Epstein-Barr virus as the cause of a human cancer

Nature ◽  
1978 ◽  
Vol 274 (5673) ◽  
pp. 740-740 ◽  
Author(s):  
M. A. Epstein
2018 ◽  
Vol 19 (9) ◽  
pp. 2810 ◽  
Author(s):  
Li Sun ◽  
David Meckes

Epstein Barr-virus (EBV) was the first virus identified to be associated with human cancer in 1964 and is found ubiquitously throughout the world’s population. It is now established that EBV contributes to the development and progression of multiple human cancers of both lymphoid and epithelial cell origins. EBV encoded miRNAs play an important role in tumor proliferation, angiogenesis, immune escape, tissue invasion, and metastasis. Recently, EBV miRNAs have been found to be released from infected cancer cells in extracellular vesicles (EVs) and regulate gene expression in neighboring uninfected cells present in the tumor microenvironment and possibly at distal sites. As EVs are abundant in many biological fluids, the viral and cellular miRNAs present within EBV-modified EVs may serve as noninvasion markers for cancer diagnosis and prognosis. In this review, we discuss recent advances in EV isolation and miRNA detection, and provide a complete workflow for EV purification from plasma and deep-sequencing for biomarker discovery.


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 4644-4644
Author(s):  
D. Fu ◽  
J. Chong ◽  
C. Foss ◽  
J. Fox ◽  
S. Wang ◽  
...  

4644 Background: Epstein-Barr virus (EBV) has been identified in a wide variety of malignancies, including gastric carcinomas. The virus encodes kinases that phosphorylate nucleoside analogs such as 2’-deoxy-2’-fluoro-5-iodo-1-beta-D- arabinofuranosyluracil (FIAU). We hypothesized that it might be possible to use the viral enzyme to specifically concentrate [125I]FIAU or [131I] FIAU in tumor cells harboring virus and thus deliver imaging and therapeutic radiation. Bortezomib is a potent stimulator of viral kinase expression in EBV tumor cell lines. Methods: We imaged lytic induction in vivo and evaluated the effect of [131I] FIAU on human cancer xenografts in SCID mice. These include a tumor line engineered to constitutively express the EBV thymide kinase (EBVTK), and a control engineered with a sham vector (SHAM), as well one EBV-associated human gastric tumor (KT tumor). Mice were treated with buffer, bortezomib (2μg/g), or radiolabeled FIAU or radiolabeled FIAU and bortezomib in combination. For imaging, mice, [125I]-FIAU and SPECT/CT were used. For therapy, 131I-FIAU was used and tumor dimensions were monitored with calipers. Results: SPECT/CT imaging with [125I]-FIAU of tumor-bearing SCID mice showed selective concentration of radiotracer in tumor tissue in EBVTK (3/3) and in EBV-associated KT tumors (3/3) when animals were pretreated with bortezomib. Treatment with buffer had no effect on 3 EBVTK tumors and 3 SHAM tumors all of which increased in volume. Treatment with 1.6 mCi of [131I]-FIAU alone led to tumor response in 3/3 mice with EBVTK tumors and 0/3 mice with SHAM tumors. Treatment with [131I]-FIAU alone had no effect on EBV KT tumor xenografts (0/3) and all tumors increased in volume. Treatment with bortezomib induced modest responses in all KT tumors. However, treatment with bortezomib and [131I]-FIAU led to marked tumor regression (>80%) in EBV-associated KT tumors (3/3). Conclusions: Treatment with bortezomib leads to selective concentration of radiolabeled FIAU in the EBV-associated tumor xenografts. In combination with [131I]-FIAU it leads to tumor regression. No significant financial relationships to disclose.


Intervirology ◽  
1995 ◽  
Vol 38 (3-4) ◽  
pp. 214-220 ◽  
Author(s):  
Kenzo Takada ◽  
Norio Shimizu ◽  
Akiko Tanabe-Tochikura ◽  
Yasuyuki Kuroiwa

2017 ◽  
Vol 8 ◽  
pp. 1178122X1773177 ◽  
Author(s):  
Daniel Esau

In 1964, Epstein, Barr, and Achong published a report outlining their discovery of viral particles in lymphoblasts isolated from a patient with Burkitt lymphoma. The Epstein-Barr virus (EBV) was the first human cancer virus to be described, and its discovery paved the way for further investigations into the oncogenic potential of viruses. In the decades following the discovery of EBV, multinational research efforts led to the discovery of further viral causes of various human cancers. Lymphomas are perhaps the cancer type that is most closely associated with oncogenic viruses: infection with EBV, human T-lymphotropic virus 1 (HTLV-1), human immunodeficiency virus (HIV), Kaposi sarcoma-associated herpesvirus/human herpesvirus 8, and hepatitis C virus have all been associated with lymphomagenesis. Lymphomas have also played an important role in the history of oncoviruses, as both the first human oncovirus (EBV) and the first human retrovirus (HTLV-1) were discovered through isolates taken from patients with unique lymphoma syndromes. The history of the discovery of these 2 key oncoviruses is presented here, and their impact on further medical research, using the specific example of HIV research, is briefly discussed.


2002 ◽  
Vol 87 (1) ◽  
pp. 127-128
Author(s):  
Lawrence Young

2019 ◽  
Author(s):  
Jakob Nikolas Kather ◽  
Jefree Schulte ◽  
Heike I. Grabsch ◽  
Chiara Loeffler ◽  
Hannah Muti ◽  
...  

AbstractOncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.


2002 ◽  
Vol 94 (24) ◽  
pp. 1832-1836 ◽  
Author(s):  
M. Wong ◽  
J. S. Pagano ◽  
J. T. Schiller ◽  
S. S. Tevethia ◽  
N. Raab-Traub ◽  
...  

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