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Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type–Specific Differences
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Abstract
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. Given these differences and the variable clinical presentation and outcomes, we sought to further understand pathogenesis by investigating transcriptomes using RNA sequencing (RNAseq) from multiple primary eBL tumors compared with sBL tumors. 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 (PSMB9/β1i, PSMB10/β2i, PSMB8/β5i, and PSME2/PA28β) in eBL tumors carrying type 2 EBV compared with type 1 EBV. Second, in comparison with previously published pediatric sBL specimens, the majority of the expression and pathway differences was related to the PTEN/PI3K/mTOR signaling pathway and was correlated most strongly with EBV status rather than geographic designation. Third, 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, a set of new genes mutated in BL, including TFAP4, MSH6, PRRC2C, BCL7A, FOXO1, PLCG2, PRKDC, RAD50, and RPRD2, were identified. Overall, these data establish that EBV, particularly EBV type 1, supports BL oncogenesis, alleviating the need for certain driver mutations in the human genome. Implications: 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. Mol Cancer Res; 15(5); 563–76. ©2017 AACR.
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“In addition, an RNA-seq study of patient tumors of EBL, a common childhood cancer from equatorial Africa where malaria is endemic, showed that the vast majority of cases which harbor EBV contain PLCγ2 mutations, along with mutations in other genes including MYC (142).”
From The role of PLCγ2 in immunological disorders, cancer, and neurodegeneration (2021) · cites this paper as [142]
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“Interestingly, while MYC translocation and hyperexpression is a hallmark of EBL, it in itself is not sufficient to induce lymphogenesis (143), which may at least in part explain why mutations in PLCγ2 are relevant to the disease initiation (142).”
From The role of PLCγ2 in immunological disorders, cancer, and neurodegeneration (2021) · cites this paper as [142]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Kadoch, C., Hargreaves, D. C., Hodges, H. C., Elias, L., Ho, L., Ranish, J. A., & Crabtree, G. R. (2013) · Nature Genetics · doi:10.1038/ng.2628 · Cited by 1,493
Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics
Schmitz, R., Young, R. M., Ceribelli, M., Jhavar, S. G., Xiao, W., Zhang, M., Wright, G. W., Shaffer, A. L., Hodson, D. J., Buras, E. D., et al. (2012) · Nature · doi:10.1038/nature11378 · Cited by 935
Landscape of genomic alterations in cervical carcinomas
Ojesina, A. I., Lichtenstein, L., Freeman, S. S., Pedamallu, C. S., Imaz-Rosshandler, I., Pugh, T. J., Cherniack, A. D., Ambrogio, L., Cibulskis, K., Bertelsen, B. I., et al. (2013) · Nature · doi:10.1038/nature12881 · Cited by 874
Rowe, M., Rowe, D., Gregory, C. D., Young, L. S., Farrell, P. J., Rupani, H., & Rickinson, A. B. (1987) · The EMBO Journal · doi:10.1002/j.1460-2075.1987.tb02568.x · Cited by 711
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Metadata
- Authors
- Kaymaz, Y., Oduor, C. I., Yu, H., Otieno, J. A., Ong'echa, J. M., Moormann, A. M., & Bailey, J. A.
- Affiliations
- University of Massachusetts Chan Medical School (US)
- Maseno University (KE)
- Ministry of Health (KE)
- 2Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
- Corresponding author
- Bailey, Jeffrey A.
- Venue
- Molecular Cancer Research
- Published
- 2017-05-01
- Volume
- 15
- Issue
- 5
- Pages
- 563-576
- ISSN
- 1541-7786 (ISSN-L)
- 1557-3125
- PMC
- PMC5471630
- OpenAlex
- W2580872164
- Cited by
- 120
- References
- 65
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 17 · 2023: 23 · 2022: 16 · 2021: 14 · 2020: 11 · 2019: 9 · 2018: 4 · 2017: 5
- FWCI
- 3.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Molecular Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001453, UL1TR000161)
- National Cancer Institute (R01CA134051, R01CA189806)
- National Institute of Allergy and Infectious Diseases (R01AI099473)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Oncology › Viral-associated cancers and disorders
- Health Sciences › Medicine › Pathology and Forensic Medicine › Lymphoma Diagnosis and Treatment
- Health Sciences › Medicine › Clinical Neurology › CNS Lymphoma Diagnosis and Treatment
- Keywords
- endemic Burkitt lymphoma, sporadic Burkitt lymphoma, Epstein-Barr virus, transcriptome, mutational profiling, immunoproteasome, PSMB9, TFAP4, MSH6, prognostic factors, pediatric lymphoma
- MeSH terms
- Adolescent, Burkitt Lymphoma, Child, Child, Preschool, Herpesvirus 4, Human, Female, Humans, Kenya, Male, Mutation, Genome, Viral, Sequence Analysis, RNA, Endemic Diseases, Epstein-Barr Virus Infections, Gene Expression Profiling, Gene Regulatory Networks, Mutation Rate
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