Endemic Burkitt lymphoma (eBL) is the most common pediatric cancer in malaria-endemic equatorial Africa and nearly always contains Epstein-Barr virus (EBV), unlike sporadic Burkitt Lymphoma (sBL) that occurs with a lower incidence in developed countries. Despite this increased burden the study of eBL has lagged. Additionally, while EBV was isolated from an African Burkitt lymphoma tumor 50 years ago, however, the impact of viral variation in oncogenesis is just beginning to be fully explored. In my thesis research, I focused on investigating molecular genetics of the endemic form of this lymphoma with a particular emphasis on the role of the virus and its variation in pathogenesis using novel sequencing and bioinformatic strategies. First, we sought to understand pathogenesis by investigating transcriptomes using RNA sequencing (RNAseq) from 30 primary eBL tumors and compared to sBL tumors. BL tumor samples were prospectively obtained from 2009 until 2012 in Kenya. Within eBL tumors, minimal expression differences were found based on anatomical presentation site, in-hospital survival rates, and EBV genome type; suggesting that eBL tumors are homogeneous without marked subtypes. The outstanding difference detected using surrogate variable analysis was the significantly decreased expression of key genes in the immunoproteasome complex in eBL tumors carrying type 2 EBV compared to type 1 EBV. Secondly, in comparison to previously published pediatric sBL specimens, the majority of the expression and pathway differences were related to the PTEN/PI3K/mTOR signaling pathway and was correlated most strongly with EBV status rather than the geographic designation. Moreover, the common mutations were observed significantly less frequently in eBL tumors harboring EBV type 1, with mutation frequencies similar between tumors with EBV type 2 and without EBV. In addition to the previously reported genes, we identified a set of new genes mutated in BL. Overall, these suggested that EBV, particularly EBV type 1, supports BL oncogenesis alleviating the need for particular driver mutations in the human genome. Second, we sought to comprehensively define sequence variations of EBV across the viral genome in eBL tumor cells and normal infections, and correlate variations with clinical phenotypes and disease risk. We investigated the whole genome sequence of EBV from primary tumors (N=41) and plasma from eBL patients (N=21) as well as EBV in the blood of healthy children (N=29) within the same malaria endemic region. We conducted a genome wide association analysis study with viral genomes of healthy kids and BL kids. Furthermore, we found that the frequencies of EBV types among healthy kids were at equal levels while they were skewed in favor of type 1 (70%) among eBL kids. To pinpoint the fundamental divergence between viral genome subtypes, type 1 and type 2, we constructed phylogenetic trees comparing to all public EBV genomes. The pattern of variation defined the substructures correlated with the subtypes. This investigation not only deciphers the puzzling pathogenic differences between subtypes but also helps to understand how these two EBV types persist in the population at the same time. Overall, this research provides insight into the molecular underpinning of eBL and the role of EBV. It further provides the groundwork and means to unravel the complexity of EBV population structure and provide insight into the viral variation that may influence oncogenesis and outcomes in eBL and other EBV-associated diseases. In addition, genomic and mutational analyses of Burkitt lymphoma tumors identify key differences based on viral content and clinical outcomes suggesting new avenues for the development of prognostic molecular biomarkers and therapeutic interventions.

Viruses are known to be associated with 20% of human cancers. Epstein Barr virus (EBV) in particular is the first virus associated with human cancers. Here, we computationally detect EBV and explore the effects of this virus across cancers by taking advantage of the fact that EBV microRNAs (miRNAs) and Epstein Barr virus small RNAs (EBERs) are expressed at all viral latencies. We identify and characterize two sub-populations of EBV positive tumors: those with high levels of EBV miRNA and EBERS expression and those with medium levels of expression. Based on principal component analysis (PCA) and hierarchical clustering of viral miRNAs across all samples we observe a pattern of expression for these EBV miRNAs which is correlated with both the tumor cell type (B cell versus epithelial cell) and with the overall levels of expression of these miRNAs. We further investigated the effect of the levels of EBV miRNAs with the overall survival of patients across cancers. Through Kaplan Meier survival analysis we observe a significant correlation with levels of EBV miRNAs and lower survival in adult AML patients. We also designed a machine learning model for risk assessment of EBV in association with adult AML and other clinical factors. Our next aim was to identify targets of EBV miRNAs, hence, we used a combination of previously known methodologies for miRNA target detection in addition to a multivariable regression approach to identify targets of these viral miRNAs in stomach cancer. Finally, we investigate the variations across EBV subtype specific EBNA3C gene which interacts with the host immune system. Preliminary data suggests potential regional variations plus higher pathogenicity of subtype 1 in comparison to subtype 2 EBV. Overall, these studies further our understanding of how EBV manipulates the tumor microenvironment across cancer subtypes.