Immunogenic Cancer Neopeptide Prediction
Tech ID: 18-072
Inventors: Dr. Brian Baker, Dr. Tim Reily
Date Added: Nov. 16, 2020
A novel method to predict immunogenic cancer neopeptides using structural and physical modeling
Cancer is the leading cause of death worldwide, accounting for an estimated 9.5 million deaths in 2019. Fortunately, random mutations in tumor cells create the neoepitopes that make cancer immunogenic and susceptible to T cells, which makes it possible to create vaccination to cancer. However, only a small percentage of neoepitopes (among the hundreds or thousands in any given tumor) appear to protect the host through an antitumor T cell response (i.e, are immunogenic). While it is relatively easy for clinicians to determine the epitopes of cancer cells, their results do not give information about T-cell binding, that is, which antigens are most easily recognized by the T-cells.
Researchers at University of Notre Dame have developed a solution that can identify on and off-target T-cell antigens from structural information. The technology is a software-based algorithm that determines which epitopes (tumor antigens) are the most feasible targets for T cells, and therefore, for immune targeting (i.e. most immunogenic and antigenic epitopes). This technology will allow cancer-vaccine producers to determine which epitopes they should be targeting. Specifically, the platform is useful for developing personalized cancer-vaccines and the end-user is therefore personalized cancer-vaccine producers.
Current technologies only use sequences to evaluate the immunogenic potential of epitopes, rather than including the higher-level structural components detailed in the energy score. The most important feature of this technology is the energy-based score determined from the neoepitope/MHC complex. Current tools describe an epitope to neural networks whereas this tool shows the epitope.
- Enhances the development of TCR-based therapies, diagnostics, and sensors
- Offers increased safety potential to the patient
- Ability to screen for all possible cross reactants
- Much faster processing times
- Epitope Prediction as a service
Technology Readiness Status
- TRL 4 - Lab Validation
- PCT Filed (Dec. 2019)