Advantages:
- Cancers with an adaptive immune response (T cell and antibody response) to EBV correlate with better outcomes: tested in ovarian cancer, stomach adenocarcinoma, Burkitt lymphoma, and diffused large B cell lymphoma
- Could be developed into a potential predictive biomarker that utilizes antibody and/or T cell receptor CDR3 sequencing to forecast EBV-positive cancer outcomes
- Provides valuable insights that aid clinicians in making informed decisions about treatment plans, enhancing the overall quality of care for cancer patients
Summary:
Epstein-Barr virus (EBV) has been implicated in the development of various cancers, including ovarian cancer and Burkitt lymphoma (BL).
In response to the growing demand for personalized cancer treatment, our researchers developed a novel method that predicts EBV-positive cancer outcomes by assessing the adaptive immune response against EBV, which includes antibody and T-cell receptor complementarity determining region-3 (CDR3) sequences against EBV. RNAseq analysis of BL and ovarian cancer patients revealed a correlation between the presence of anti-EBV CDR3s and improved overall survival probability. This discovery highlights the importance of CDR3 profiling in predicting patient outcomes. Considering these results and the fact that the development of certain lymphomas is associated with an EBV infection, these findings suggest that lymphoma may be misdiagnosed as ovarian cancer. This misdiagnosis could explain the improved survival rates observed in patients with higher complementarity scores, as lymphomas generally have a better prognosis than ovarian cancer.
This research suggests the benefit of testing for EBV immunoglobulins in serum after a patient is diagnosed with ovarian cancer, or for anti-EBV T-cell responses, to ensure proper diagnosis and determine better cancer-type-specific treatments for patients.
The above graph presents an overall survival (OS) KM analysis of case IDs categorized into the upper or lower 50th percentile groups based on TCGAOV IGH CDR3-IEDB*30951 Hydro CSs, using a mining algorithm.
Desired Partnerships:
- Sponsored Research
- Co-Development