Applying Computational Pipelines to Transcriptomic Data from Virally Associated Lymphomas in Malawi

Epstein-Barr virus (EBV) and human immunodeficiency virus (HIV) affect tumorigenesis. It is known that EBV affects the development of B cells and HIV infects and kills CD4+ T cells. Therefore, lymphomas, such as classic Hodgkin lymphoma (cHL) and diffuse large B-cell lymphoma (DLBCL), are categorized based on their viral associations. Viral associations greatly impact cHL since it is characterized by a low tumor cell content and a high background immune cell content. Yet, it is unknown how various combinations of EBV and HIV alter these immune cell proportions within the tumor microenvironment (TME) of cHL and which virus drives these changes. DLBCL is a molecularly heterogeneous disease, partly due to viral associations. However, there is inconsistency in the percentage of tumor cells that need to be stained by Epstein-Barr encoding region in situ hybridization (EBER-ISH) to classify the tumor as EBV-positive (EBV+). This study begins to fill these gaps through the use of computational pipelines and analysis on bulk tumor RNA sequencing data. CIBERSORTx performed cell type analysis on the TME, while VirDetect and VIRTUS each categorized DLBCL based on EBV presence. CIBERSORTx highlighted that more cell type differences of the TME are based on EBV status than HIV status. Furthermore, there were differences among EBV classification between EBER-ISH, VirDetect and VIRTUS. Numerous samples were considered EBV negative (EBV-) via EBER-ISH but had many counts of EBV genes via VIRTUS highlighting the discordance in EBV detection and classification. Thus, computational pipelines are beneficial to determine and understand the impact of viruses on lymphoma which may aid in therapeutic strategy selection.