Extracellular vesicles in DLBCL provide abundant clues to aberrant transcriptional programming and genomic alterations

Sarah C. Rutherford, Angela A. Fachel, Sheng Li, Seema Sawh, Ashlesha Muley, Jennifer Ishii, Ashish Saxena, Pilar M. Dominguez, Eloisi Caldas Lopes, Xabier Agirre, Nyasha Chambwe, Fabian Correa, Yanwen Jiang, Kristy L. Richards, Doron Betel and Rita Shaknovich

Key points

  • Extracellular vesicles derived from DLBCL cells can be traced from and provide insight into cell of origin.

  • Mutated RNAs may be preferentially packaged into extracellular vesicles, and this could enable disease monitoring through liquid biopsy.


The biological role of extracellular vesicles (EVs) in diffuse large B-cell lymphoma (DLBCL) initiation and progression remains largely unknown. We characterized EVs secreted by five DLBCL cell lines, a primary DLBCL tumor and a normal control B cell sample, optimized their purification and analyzed their content. We found that DLBCLs secreted large quantities of CD63, Alix, TSG101 and CD81 EVs, which can be extracted using an ultracentrifugation-based method and traced by their cell of origin surface markers. We also showed that tumor-derived EVs can be exchanged between lymphoma cells, normal tonsillar and HK stromal cells. We then examined the content of EVs, focusing on isolation of high quality total RNA. We sequenced the total RNA, and analyzed the nature of RNA species including coding and non-coding RNAs. We compared whole cell and EV-derived RNA composition in benign and malignant B cells and discovered that transcripts from EVs were involved in many critical cellular functions. Finally, we performed mutational analysis and found that mutations detected in EVs exquisitely represented mutations in the cell of origin. These results enhance our understanding and enable future studies of the role that EVs may play in the pathogenesis of DLBCL, particularly with regards to the exchange of genomic information. Current findings open a new strategy for liquid biopsy approaches in disease monitoring.

  • Submitted December 20, 2017.
  • Accepted June 21, 2018.