A computational workflow for predicting cancer neoantigens

Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict canc...

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Bibliographic Details
Published in:Bioinformation 2022-03, Vol.18 (3), p.214
Main Authors: Kasaragod, Sandeep, Kotimoole, Chinmaya Narayana, Gurtoo, Sumrati, Prasad, Thottethodi Subrahmanya Keshava, Gowda, Harsha, Modi, Prashant Kumar
Format: Article
Language:English
Subjects:
Antigens
Cancer
Cell surface
Computer applications
Gene sequencing
Immunotherapy
Mutation
Neoantigens
Peptides
Proteins
Proteomics
Workflow
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Summary:Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict cancer neo-antigens. However, this strategy is associated with significant false positives as many coding mutations may not be expressed at the protein level. Hence, we describe a computational workflow to integrate genomic and proteomic data to predictpotential neo-antigens.
ISSN:0973-8894
0973-2063
DOI:10.6026/97320630018214