Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies
AuthorsSaikumar Lakshmi, Priya
Oduor, Cliff I
Forconi, Catherine S
Gerstein, Rachel M
Otieno, Juliana A
Ong'echa, John M
Luftig, Micah A
Brehm, Michael A
Bailey, Jeffrey A
Moormann, Ann M
UMass Chan AffiliationsDiabetes Center of Excellence
Microbiology and Physiological Systems
Program in Molecular Medicine
Document TypeJournal Article
MetadataShow full item record
AbstractEndemic Burkitt lymphoma (BL) is a childhood cancer in sub-Saharan Africa characterized by Epstein-Barr virus and malaria-associated aberrant B-cell activation and MYC chromosomal translocation. Survival rates hover at 50% after conventional chemotherapies; therefore, clinically relevant models are necessary to test additional therapies. Hence, we established five patient-derived BL tumor cell lines and corresponding NSG-BL avatar mouse models. Transcriptomics confirmed that our BL lines maintained fidelity from patient tumors to NSG-BL tumors. However, we found significant variation in tumor growth and survival among NSG-BL avatars and in Epstein-Barr virus protein expression patterns. We tested rituximab responsiveness and found one NSG-BL model exhibiting direct sensitivity, characterized by apoptotic gene expression counterbalanced by unfolded protein response and mTOR pro-survival pathways. In rituximab-unresponsive tumors, we observed an IFN-α signature confirmed by the expression of IRF7 and ISG15. Our results demonstrate significant inter-patient tumor variation and heterogeneity, and that contemporary patient-derived BL cell lines and NSG-BL avatars are feasible tools to guide new therapeutic strategies and improve outcomes for these children.
SourceSaikumar Lakshmi P, Oduor CI, Forconi CS, M'Bana V, Bly C, Gerstein RM, Otieno JA, Ong'echa JM, Münz C, Luftig MA, Brehm MA, Bailey JA, Moormann AM. Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies. Life Sci Alliance. 2023 Mar 6;6(5):e202101355. doi: 10.26508/lsa.202101355. PMID: 36878637; PMCID: PMC9990458.
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/51794
Rights© 2023 Saikumar Lakshmi et al. This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).; Attribution 4.0 International
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Except where otherwise noted, this item's license is described as © 2023 Saikumar Lakshmi et al. This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).