hydroxyglutarate in serum of AML-patients with mutations in isocitrate dehydrogenase, 2 hydroxyglutarate-lactone was discovered. The HCT116 panel with IDH1/2 mutation served as a model to learn more about the novel metabolite as both metabolites – 2 HG and 2 HG lactone - can be detected in extracts of these cells. Thereby, 2 hydroxyglutarate was verified as the precursor by performing 13C5-glutamine tracing experiments. However, 2 hydroxyglutarate lactonization does not take place whenever the precursor is abundant, as for some biological specimens with elevated 2 hydroxyglutarate the lactone was not detectable. Further experiments provided hints that 2 hydroxyglutarate-lactone is of enzymatic origin but did not lead to the identification of this enzyme. In doing all these experiments, interconversion of 2 hydroxyglutarate and its lactone needed to be controlled and required development of new protocols using different approaches of hyphenated mass spectrometry. For instance, set up of an enantioselective analysis of 2 hydroxyglutarate and its lactone was an objective of this thesis but was not achieved with the possibilities at the institute. Finally, this thesis provided insights into origin, fate and impact of 2-HG and 2-HG-lactone in the context of tumor metabolism

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Identification of distinct markers to differentiate natural and induced T regulatory cells in cancer.

Regulatory T cells (Tregs) are a distinct subset of CD4+ T cells that play a vital role in maintaining immune homeostasis and peripheral tolerance, thereby preventing autoimmunity. Tregs are generally categorised into two main subsets; natural and induced Tregs. In cancer, Tregs are found extremely enriched in the tumour microenvironment and contribute to the inhibition of anti-tumour response and tumour progression. The origin of tumour-infiltrating Tregs (whether it is nTregs or iTregs) is still enigmatic, since there are no distinct biomarkers which can differentiate between the two subsets. Therefore, the aim of this study is to identify cell surface biomarkers that can differentiate phenotypic features of iTregs from nTregs in the context of cancer. With this aim, an in vitro murine model was successfully developed to generate CD4+CD25++Foxp3+ iTregs from purely sorted naïve CD4+CD25-Foxp3- T cells in the presence of TGF-β1. The induction of iTregs was assessed using flow cytometry. Methylation status of Foxp3-TSDR and Foxp3 stability was assessed. Naïve CD4+CD25-Foxp3- T cells and CD4+CD25+Foxp3+ nTregs were purely sorted using cell sorting. Five biologically different subsets of CD4+ cells including naïve CD4+CD25-Foxp3- T cells, activated CD4+CD25-Foxp3- T cells, naïve CD4+CD25+Foxp3+ nTregs, activated CD4+CD25+Foxp3+ nTregs and CD4+CD25++Foxp3+ iTregs were subjected to quantitative proteomic profiling using SWATH-MS. Subcellular fractionation methods were employed to isolate membrane and cytoplasmic proteins from each of the subsets. Quantitative proteomic data were analysed using artificial neural networks. The results revealed that 4 distinct membrane biomarkers (PLP2, ITIH4, HEM6 and MAVS) were differentially upregulated in iTregs compared to other subsets. EPHX1 (HYEP) was identified upregulated only in naïve nTregs and downregulated in iTregs and other subsets. The biomarkers were further tested. Pathway enrichment analysis of iTregs showed a distinct metabolic pathway enrichment in iTregs indicating a mechanistic insight into the iTreg development. Once validated in humans these proteins could be used as a biomarker for iTreg or as a drug target for the selective depletion for better immunotherapeutic outcome in cancer patients

Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome

The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies.Doctor of Philosoph

Gene expression profiling in acute leukemias: New insights into biology and a global approach to the diagnosis of leukemia using microarray technology

The application of global gene expression profiling allows to obtain detailed molecular fingerprints of underlying gene expression in any cell of interest. In this work gene expression profiles were generated from a comprehensive cohort of leukemia patients and healthy donors referred to and diagnosed in the Laboratory for Leukemia Diagnostics, Munich, Germany, which is a nation-wide reference center for the diagnosis of hematologic malignancies. Thoroughly characterized clinical samples were analyzed by high-density microarrays interrogating the expression status of more than 33,000 transcripts. In one specific aspect of this work the potential application of gene expression signatures for the prediction and classification of specific leukemia subtypes was assessed. Today the diagnosis and subclassification of leukemias is based on a controlled application of various techniques including cytomorphology, cytogenetics, fluorescence in situ hybridization, multiparameter flow cytometry, and PCR-based methods. The diagnostic procedure is performed according to a specific algorithm, but is time-consuming, cost-intensive, and requires expert knowledge. Based on a very low number of candidate genes it is demonstrated in this work that prognostically relevant acute leukemia subtypes can be classified using microarray technology. Moreover, in an expanded analysis including 937 patient samples representing 12 distinct clinically relevant acute and chronic leukemia subtypes and healthy, non-leukemia bone marrow specimens a diagnostic prediction accuracy of ~95% was achieved. Thus, given these results it can be postulated that the occurring patterns in gene expression would be so robust that they would allow to predict the leukemia subtype using global gene expression profiling technology. This finding is further substantiated through the demonstration that reported differentially expressed genes from the literature, namely pediatric gene expression signatures representing various acute lymphoblastic leukemia (ALL) subtypes, can be used to independently predict the corresponding adult ALL subtypes. Furthermore, it could be demonstrated that microarrays both confirm and reproduce data from standard diagnostic procedures, but also provide very robust results. Parameters such as partial RNA degradation, shipment time of the samples, varying periods of storage of the samples, or target preparations at different time points from either bone marrow or peripheral blood specimens by different operators did not dramatically influence the diagnostic gene expression signatures. In another major aspect of this work gene expression signatures were examined in detail to obtain new insights into the underlying biology of acute promyelocytic leukemia (APL) and t(11q23)/MLL leukemias. In APL, microarrays led to a deeper understanding of morphological and clinical characteristics. Firstly, genes which have a functional relevance in blood coagulation were found to be differentially expressed when APL was compared to other acute myeloid leukemia (AML) subtypes. Secondly, a supervised pairwise comparison between the two different APL phenotypes, M3 and its variant M3v, for the first time revealed differentially expressed genes encoding for biological functions and pathways such as granulation and maturation. With respect to 11q23 leukemias it could be demonstrated that leukemias with rearrangements of the MLL gene are characterized by a common specific gene expression signature. Additionally, in unsupervised and supervised data analysis algorithms ALL and AML cases with t(11q23)/MLL segregated according to the lineage, i.e., myeloid or lymphoid, respectively. This segregation could be explained by a highly differing transcriptional program. Through the use of biological network analyses essential regulators of early B cell development, PAX5 and EBF, were shown to be associated with a clear B-lineage commitment in lymphoblastic t(11q23)/MLL leukemias. Also, the influence of the different MLL translocation partners on the transcriptional program was directly assessed. But interestingly, gene expression profiles did not reveal a clear distinct pattern associated with one of the analyzed partner genes. Taken together, the identified molecular expression pattern of MLL fusion gene samples and biological networks revealed new insights into the aberrant transcriptional program in t(11q23)/MLL leukemias. In addition, a series of analyses was targeted to obtain new insights into the underlying biology in heterogeneous B-lineage leukemias not positive for BCR/ABL or MLL gene rearrangements. It could be demonstrated that the genetically more heterogeneous precursor B-ALL samples intercalate with BCR/ABL-positive cases, but their profiles were clearly distinct from T-ALL and t(11q23)/MLL cases. In conclusion, various unsupervised and supervised data analysis strategies demonstrated that defined leukemia subtypes can be characterized on the basis of distinct gene expression signatures. Specific gene expression patterns reproduced the taxonomy of this hematologic malignancy, provided new insights into different disease subtypes, and identified critical pathway components that might be considered for future therapeutic intervention. Based on these results it is now further possible to develop a one-step diagnostic approach for the diagnosis of leukemias using a customized microarray

Gene expression profi ling of acute myeloid leukemia

Hematopoïese, of de vorming van functionele bloedcellen, is een proces wat plaats vindt in het beenmerg. Hematopoïetische stamcellen ondergaan cycli van deling en differentiatie waarin de functionele eindcellen, zoals rode bloedcellen, bloedplaatjes en witte bloedcellen, worden gevormd. Leukemie is een ziekte waarbij de stamcellen abnormale processen van deling in combinatie met een stop van de differentiatie ondergaan, waardoor er de vorming van functionele eindcellen wordt belemmerd. In het geval van acute myeloïde leukemie (AML) is er een afwijking in de tak van bloedcelvorming waar onder andere rode bloedcellen, bloedplaatjes en granulocyten worden gevormd. De ontsporing van hematopoïetische stamcellen met AML als gevolg wordt veroorzaakt door abnormaliteiten in het genoom, zoals chromosomale fusies, deleties en mutaties. De klinische prognose wordt momenteel bepaald aan de hand van de aan- of afwezigheid van (combinaties van) abnormaliteiten. Het belangrijkste gevolg van genomische afwijkingen is de abnormale transcriptie van genen naar mRNA. Met behulpvan gen expressie profilering, door middel van microarrays, kunnen de transcriptie niveaus van duizenden genen simultaan worden bepaald. In hoofdstuk 2 is een onderzoek beschreven waarin met gen expressie profilering is toegepast op 285 beenmerg monsters van de novo AML patiënten, voor het bepalen van prognose. Verschillende bekende prognostische groepen, zoals t(8;21) en inv(16) konden worden geidentificeerd, alsmede een nieuwe prognostisch relevante groep van patiënten met een relatief slechte prognose (cluster 10).Hoofdstuk 2 laat zien dat gen expressie profilering in staat is om de huidige technieken voor het bepalen van prognose te vervangen, en prognose te verbeteren.Roeland George Willehad Verhaak was born in Wijchen, the Netherlands, on September 29 1976. After fi nishing his VWO education at the Kottenpark College in Enschede in 1996, he started a curriculum Biomedical Health Sciences at the Catholic University Nijmegen (KUN, currently Radboud University). As part of this education, he followed majors in pathobiology and toxicology, and a minor in computer science. A toxicology internship, titled ‘Mitochondrial toxicity of nuclease reverse transcriptase inhibitors, was completed at the Department of Pharmacology and Toxicology of the KUN under supervision of Dr. Roos Masereeuw. A second intership project, ‘Development of a diagnostic marker of multiple sclerosis’, was completed at the Department of Biochemistry, under supervision of Dr. Rinie van Boekel en Prof.dr. W. Van Venrooij. He obtained his Masters–degree in August 2000. After having started a project at the Department of Medical Informatics of the KUN in October 2000 in which he worked on structuring of temporal data, he switched to the bioinformatics company Dalicon BV in April 2002. At Dalicon, he worked as software engineer, with a particular focus at the database system SRS. In April 2003 he started a PhD-project at the Department of Hematology at the Erasmus MC in the lab of Prof.dr. Bob Löwenberg, supervised by Dr. Peter Valk. This work has been described in this thesis. From March 2006 until June 2006, he was a visiting scientist of the Department of Biostatistics and Computational Biology of the Dana-Farber Cancer Institute in Boston, supervised by Prof.dr. John Quackenbush. The author wil continue his academic career at the Broad Institute in Boston, a research collaboration of MIT, Harvard and its affiliated hospitals, and the Whitehead Institute

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

The European Hematology Association Roadmap for European Hematology Research: a consensus document

The European Hematology Association (EHA) Roadmap for European Hematology Research highlights major achievements in diagnosis and treatment of blood disorders and identifies the greatest unmet clinical and scientific needs in those areas to enable better funded, more focused European hematology research. Initiated by the EHA, around 300 experts contributed to the consensus document, which will help European policy makers, research funders, research organizations, researchers, and patient groups make better informed decisions on hematology research. It also aims to raise public awareness of the burden of blood disorders on European society, which purely in economic terms is estimated at €23 billion per year, a level of cost that is not matched in current European hematology research funding. In recent decades, hematology research has improved our fundamental understanding of the biology of blood disorders, and has improved diagnostics and treatments, sometimes in revolutionary ways. This progress highlights the potential of focused basic research programs such as this EHA Roadmap. The EHA Roadmap identifies nine ‘sections’ in hematology: normal hematopoiesis, malignant lymphoid and myeloid diseases, anemias and related diseases, platelet disorders, blood coagulation and hemostatic disorders, transfusion medicine, infections in hematology, and hematopoietic stem cell transplantation. These sections span 60 smaller groups of diseases or disorders. The EHA Roadmap identifies priorities and needs across the field of hematology, including those to develop targeted therapies based on genomic profiling and chemical biology, to eradicate minimal residual malignant disease, and to develop cellular immunotherapies, combination treatments, gene therapies, hematopoietic stem cell treatments, and treatments that are better tolerated by elderly patients