Dissecting tumor microenvironments of blood cancers using single-cell expression data

Cancer is a major global issue claiming the lives of millions every year. It is an extremely complex and diverse disease spanning numerous cancer types, however, they are all defined by one specific hallmark: abnormal cell growth. Historically, researchers have focused on the cancer itself, but it is now apparent that several other factors contribute to the disease. Cancer cells and immune cells constantly exert mutual selective pressure on each other during cancer development. Immune cells posses the ability to kill cancer cells, nevertheless cancer cells can escape this by creating immunosuppressive environments. The introduction of immunotherapies has changed the cancer treatment paradigm. Instead of using highly toxic treatment options such as chemotherapy and radiation, immunotherapies aim at unleashing the potential of immune cells to eliminate the cancer cells. Due to varying response rates, there is a strong need for patient stratification in order to determine who will benefit from treatment.With the advances of single-cell technologies such single-cell RNA (scRNA) sequencing, dissection of heterogeneous cell systems such as the tumor microenvironment is now widely adopted. This thesis is comprised of four studies with a common goal of elucidating various aspects of the tumor microenvironment primarily employing scRNA sequencing. The first study presented highlights transcriptional differences between healthy immune cells and cancer patients with either chronic lymphocytic leukemia (CLL) or the precursor stage thereof.Secondly, a study seeking to discover the determinants of response in Richter’s syndrome (RS) patients following PD1 checkpoint blockade is presented. This showed that response associated with a CD8+ T cell population marked by the expression of the transcription factor ZNF683 that appeared to regulate key pathways of T cell activation and differentiation.The third included in this thesis observes no transcriptional changes of the tumor cells of CLL patients going from precursor stage to disease. This is in concordance with the DNA methylation patterns presented in this study, that is detected already at the precursor stage and is persistent through the disease course. Finally, scRNA sequencing was utilized to detect clonal evolution of CLL cells transforming into an aggressive secondary lymphoma, RS. The findings of this study also includes molecular events driving this transformation

SCCmecFinder, a Web-Based Tool for Typing of Staphylococcal Cassette Chromosome mec in Staphylococcus aureus Using Whole-Genome Sequence Data

Typing of methicillin-resistant Staphylococcus aureus (MRSA) is important in infection control and surveillance. The current nomenclature of MRSA includes the genetic background of the S. aureus strain determined by multilocus sequence typing (MLST) or equivalent methods like spa typing and typing of the mobile genetic element staphylococcal cassette chromosome mec (SCCmec), which carries the mecA or mecC gene. Whereas MLST and spa typing are relatively simple, typing of SCCmec is less trivial because of its heterogeneity. Whole-genome sequencing (WGS) provides the essential data for typing of the genetic background and SCCmec, but so far, no bioinformatic tools for SCCmec typing have been available. Here, we report the development and evaluation of SCCmecFinder for characterization of the SCCmec element from S. aureus WGS data. SCCmecFinder is able to identify all SCCmec element types, designated I to XIII, with subtyping of SCCmec types IV (2B) and V (5C2). SCCmec elements are characterized by two different gene prediction approaches to achieve correct annotation, a Basic Local Alignment Search Tool (BLAST)-based approach and a k-mer-based approach. Evaluation of SCCmecFinder by using a diverse collection of clinical isolates (n = 93) showed a high typeability level of 96.7%, which increased to 98.9% upon modification of the default settings. In conclusion, SCCmecFinder can be an alternative to more laborious SCCmec typing methods and is freely available at https://cge.cbs.dtu.dk/services/SCCmecFinder. IMPORTANCE SCCmec in MRSA is acknowledged to be of importance not only because it contains the mecA or mecC gene but also for staphylococcal adaptation to different environments, e.g., in hospitals, the community, and livestock. Typing of SCCmec by PCR techniques has, because of its heterogeneity, been challenging, and whole-genome sequencing has only partially solved this since no good bioinformatic tools have been available. In this article, we describe the development of a new bioinformatic tool, SCCmecFinder, that includes most of the needs for infection control professionals and researchers regarding the interpretation of SCCmec elements. The software detects all of the SCCmec elements accepted by the International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements, and users will be prompted if diverging and potential new elements are uploaded. Furthermore, SCCmecFinder will be curated and updated as new elements are found and it is easy to use and freely accessible

Immunity and mental illness: findings from a Danish population-based immunogenetic study of seven psychiatric and neurodevelopmental disorders

Human leukocyte antigen (HLA) genes encode proteins with important roles in the regulation of the immune system. Many studies have also implicated HLA genes in psychiatric and neurodevelopmental disorders. However, these studies usually focus on one disorder and/or on one HLA candidate gene, often with small samples. Here, we access a large dataset of 65,534 genotyped individuals consisting of controls (N = 19,645) and cases having one or more of autism spectrum disorder (N = 12,331), attention deficit hyperactivity disorder (N = 14,397), schizophrenia (N = 2401), bipolar disorder (N = 1391), depression (N = 18,511), anorexia (N = 2551) or intellectual disability (N = 3175). We imputed participants' HLA alleles to investigate the involvement of HLA genes in these disorders using regression models. We found a pronounced protective effect of DPB1*1501 on susceptibility to autism (p = 0.0094, OR = 0.72) and intellectual disability (p = 0.00099, OR = 0.41), with an increased protective effect on a comorbid diagnosis of both disorders (p = 0.003, OR = 0.29). We also identified a risk allele for intellectual disability, B*5701 (p = 0.00016, OR = 1.33). Associations with both alleles survived FDR correction and a permutation procedure. We did not find significant evidence for replication of previously-reported associations for autism or schizophrenia. Our results support an implication of HLA genes in autism and intellectual disability, which requires replication by other studies. Our study also highlights the importance of large sample sizes in HLA association studies