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A computational workflow for predicting cancer neo-antigens.
Sandeep Kasaragod1, Chinmaya Narayana Kotimoole1, Sumrati Gurtoo1
1Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India.
Bioinformation
|December 15, 2022
Summary
Predicting cancer neo-antigens is crucial for immunotherapy success. This study integrates genomic and proteomic data to improve neo-antigen prediction accuracy, reducing false positives from mutation data alone.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neo-antigens on the cell surface are critical for effective immunotherapies.
- Current methods often predict neo-antigens from cancer genome sequencing, leading to high false positive rates.
- Many coding mutations are not expressed at the protein level, limiting the accuracy of genomic-only approaches.
Purpose of the Study:
- To develop a computational workflow for more accurate neo-antigen prediction.
- To integrate both genomic and proteomic data for enhanced neo-antigen identification.
- To overcome limitations of solely relying on mutation data for neo-antigen discovery.
Main Methods:
- Development of a computational pipeline.
- Integration of cancer genome sequencing data.
- Integration of proteomic data to validate protein expression.
Main Results:
- A novel computational workflow was established.
- The workflow integrates genomic and proteomic datasets.
- This approach aims to identify potential neo-antigens with higher confidence.
Conclusions:
- Integrating genomic and proteomic data offers a more robust strategy for neo-antigen prediction.
- This approach can significantly reduce false positives in neo-antigen discovery.
- Improved neo-antigen prediction holds promise for advancing cancer immunotherapies.
