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Identification and ranking of recurrent neo-epitopes in cancer
Eric Blanc1,2, Manuel Holtgrewe1,2, Arunraj Dhamodaran3
1Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.
Background:
Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system's capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority.
Methods:
We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions.
Results:
We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year.
Conclusion:
While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.
Insights
Identifying cancer neo-epitopes is crucial for immunotherapy. This study presents a computational method to prioritize recurrent neo-epitopes, aiding in the development of precision cancer treatments.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Cancer immune escape is a major challenge.
- Somatic mutations generate neo-epitopes, key targets for cancer immunotherapy.
- Rapid identification of neo-epitope candidates is a priority.
Purpose of the Study:
- To develop a computational method for selecting and prioritizing neo-epitope candidates.
- To identify neo-epitopes with high potential for generation and patient recurrence.
- To facilitate the development of novel cancer immunotherapies.
Main Methods:
- Screening The Cancer Genome Atlas (TCGA) datasets for recurrent somatic amino acid exchanges.
- Applying MHC class I binding predictions for neo-epitope candidate selection.
- In silico prioritization of neo-epitopes likely to occur in multiple patients.
Main Results:
- Identified 769 candidate neo-epitopes with high potential for neo-antigen generation.
- These neo-epitopes are predicted to occur in 77,629 patients annually.
- The method provides an objective order for experimental validation of recurrent neo-epitopes.
Conclusions:
- Recurrent neo-epitopes can be computationally identified and prioritized.
- These findings support the development of precision treatment options.
- Recurrent neo-epitopes may supplement existing personalized T-cell therapies.
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