Computational Biology-Driven Genomic and Epigenomic Delineation of Acute Myeloid Leukemia

Hematopoiesis is the deterministic process of blood cell formation taking place in the bone marrow. Mature blood cells are produced by a tightly controlled mechanism from hematopoietic stem cells (HSCs) residing in the bone marrow. Upon maturation blood cells are released into the peripheral blood and from this point onward can be transported to the different locations of the body. The mature blood cells exert different functions dependent on a strictly controlled path of maturation. The distinct leukocytes comprising granulocytes, monocytes, macrophages, natural killer cells and lymphocytes are essential for the defense against pathogens and foreign invaders, erythrocytes play a pivotal role in the transportation of oxygen to remote organs, and platelets confer the process of blood clotting. Mature blood cells are short-lived and require continuous replenishment. The control of the production and the total number of blood cells is conferred by multipotent progenitors and a small population of pluripotent HSCs (Figure 1). HSCs reside in the bone marrow of adult mammals at the apex of a hierarchy of progenitors which become progressively restricted to several and eventually single lineages of blood cells. Additionally these pluripotent stem cells have the unique ability to self-renew, generating a source for continuous replenishment of the complete blood cell system. The hematopoietic stem cell compartment contains stem cells with progressively decreased self-renewal capacity with the retention of multi-lineage reconstitution. The rare long term HSC (LT-HSC) is at the pinnacle of the hematopoietic hierarchy and is mainly quiescent. With the most conserved rate of self-renewal it prevents the depletion of the stem cell pool. The less rare short term HSC (ST-HSC) still retains a minimal ability for self-renewal and is the more active effector cell for hematopoietic replenishment in normal situations. The main constituent of the hematopoietic stem cell compartment is the multipotent progenitor (MPP) which lost its self-renewal capacity, however, kept the ability to give rise to daughter cells of different lineages. The daughter cells, common myeloid progenitor (CMP) and common lymphoid progenitor (CLP), are still oligopotent as they give rise to multiple blood cell types, e.g., lymphocytes, granulocytes, platelets and erythrocytes. The production of mature blood cells is a strictly controlled process that adapts to the needs of human physiology, e.g., erythrocyte production after blood loss. The control is asserted mainly by external stimuli, e.g., hematopoietic cytokines or growth factors, which are produced by constituents of the regulatory microenvironment within the bone marrow niche, other blood cells or cytokine secreting organs. The microenvironment plays a pivotal role in the formation of adequate numbers of blood cells of the correct type and the hematopoietic cytokines it produces allows the hematopoietic system to dynamically adapt to extramedullary events, e.g., blood loss, infection or cancer immunoediting

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

Learning cell states from high-dimensional single-cell data

Recent developments in single-cell measurement technologies have yielded dramatic increases in throughput (measured cells per experiment) and dimensionality (measured features per cell). In particular, the introduction of mass cytometry has made possible the simultaneous quantification of dozens of protein species in millions of individual cells in a single experiment. The raw data produced by such high-dimensional single-cell measurements provide unprecedented potential to reveal the phenotypic heterogeneity of cellular systems. In order to realize this potential, novel computational techniques are required to extract knowledge from these complex data. Analysis of single-cell data is a new challenge for computational biology, as early development in the field was tailored to technologies that sacrifice single-cell resolution, such as DNA microarrays. The challenges for single-cell data are quite distinct and require multidimensional modeling of complex population structure. Particular challenges include nonlinear relationships between measured features and non-convex subpopulations. This thesis integrates methods from computational geometry and network analysis to develop a framework for identifying the population structure in high-dimensional single-cell data. At the center of this framework is PhenoGraph, and algorithmic approach to defining subpopulations, which when applied to healthy bone marrow data was shown to reconstruct known immune cell types automatically without prior information. PhenoGraph demonstrated superior accuracy, robustness, and efficiency, compared to other methods. The data-driven approach becomes truly powerful when applied to less characterized systems, such as malignancies, in which the tissue diverges from its healthy population composition. Applying PhenoGraph to bone marrow samples from a cohort of acute myeloid leukemia (AML) patients, the thesis presents several insights into the pathophysiology of AML, which were extracted by virtue of the computational isolation of leukemic subpopulations. For example, it is shown that leukemic subpopulations diverge from healthy bone marrow but not without bound: Leukemic cells are apparently free to explore only a restricted phenotypic space that mimics normal myeloid development. Further, the phenotypic composition of a sample is associated with its cytogenetics, demonstrating a genetic influence on the population structure of leukemic bone marrow. The thesis goes on to show that functional heterogeneity of leukemic samples can be computationally inferred from molecular perturbation data. Using a variety of methods that build on PhenoGraph's foundations, the thesis presents a characterization of leukemic subpopulations based on an inferred stem-like signaling pattern. Through this analysis, it is shown that surface phenotypes often fail to reflect the true underlying functional state of the subpopulation, and that this functional stem-like state is in fact a powerful predictor of survival in large, independent cohorts. Altogether, the thesis takes the existence and importance of cellular heterogeneity as its starting point and presents a mathematical framework and computational toolkit for analyzing samples from this perspective. It is shown that phenotypic and functional heterogeneity are robust characteristics of acute myeloid leukemia with clinically significant ramifications

Analysis of transcriptional networks and chromatin states in normal and abnormal blood cells

Altered myeloid differentiation can lead to a variety of haematological malignancies including the Myelodysplastic Syndrome (MDS), chronic myelomonocytic leukaemia (CMML) and acute myeloid leukaemia (AML). We have studied transcriptome regulation in haematopoietic stem and progenitor cells (HSPC) using different high-throughput technologies. In this thesis, I introduce bioinformatics pipelines and an algorithm for the analysis of next-generation sequencing (NGS) data and highlight methods to integrate different genome-wide datasets to derive chromatin states, transcriptional and post-transcriptional networks in normal and abnormal blood cells. Following an introduction to key concepts relevant to this thesis, in the second chapter, I detail the first genome-wide characterisation of small non-coding RNAs in HSPC in MDS patients. By profiling mRNA expression in the same patients, I developed a novel statistical model that integrated miRNA, transcription factors (TF) and gene expression to identify novel regulatory pathways in MDS. MDS and CMML patients often die following transformation into AML. In the third chapter, I present an analysis of a heptad of HSPC TFs that regulate their own expression by binding enhancers of these genes. The enhancer and the heptad are active in a subset of AMLs, normal HSPC and leukemic stem cells. The heptad and a gene signature derived from enhancer activity, predict clinical outcome in AML, while the expression of four heptad genes further correlated with the underlying genetic mutations in cytogenetically normal AML patients. In the fourth chapter, I describe a novel algorithm (LPCHP) to define histone states from NGS data. LPCHP makes use of signal characteristics such as peak shape, location and frequencies in contrast to other algorithms, which only evaluate read intensities. LPCHP was evaluated and performed well in terms of correlation with gene expression, prediction of histone states, parameter variations and signal-to-noise ratios. In the final chapter, I present preliminary data and outline plans for future work. I propose a systems biology approach to study networks of miRNAs and TFs in MDS and CMML. Sequencing of miRNA and mRNA facilitates network reconstruction where interactions between miRNA and mRNA are predicted at single nucleotide resolution, providing avenues for patient stratification and drug response prediction

Computational Biology in Acute Myeloid Leukemia with CEBPA Abnormalities

__Abstract__ In the last decade, tiling-array and next-generation sequencing technologies allowed quantitative measurements of different cellular processes, such as mRNA expression, genomic changes including deletions or amplifications, DNA-methylation, chromatin modifications or Protein-DNA-binding interactions. Using these technologies, thousands of features can now be measured simultaneously in a patient cell sample. The use of for instance mRNA expression profiles or DNA-methylation profiles have already provided new insight into the molecular biology of patients with Acute Myeloid Leukemia (AML). AML is a blood cell malignancy, in which primitive myeloid cells have been transformed and accumulate in the bone marrow and blood. Different forms of AML exist with different molecular abnormalities that associate with distinct responses to therapy. Many subgroups with comparable mRNA expression or DNA-methylation patterns were identified. These studies also revealed the existence of novel previously undefined AML subtypes. Among those was a group of patients with a mutation in a gene called CEBPA. CEBPA is a gene that encodes the transcription factor CCAAT Enhancer Binding Protein Alpha (C/EBPα), which controls the expression of genes in myeloid progenitor cells. Mutated CEBPA encodes a dysfunctional C/EBPα-protein, which consequently results in aberrant control of “target genes”. In this thesis we focus particularly on the role of CEBPA. We studied the predictive and prognostic relevance of mutated CEBPA, and analyzed in a genome wide fashion the mRNA expression, DNA-methylation and the protein-DNA-binding levels corresponding to (mutated) CEBPA in AML. For the analysis of protein-DNA-binding, we developed a novel statistical methodology. With this statistical methodology we studied the fundamental role of (mutant) C/EBPα binding and the effect on gene expression levels. We also integrated gene expression with DNA-methylation profiles of hundreds of AML patients and revealed the existence of two previously unidentified AML subtypes